RADIONUCLIDE SORPTION TECHNICAL ASSISTANCE 

ACTIVITIES AT THE CENTER FOR NUCLEAR WASTE 

REGULATORY ANALYSES 

Prepared for 

U.S. Nuclear Regulatory Commission 

Contract NRC–02–07–006 

Prepared by 

Paul Bertetti 

Roberto Pabalan 

David Pickett 

David Turner 

Center for Nuclear Waste Regulatory Analyses 

San Antonio, Texas 

September 2011
ii 

ABSTRACT 

The potential release of radionuclides as dissolved constituents in groundwater is a primary 

concern in performance assessment of proposed high-level nuclear waste geologic repositories. 

Sorption onto mineral surfaces is an important mechanism for reducing radionuclide 

concentrations along groundwater flow paths and retarding radionuclide migration. 

Radionuclide sorption is, in turn, influenced by the physical and chemical conditions of the 

groundwater system. Beginning in 1990, Center for Nuclear Waste Regulatory Analyses 

(CNWRA®) staff initiated experimental and modeling programs to evaluate processes that 

influence radionuclide sorption and to identify key geochemical and physical parameters that 

have the greatest effects. As the sorption program evolved, CNWRA staff focused on two main 

sorption mechanisms: (i) ion exchange, which has the greatest effects on alkali and alkaline 

earth radionuclides, such as Cs+

 and Sr2+ and (ii) surface complexation, the process that exerts 

the most control on the sorption of long-lived actinides, such as uranium, neptunium, plutonium, 

americium, and thorium. In addition, fine-scaled analysis (extended x-ray absorption fine 

structure and molecular dynamics simulations) were used to better understand radionuclide 

sorption reactions at the molecular scale. CNWRA staff used the results from the experimental 

program to calibrate detailed ion-exchange and surface complexation process models that were 

used to inform CNWRA understanding of how radionuclide sorption could be abstracted into 

performance assessment models. The purpose of this knowledge management report is to 

capture insights gained during more than 20 years of CNWRA experience in evaluating 

radionuclide sorption processes and incorporating the results of detailed modeling into the 

performance assessment codes U.S. Nuclear Regulatory Commission and CNWRA staffs 

developed. A list of sorption-related reports and publications CNWRA staff produced is included 

as an appendix. 
iii 

CONTENTS 

Section Page 

ABSTRACT ................................................................................................................................... ii 

FIGURES ..................................................................................................................................... vi 

TABLES ....................................................................................................................................... ix 

ACKNOWLEDGMENTS ............................................................................................................... x 

1 INTRODUCTION ................................................................................................................ 1-1 

1.1 Background ............................................................................................................... 1-1 

1.2 History of Radionuclide Sorption Technical Assistance at the CNWRA .................... 1-1 

1.2.1 Experimental Program(s) and Developing Process Models .......................... 1-1 

1.2.1.1 Ion Exchange ................................................................................... 1-1 

1.2.1.2 Surface Complexation ..................................................................... 1-2 

1.3 Process Model Abstraction for Total-system Performance Assessment ................... 1-3 

1.4 References (Section 1) .............................................................................................. 1-4 

2 RADIONUCLIDE SORPTION EXPERIMENTS .................................................................. 2-1 

2.1 Chemical Systems Considered ................................................................................. 2-1 

2.1.1 Alkaline/Alkaline Earths (Na–Ca–K–Sr–Cs) .................................................. 2-2 

2.1.2 Uranium ......................................................................................................... 2-2 

2.1.3 Neptunium ..................................................................................................... 2-3 

2.1.4 Technetium .................................................................................................... 2-3 

2.2 Batch Experiments .................................................................................................... 2-4 

2.2.1 Quartz ............................................................................................................ 2-5 

2.2.2 Alumina .......................................................................................................... 2-6 

2.2.3 Clinoptilolite ................................................................................................... 2-6 

2.2.4 Montmorillonite .............................................................................................. 2-7 

2.2.5 Calcite ............................................................................................................ 2-8 

2.2.6 Iron (Hydr)oxides (Corrosion Products) ......................................................... 2-8 

2.2.7 Mineral Mixtures ............................................................................................ 2-9 

2.2.7.1 Quartz and Clinoptilolite .................................................................. 2-9 

2.2.7.2 Alluvium ........................................................................................... 2-9 

2.3 References .............................................................................................................. 2-10 

2.4 X-Ray Absorption Fine-Structure Spectroscopy Study of Uranium(VI) 

Sorption ................................................................................................................... 2-13 

2.4.1 Experimental ................................................................................................ 2-13 

2.4.2 Results ......................................................................................................... 2-14 

2.4.3 Conclusions ................................................................................................. 2-15 

2.4.4 References (Section 2.3) ............................................................................. 2-15 

2.5 Key Geochemical Parameters Controlling Radionuclide Sorption .......................... 2-16 

2.5.1 Effects of Aqueous Speciation ..................................................................... 2-16 

2.5.2 Physical Properties ...................................................................................... 2-16 

2.5.3 Representing Actinide Sorption Data ........................................................... 2-17 

2.6 References (Section 2.4) ......................................................................................... 2-18 

3 SORPTION MODELS ......................................................................................................... 3-1

3.1 Empirical Models ....................................................................................................... 3-1 

3.1.1 Linear Adsorption (Kd Approach) Isotherm .................................................... 3-1 

3.1.2 Freundlich Adsorption Isotherm ..................................................................... 3-2
iv 

CONTENTS (continued) 

Section Page 

3.1.3 Langmuir Adsorption Isotherm ....................................................................... 3-2 

3.1.4 Parameters for Empirical Equilibrium Adsorption Models .............................. 3-2 

3.1.5 References (Section 3.1) ............................................................................... 3-4 

3.2 Experimental and Modeling Study of Multicomponent Ion-Exchange 

Equilibria on Zeolite Minerals .................................................................................... 3-6 

3.2.1 Introduction .................................................................................................... 3-6 

3.2.2 Ion-Exchange Experiments ............................................................................ 3-7 

3.2.2.1 Binary Ion-Exchange Experiments .................................................. 3-7 

3.2.2.2 Ternary Ion-Exchange Experiments ................................................ 3-8 

3.2.2.3 Quaternary Ion-Exchange Experiment ............................................ 3-9 

3.2.3 Thermodynamic Model .................................................................................. 3-9 

3.2.3.1 Ion-Exchange Isotherm ................................................................... 3-9 

3.2.3.2 Thermodynamic Equilibrium Constant ........................................... 3-10 

3.2.3.3 Activity Coefficients of Aqueous Ions ............................................ 3-11 

3.2.3.4 Activity Coefficients of Zeolite Components .................................. 3-11 

3.2.3.5 Modeling ........................................................................................ 3-13 

3.2.4 Results And Discussion ............................................................................... 3-14 

3.2.4.1 Binary Ion-exchange Results ......................................................... 3-14 

3.2.4.2 Comparison of Margules and Wilson Models ................................ 3-14 

3.2.4.3 Results of Ternary and Quaternary Ion-Exchange 

Experiments ................................................................................... 3-14 

3.2.5 Summary and Conclusions .......................................................................... 3-17 

3.2.6 References (Section 3.2) ............................................................................. 3-17 

3.3 General Features of a Surface Complexation Modeling Approach ......................... 3-22 

3.3.1 Introduction to Surface Complexation Modeling .......................................... 3-22 

3.3.2 Model-Specific Features .............................................................................. 3-23 

3.3.2.1 Diffuse Layer Model ....................................................................... 3-24 

3.3.2.2 Constant Capacitance Model ........................................................ 3-24 

3.3.2.3 Triple-Layer Model ......................................................................... 3-24 

3.3.2.4 Nonelectrostatic Surface Complexation Model .............................. 3-25 

3.3.2.5 Generalized Composite and Component Additivity Modeling 

Approaches ................................................................................... 3-25 

3.3.3 References (Section 3.3) ............................................................................. 3-26 

3.4 Molecular Dynamics Simulation of Uranium(VI) Sorption ........................................ 3-28

3.4.1 Computational Methods ............................................................................... 3-29

3.4.2 Results and Discussion ............................................................................... 3-30

3.4.3 Conclusions ................................................................................................. 3-32

3.4.4 References (Section 3.4) ............................................................................. 3-33

4 IMPLEMENTING SORPTION MODELING APPROACHES IN PERFORMANCE 

ASSESSMENT ................................................................................................................... 4-1

4.1 Groundwater Chemistry Considerations .................................................................... 4-1

4.1.1 References .................................................................................................... 4-2

4.2 Empirical Models—Deterministic ............................................................................... 4-2

4.2.1 Evaluating Kd Values for Deterministic Performance Assessments—

An Example ................................................................................................... 4-2

4.2.2 References .................................................................................................... 4-4
v 

CONTENTS (continued) 

Section Page 

4.3 Using Site-Specific Groundwater Chemistry To Develop Probability Density 

Function Statistics ..................................................................................................... 4-4

4.3.1 Steps in Applying the Model .......................................................................... 4-5

4.3.2 Correlations Among Radionuclides ............................................................... 4-6

4.3.3 Caveats to the Groundwater Chemistry Modeling Approach ......................... 4-6

4.3.4 References (Section 4.3) ............................................................................... 4-7

4.4 Thermodynamic Model—Response Surface ............................................................. 4-8

4.4.1 Description of a Response Surface Approach ............................................... 4-8

4.4.2 References (Section 4.4) ............................................................................... 4-8

4.5 In-Package Radionuclide Transport in Total System 

Performance Assessment ......................................................................................... 4-9

5 SUMMARY AND CONCLUSIONS ...................................................................................... 5-1

5.1 Summary ................................................................................................................... 5-1

5.2 Key Uncertainties and Future Work ........................................................................... 5-1

5.2.1 Development of Sorption Coefficient Databases ........................................... 5-1

5.2.2 Steel Corrosion Product Sorption .................................................................. 5-1

5.2.3 Radionuclide Systems ................................................................................... 5-2

APPENDIX: LIST OF CENTER FOR NUCLEAR WASTE REGULATORY ANALYSES 

(CNWRA®) SORPTION-RELATED PRODUCTS 
vi 

FIGURES 

Figure Page 

2.1-1 Results of Experiments Investigating Tc-99 Sorption on Calcite 

 (Triangles) and Clinoptilolite (Circles). .............................................................. 2-19 

2.2-1 Plot of the Percent U-233 Sorbed Onto Polycarbonate Experimental 

 Containers Before (Blue) and After (Yellow) Addition of Quartz Substrate ...... 2-19 

2.2-2 Plot of U-233 Sorption on Quartz in Absence of CO2 (g) and Varying pH, Solid 

 Mass to Solution Volume Ratios (M/V), and U-233 Concentrations ................. 2-20 

2.2-3 Plot of Np-237 Sorption on Quartz Under Varying pH, PCO2, Solid 

 Mass to Solution Volume Ratios (M/V), and Np-237 Concentrations ............... 2-20 

2.2-4 Plot of U-233 Sorption and α-Alumina, Clinoptilolite, Montmorillonite, 

 and Quartz Under Atmospheric PCO2 Conditions and Normalized to 

 Effective Surface Area (Ka,eff) of Each Mineral ................................................. 2-21 

2.2-5 Plot of Np-237 Sorption on α-Alumina, Clinoptilolite, Montmorillonite, 

 and Quartz in the Absence of CO2(g) and Normalized to Effective Surface 

 Area (Ka,eff) of Each Mineral .............................................................................. 2-21 

2.2-6 Plot of Np-237 Sorption on Clinoptilolite Under Varying PCO2 Conditions ........ 2-22 

2.2-7 Plot of Np-237 Sorption on Montmorillonite Under Varying PCO2 Conditions. 

 A Diffuse Layer Model Was Fit to the No-CO2 Data and the Parameters 

 Used to Predict Sorption at Atmospheric PCO2 ................................................. 2-22

2.2-8 Plot of Np-237 Sorption on Calcite Under Atmospheric PCO2 and Varying 

 Solid Mass to Solution Volume Ratios (M/V) .................................................... 2-23 

2.2-9 Plot of Measured Np-237 Kd Values for the Carbon Steel Corrosion 

 Products, Calcite-Bearing Alluvium, and the Range of Values ......................... 2-23 

2.2-10 Plot of the Sorption of U-233 as a Function of Effective Surface Area for 

 the Quartz-Clinoptilolite Mixed Mineral Experiments. Error Bars Show 

 Calculated Uncertainty for Selected Samples. ................................................. 2-24 

2.2-11 Plot of Np-237 Sorption on Alluvium Sample Composites (CAL) 

 Collected From Fortymile Wash, Nevada. Experiments Were Conducted 

 Atmospheric PCO2 Conditions. .......................................................................... 2-24 

2.3-1 Comparison of (a) Uranyl Sorption of Na-Montmorillonite, Na-Clinoptilolite, 

 and Quartz and (b) Uranyl Aqueous Speciation ............................................... 2-25 

2.3-2 (a) Near-Edge Regions of Uranium L2-Edge Absorption Spectra and 

 Fourier Transform Magnitudes (k3

-Weighting; Uncorrected for Phase Shifts) 

 for Uranyl Sorbed on (b) Montmorillonite and (c) Clinoptilolite at Different 

 pH Values ......................................................................................................... 2-26 

3.2-1 Experimental Data for Ion Exchange Between Clinoptilolite and Aqueous 

Solutions of (a) Na+

 + K+, (b) Cs+ + K+, (c) Cs+ + Na+, (d) Sr2+ + K+, 

 (e) Ca2+ + K+

, and (f) Sr2+ + Ca2+ ...................................................................... 3-34 

3.2-2 Comparison of Experimental Data on Ion Exchange Involving Clinoptilolite 

 and Aqueous Solutions of Li+

/Na+, K+/Na+, Cs+/Na+, and Cs+K+ With 

 Isotherms (Solid Curve) Calculated Using the Wilson Model ........................... 3-35 

3.2-3 Comparison of Experimental Data on Ion Exchange Involving Clinoptilolite 

 and Aqueous Solutions of Cs+

/Rb+, NH4

+

/Na+, NH4

+

/K+, and NH4

+

/Ca2+ 

 With Isotherms (Solid Curve) Calculated Using the Wilson Model ................... 3-36 
vii 

FIGURES (continued) 

Figure Page 

3.2-4 Comparison of Experimental Data on Ion Exchange Involving Clinoptilolite 

 and Aqueous Solutions of 0.005 N Ca2+/Na+

, 0.05 N Ca2+/Na+, Cs+/Na+, 

 and 0.05 N Ca2+/K+

 With Isotherms (Solid Curve) Calculated Using the 

Wilson Model .................................................................................................... 3-37 

3.2-5 Comparison of Experimental Data on Ion Exchange Involving Clinoptilolite 

 and Aqueous Solutions of 0.005 N Sr2+/Na+

, 0.05 N Sr2+/Na+,0.5 N Sr2+/Na+, 

 1.0 N Sr2+/Ca2+, and 0.05 N Sr2+/K+

 With Isotherms (Solid Curve) Calculated 

Using the Wilson Model .................................................................................... 3-38 

3.2-6 (a) Initial and Equilibrium Solution Compositions in the 0.005 N Na+

/K+/Ca2+

 System. (b) Solution and Zeolite Compositions at Equilibrium in the 

 0.005 N Na+

/K+/Ca2+ System. ........................................................................... 3-39 

3.2-7 (a) Initial and Equilibrium Solution Compositions in the 0.05 N Na+/K+/Ca2+

 System. (b) Solution and Zeolite Compositions at Equilibrium in the 

 0.05 N Na+

/K+/Ca2+ System. ............................................................................. 3-40 

3.2-8 (a) Initial and Equilibrium Solution Compositions in the 0.005 N Na+

/K+/Sr2+

 System. (b) Equilibrium Solution Compositions (Red Circles) in the 0.005 N 

 Na+

/K+/Sr2+ System. .......................................................................................... 3-41 

3.2-9 (a) Initial and Equilibrium Solution Compositions in the 0.05 N Na+

/K+/Sr2+

 System. (b) Solution and Zeolite Compositions at Equilibrium in 

 the 0.005 N Na+

/K+/Sr2+ System. ...................................................................... 3-42 

3.2-10 (a) Initial and Equilibrium Solution Compositions in the 0.005 N Na+

/K+/Cs+

 System. (b) Solution and Zeolite Compositions at Equilibrium in 

 the 0.005 N Na+

/K+/Cs+ System. ...................................................................... 3-43 

3.2-11 (a) Initial and Equilibrium Solution Compositions in the 0.05 N Na+

/K+/Cs+

 System. (b) Solution and Zeolite Compositions at Equilibrium in 

 the 0.05 N Na+

/K+/Cs+ System. ........................................................................ 3-44 

3.2-12 Solution and Zeolite Compositions at Equilibrium in the Quaternary 

 Na+

/K+/Ca2+/Sr2+ System at 0.05 N. Tie-Lines Connect Corresponding 

 Zeolite and Aqueous Samples ......................................................................... 3-45 

3.3-1 Schematic Diagram of the Mineral-Water Interface as Represented in 

 Different Surface Complexation Modeling Approaches (a) Diffuse-Layer 

 Model, (b) Constant Capacitance Model, and (c) Triple Layer Model .............. 3-46 

3.4-1 Plots of the Uranium–Oxygen (Solid Line) and Uranium–Hydrogen 

 (Dashed Line) Radial Distribution Functions From a Simulation of UO2

2+ in a 

 Box of 200 Water Molecules............................................................................. 3-47 

3.4-2 Plots of the Uranium-Oxygen Radial Distribution Functions From 

 Simulations in (a) Bulk Solution, (b) Singly Protonated Quartz (010) 

 Surface, and (c) Partially Deprotonated Quartz (010) Surface ......................... 3-48 

3.4-3 Equilibrium Snapshot (x, z Plane) of the Supercell Containing 300 Water 

 Molecules and the Singly Protonated Quartz (010) Surface ............................ 3-49 

3.4-4 Results From the Simulation of UO2

2+ and 300 Water Molecules Near the 

 Singly Protonated Quartz (010) Surface........................................................... 3-50 

3.4-5 Equilibrium Snapshot (x, z Plane) of the Inner-Sphere Surface Complexes 

 (a) [UO2(H2O)(OH)2{O}2] and (b) [UO2(H2O)2(CO3){O}2], Both Shown as 

Large Spheres .................................................................................................. 3-51 
viii 

FIGURES (continued) 

Figure Page 

3.4-6 Equilibrium Trajectories (z-Direction) of Uranium Atoms in Inner-Sphere, 

 Outer-Sphere, and Diffuse Uranyl Species. From Bottom to Top, the Three 

Uranyl Complexes Are [UO2(H2O)(OH)2{O}2] [UO2(H2O)5]

2+ and 

 [UO2(H2O)3(OH)2] ............................................................................................. 3-52 

4.1-1 Plot of Water Chemistry Data From Well (107 Samples) Located Within the 

 Site-Scale Model Area in the Yucca Mountain Vicinity ..................................... 4-10 

4.1-2 Map of Calculated Neptunium KA,eff (See Section 2.4.3) Sorption Values for 

Selected Wells Plotted Along With a Geostatistical Prediction Map of 

 Neptunium KA,eff Sorption Values for the Yucca Mountain Region ................... 4-11 

4.4-1 Developing a Response Surface Using a Simplified Diffuse Layer Model 

 Approach. (a) U(VI)-Goethtite Sorption Data.................................................... 4-12 

4.4-2 Developing Parametric Expressions to Simulate Np(V)-Montmorillonite 

 Sorption Surface for a Range in PCO2 from 0 to 10−2

 atm. ................................ 4-13 

4.4-3 Flow Diagram for Implementing a Sorption Response Surface in 

 Performance Assessment ................................................................................ 4-14 
ix 

TABLES 

Table Page 

2.1.2-1 Summary of U(VI) Sorption Experiments on Various Minerals ......................... 2-27 

2.1.3-1 Summary of Np(V) Sorption Experiments on Various Minerals ........................ 2-28 

2.2.7.1-1 Summary of U(VI) Sorption Experiments on Mixed Minerals ........................... 2-29 

2.2.7.2-1 Summary of Np(V) Sorption Experiments on Alluvium and Related 

 Minerals Using Actual or Simulated Groundwaters .......................................... 2-30 

2.3.1 Best-Fit X-Ray Absorption Fine Structure Parameters for Uranyl Sorbed 

 on Montmorillonite and Clinoptilolite ................................................................. 2-31 

2.3-2 Comparison of X-Ray Absorption Fine Structure Data From This Study 

 With Literature Values ...................................................................................... 2-32 

3.2-1 Summary of Binary Ion-Exchange Experiments ............................................... 3-53 

3.2-2 Summary of Ternary and Quaternary Ion-Exchange Experiments ................... 3-53 

3.2-3 Model Parameters Derived From Regression of Ion-Exchange Data. 

 In Parentheses Are the Standard Errors in the Derived Parameters ................ 3-55 

3.4-1 Results for Simulations in Bulk Water .............................................................. 3-56 

3.4-2 Results for Simulations Near the Singly Protonated Quartz (010) Surface ...... 3-56 

3.4-3 Results for Simulations Near the Partially Deprotonated 

 Quartz (010) Surface ........................................................................................ 3-56 
x 

ACKNOWLEDGMENTS 

This report was prepared to document work performed by the Center for Nuclear Waste 

Regulatory Analyses (CNWRA®) for the U.S. Nuclear Regulatory Commission (USNRC) under 

Contract No. NRC–02–007–006. The studies and analyses reported here were performed on 

behalf of the NRC Office of Nuclear Material Safety and Safeguards, Division of High-Level 

Waste Repository Safety. The report is an independent product of the CNWRA and does not 

necessarily reflect the views or regulatory position of USNRC. 

The authors thank James Myers for technical review and English Pearcy for programmatic 

review. The authors also thank Arturo Ramos for support in report preparation and 

Lauren Mulverhill for editorial review. 

QUALITY OF DATA, ANALYSES, AND CODE DEVELOPMENT 

DATA: All CNWRA-generated original data contained in this report meet the quality assurance 

requirements described in the Geosciences and Engineering Division Quality Assurance 

Manual. Sources for other data should be consulted for determining the level of quality for 

those data.

ANALYSES AND CODES: No scientific and engineering software was used in the analyses 

contained in this report. 
1-1 

1 INTRODUCTION 

1.1 Background 

The potential release of radionuclides as dissolved constituents in groundwater is a primary 

concern in performance assessment of proposed high-level nuclear waste geologic repositories. 

Sorption onto mineral surfaces is an important mechanism for reducing radionuclide 

concentrations along groundwater flow paths and retarding radionuclide migration to a receptor 

location [such as the reasonably maximally exposed individual (RMEI)]. 

Early in the high-level nuclear waste program, U.S. Nuclear Regulatory Commission (NRC) staff 

recognized the importance of understanding processes that may influence radionuclide 

sorption in the engineered and natural barrier components of a geologic repository system at 

Yucca Mountain, Nevada. As part of its charter program work to support NRC in meeting its 

responsibilities under the Nuclear Waste Policy Act of 1982, as amended, the Center for 

Nuclear Waste Regulatory Analyses (CNWRA®) has been engaged since 1990 in experimental 

and modeling programs designed to understand how physical and chemical conditions can 

affect radionuclide sorption. A list of sorption-related reports and publications CNWRA 

produced staff is included as an appendix. 

The purpose of this knowledge management report is to capture insights gained during more 

than 20 years of CNWRA experience in evaluating radionuclide sorption processes and 

incorporating the results of detailed modeling into the performance assessment codes 

developed by NRC and CNWRA staffs. 

1.2 History of Radionuclide Sorption Technical Assistance at 

the CNWRA 

Beginning in 1990, CNWRA staff initiated experimental and modeling programs to evaluate 

processes that might influence radionuclide sorption, and to identify key geochemical and 

physical parameters that have the greatest effects. As the sorption program evolved, CNWRA 

staff focused on two main sorption mechanisms: (i) ion exchange, which has the greatest 

effects on alkali and alkaline earth radionuclides such as Cs+

 and Sr2+ and (ii) surface 

complexation, the process that exerts the most control on the sorption of long-lived actinides 

such as uranium, neptunium, plutonium, americium, and thorium. 

1.2.1 Experimental Program(s) and Developing Process Models 

1.2.1.1 Ion Exchange 

Zeolite minerals are naturally occurring aluminosilicate compounds with a characteristic 

three-dimensional pore structure that allows exchange of cations between aqueous solutions 

and intracrystalline sites. Because of their favorable ion-exchange selectivity for certain cations 

such as Cs+

, Sr2+, and NH4

+

, zeolite minerals are of interest for potential use in the remediation 

of sites contaminated with fission products such as Sr-90, Cs-135, and Cs-137. Zeolites are 

also of interest because Yucca Mountain is underlain by diagenetically altered, zeolite-rich 

(predominantly clinoptilolite) rhyolitic tuffs that could serve as barriers to radionuclide migration 

to the accessible environment. Ion-exchange equilibria are a function of solid and aqueous 

phase composition and aqueous solution concentration. Because these parameters can vary 
1-2 

substantially for the chemical system of interest, it is essential to have chemical models that 

allow accurate description and prediction of ion-exchange equilibria. 

To investigate the key aspects of ion-exchange processes and to develop the parameters 

needed to test different thermodynamic ion exchange models, CNWRA conducted experiments 

on ion exchange between clinoptilolite and aqueous solutions of Na+

, K+, Sr2+, and Ca2+ to 

examine binary, ternary, and quaternary exchanges. One thermodynamic approach that 

CNWRA staff found useful for this purpose combined the Pitzer electrolyte solution model to 

describe nonideal behavior in the aqueous phase with Wilson and Margules solid solution 

formulations to account for nonideality effects in the zeolite. The thermodynamic approach, 

detailed in Section 3.2, was successful in describing and predicting ion-exchange equilibria 

between clinoptilolite and aqueous solutions over wide ranges of solution composition 

and concentration. 

1.2.1.2 Surface Complexation 

In contrast to fission products like Cs-137 and Sr-90, sorption studies by CNWRA staff and 

other groups indicate that actinides, such as uranium and neptunium, sorb onto most silicate 

and oxide minerals not through ion exchange, but through a surface complexation mechanism. 

This type of sorption behavior has been well simulated using a class of chemical equilibrium 

models called surface complexation models. In a surface complexation approach, the sorbing 

radionuclides are assumed to form complexes with functional surface sites at the mineral–water 

interface that are analogous to actinide speciation in the bulk aqueous phase (Davis and Kent, 

1990). An example of a general type of surface complexation reaction for an actinide (An) 

sorbing on a functional surface site >SOH0

 would be 

[ ] ( )( ) ( ) > S O H 0 p A n m + n H O > S O H A n O H 1 n q H 2 q p n

p m + q n 1 +

+ + = − + +− − − −

(1.2-1)

The strength of the surface complex, and therefore the amount of sorbed radionuclide, depends 

on the speciation of the actinide in the bulk aqueous phase as well as the physical and 

electrostatic characteristics of the sorbing mineral. The surface complexation modeling 

approach is detailed in Section 3.3. 

The surface complexation approach works well at simulating sorption onto relatively simple 

pure mineral phases, but the dependence of sorption on aqueous solution properties 

(e.g., pH, radionuclide concentration, ionic strength, and complexing ligands) and sorptive 

phase characteristics (e.g., composition, surface area, and sorption site density) makes 

prediction of radionuclide retardation and transport difficult in natural geologic systems that are 

mineralogically and chemically heterogeneous. 

CNWRA staff conducted batch experiments to examine uranium and neptunium sorption on 

alumina, quartz, clinoptilolite, and montmorillonite. These phases were selected because they 

represent minerals that occur at Yucca Mountain, and because they represent a relatively broad 

range in surface properties. To evaluate the effects of key geochemical parameters, the 

experiments were conducted over a wide range in radionuclide concentrations, solution 

chemistry, and partial pressure of CO2 gas chemistry (see Section 2.2). CNWRA experiments 

indicated that actinide sorption onto silicates, aluminosilicates, and oxides tended to be 

strongest where the chemistry of the system (including complexing ligands such as carbonate) 

favors the formation of hydroxy complexes in the aqueous phase, and the magnitude of sorption 

is controlled primarily by the number of available sorption sites (see Section 2.4). Data from 
1-3 

these experiments were used to derive the parameters necessary to implement a simplified and 

internally consistent surface complexation model, that was then abstracted to provide input into 

performance assessment calculations (see Section 4). 

1.3 Process Model Abstraction for Total-system 

Performance Assessment 

Traditionally, performance assessment models have simulated radionuclide sorption using 

constant empirical relationships between the mass of radionuclide sorbed on the solid (S) to the 

mass of radionuclide dissolved in solution (C). The most common of these empirical 

relationships is the relatively straightforward linear sorption coefficient {Kd in mL/g [fl. oz/oz]} 

K S

C

V

M d = 



 









 



 (1.3-1) 

where S is the amount of radionuclide sorbed on the mineral surface {g/g or [oz/oz]}, C is the 

amount of radionuclide dissolved in the solution [g/mL (oz/fl. oz)]. V represents the solution 

volume (mL [fl. oz]), and M represents the mass of solid in g. The Kd is then used to represent 

the relative contaminant transport velocity through a retardation factor, Rf (unitless) (Freeze and 

Cherry, 1979), such that 

R g r o u n d w a t e r v el o ci t y

c o n t a mi n a n t v el o ci t y = 1 + K f

b

d = 



 



 ρ

θ (1.3-2)

where ρb = bulk density, and θ = porosity. For performance assessment calculations, the Kd

value for a given radioelement is typically assumed to be a property of the geologic medium, 

much like permeability and porosity. In actuality, the Kd value is a derived property that is 

known to be a function of system chemistry. The Kd approach assumes the amount of 

radionuclide sorbed on a solid is directly proportional to the amount dissolved in solution, but 

does not explicitly consider changes in system chemistry or variations in the mineral–water 

interface that may affect radionuclide sorption. For actinides, however, sorption is a complex 

function of chemistry, particularly pH, mineral surface area, and total carbon concentration 

(Bertetti, et al., 1998; Pabalan, et al., 1998; Turner, 1995; Wang, et al., 2001a,b; Davis and 

Curtis, 2003). For this reason, it is difficult to extrapolate experimentally derived sorption 

coefficients (Turner, 1995; Davis and Curtis, 2003). 

In performance assessment analyses, uncertainty and variability in sorption coefficients are 

represented by statistical parameter distributions based, at least in part, on expert judgment that 

is in turn based on experimental determinations of Kd. The parameter distributions for sorption 

coefficients and other uncertain parameters are sampled during individual numerical simulations 

or realizations. 

The principal goal of CNWRA sorption work has been to develop performance assessment 

methods that incorporate, at least indirectly, the effects of chemistry on radionuclide sorption 

coefficients. Turner, et al. (2006, 2002) identified two methods for doing this. In the first 

approach, a surface complexation model was applied with available site-specific hydrochemistry 

data to develop frequency histograms and the accompanying statistics for calculated Kd. These 

statistics provide more realistic constraints on the probability distributions used in performance 

assessment to describe radionuclide sorption. The model results suggest that lognormal 
1-4 

distributions of actinide sorption coefficients are appropriate. This approach allows the user to 

estimate statistical relationships correlating among the different calculated radionuclide sorption 

parameters and provides for an indirect inclusion of geochemical effects on sorption in 

performance assessment calculations. 

In the second approach, CNWRA staff used calibrated surface complexation models to simulate 

actinide sorption over a wide range of geochemical conditions. This approach allows the user to 

develop a sorption response surface for a given radionuclide as a function of key geochemical 

parameters. For each performance assessment realization, key geochemical parameters, such 

as pH and PCO2, are sampled from distributions based on site-specific data, and the 

corresponding Kd is estimated from the response surface. There is uncertainty in extrapolating 

beyond the experimental conditions used for parameterizing the model, but even so, the 

extrapolation is based on a more sound theoretical basis than strictly empirical approaches. 

1.4 References (Section 1) 

Bertetti, F.P., R.T. Pabalan, and M.G. Almendarez. “Studies of NeptuniumV Sorption on 

Quartz, Clinoptilolite, Montmorillonite, and α-alumina.” Adsorption of Metals by Geomedia. 

E.A. Jenne, ed. New York City, New York: Academic Press, Inc. pp. 131–148. 1998. 

Davis, J.A. and G.P. Curtis. NUREG/CR–6820, “Application of Surface Complexation Modeling 

to Describe Uranium(vi) Adsorption and Retardation at the Uranium Mill Tailings Site at Naturita, 

Colorado.” Washington, DC: NRC. 2003. 

Davis, J.A. and D.B. Kent. “Surface Complexation Modeling in Aqueous Geochemistry. 

Reviews in Mineralogy. Vol. 23.” Mineral-Water Interface Geochemistry. M.F. Hochella, Jr. 

and A.F. White, eds. Washington, DC: Mineralogical Society of America. pp. 77−260. 1990. 

Freeze, R.A. and J.A. Cherry. Groundwater. Englewood Cliffs, New Jersey: 

Prentice-Hall, Inc. 1979. 

Kent, D.B., V.S. Tripathi, N.B. Ball, J.O. Leckie, and M.D. Siegel. NUREG/CR–4807, 

“Surface-Complexation Modeling of Radionuclide Adsorption in Subsurface Environments.” 

Washington, DC: NRC. 1988. 

Pabalan, R.T., D.R. Turner, F.P. Bertetti, and J.D. Prikryl. “UraniumVI Sorption onto Selected 

Mineral Surfaces.” Adsorption of Metals by Geomedia. E.A. Jenne, ed. New York City, 

New York: Academic Press, Inc. pp 99–130. 1998. 

Turner, D.R. CNWRA 95-001, “A Uniform Approach to Surface Complexation Modeling of 

Radionuclide Sorption.” San Antonio, Texas: CNWRA. 1995. 

Turner, D.R., R.T. Pabalan, and F.P. Bertetti. Applying Surface Complexation Modeling to 

Radionuclide Sorption.” Surface Complexation Modeling. F. Lützenkirchen, ed. 

Amsterdam: Elsevier, Ltd: pp. 553–604. 2006. 

Turner, D.R., F.P. Bertetti, and R.T. Pabalan. “The Role of Radionuclide Sorption in High-Level 

Waste Performance Assessment: Approaches for the Abstraction of Detailed Models.” 

Soil Science Society of America Proceedings Volume: Soil Geochemical Processes of 

Radionuclides. pp. 211–252. Pittsburgh, Pennsylvania: Soil Science of America. 2002. 
1-5 

Wang, P., A. Anderko, and D.R. Turner. “Thermodynamic Modeling of Radionuclide Adsorption 

on Selected Minerals. I: Cations.” Industrial and Engineering Chemistry Research. Vol. 40. 

pp. 4,428–4,443. 2001. 

Wang, P., A. Anderko, and D.R. Turner. “Thermodynamic Modeling of Radionuclide Adsorption 

on Selected Minerals. II: Anions.” Industrial and Engineering Chemistry Research. Vol. 40. 

pp. 4,444–4,453. 2001.
2-1 

2 RADIONUCLIDE SORPTION EXPERIMENTS 

In a generic sense, the term sorption refers to the removal of a dissolved contaminant from 

solution through interaction with a solid substrate to which the solution is exposed. Sorption 

may involve several physicochemical processes including precipitation, co-precipitation, ion 

exchange, and surface complexation. Because of their long half-lives and radiotoxicity, the 

Center for Nuclear Waste Regulatory Analyses (CNWRA®) experimental program on sorption 

generally focused on actinide sorption through a process of surface complexation. 

The CNWRA sorption experiments were designed to identify, characterize, and reduce 

uncertainties of features and processes important to assessing the safety of high-level waste 

disposal. The experimental program was not intended to provide a comprehensive 

characterization of sorption and sorption parameters related to Yucca Mountain. Instead, work 

was focused on improving knowledge in critical areas as guided by the risk-informed 

prelicensing process. 

To develop an understanding of specific important physical and chemical parameters that affect 

sorption behavior in the Yucca Mountain environment, experiments were conducted to 

investigate the sorption behavior of uranium, neptunium, and other radionuclides on geologic 

media. The experiments were designed to determine the effects of pH, initial radionuclide 

concentration in solution, solid mass to solution volume ratio, solution ionic strength, and partial 

pressure of CO2(g) on sorption. The minerals selected for these sorption experiments were 

representative of matrix and fracture lining minerals in Yucca Mountain stratigraphic units 

(Bish and Chipera, 1989). 

Results of the experimental and associated sorption modeling program were used to (i) provide 

input and guidance for the development and review of performance assessment models, 

(ii) provide confirmatory results for review of U.S. Department of Energy (DOE) activities, and 

(iii) identify and reduce uncertainty in parameters deemed important to repository performance. 

2.1 Chemical Systems Considered 

Initial DOE investigations of the Yucca Mountain region indicated that the potential 

radionuclide flow paths within the volcanic and alluvial aquifers are dominated by 

sodium-calcium-bicarbonate waters of relatively low ionic strength (~0.003 M) (e.g., Ogard and 

Kerrisk, 1984). In addition, the groundwaters are generally oxidizing and ranged in pH from 

about 6 to 9. Subsequent data derived from DOE field studies and independent CNWRA field 

sampling confirmed these general characteristics for the volcanic and alluvial aquifers 

(e.g., Perfect, et al., 1995; Turner and Pabalan, 1999; Bertetti, et al., 2004; McMurry and 

Bertetti, 2005). The type and variations of the chemistry of Yucca Mountain groundwaters were 

carefully considered in the CNWRA experimental program on sorption, and experimental 

solution chemistries were tailored as needed for the goals of a particular sorption experiment. 

Early experimental efforts focused on developing a general understanding of sorption behavior 

over a wide range of pH conditions (e.g., Pabalan, et al., 1998). The influence of carbonate 

complexation of actinides on sorption was also considered by varying the CO2(g) concentrations 

of the experimental solutions. Because of the potential confounding effects of ion-exchange 

processes in assessing the complexation of radionuclides on mineral surfaces, many of the 

sorption experiments were conducted using a background electrolyte of 0.1 M NaNO3

(Pabalan, et al., 1998; Bertetti, et al., 1998), which helped to minimize ion exchange by 

providing an excess of Na+

 in solution. In some experiments, however, ionic strength of solution 
2-2 

was varied to examine its effects on sorption. All experiments were conducted under oxidizing 

conditions, which predominated along expected flow paths from Yucca Mountain to the 

accessible environment (e.g., Ogard and Kerrisk, 1984; Bertetti, et al., 2004). Radionuclide 

concentrations in experiments were constrained by the need to maintain concentrations low 

enough to remain below solubility limits (to prevent the potential confounding effects of 

precipitation) yet high enough to effectively quantify radionuclide concentrations with the 

analytical tools available. 

The sorption experimental program evolved as data were collected from DOE characterization 

of the Yucca Mountain region, and the conceptual understanding of the Yucca Mountain 

groundwater system and its transport processes matured. Initial experiments assessed the 

sorption influence of saturated volcanic strata below the proposed repository horizon by 

focusing on common minerals (quartz, clinoptilolite, calcite, and montmorillonite) identified in 

those units (Bish and Chipera, 1989). Later, as focus shifted to the saturated alluvium as a 

potentially important natural barrier, analyses were conducted to evaluate and confirm the 

mineralogy of potential sorptive surface in the alluvium (Bertetti and Werling, 2005; McMurry 

and Bertetti, 2005). As a result, experiments were expanded to include calcite and samples of 

alluvium collected in the field (e.g., Bertetti, 2002; Bertetti and Werling, 2005). Additionally, 

experiments were modified to assess sorption using actual groundwater collected from the 

Fortymile Wash region or using simulated groundwater that mimicked the composition of the 

alluvial aquifer (Bertetti and Werling, 2005). 

2.1.1 Alkaline/Alkaline Earths (Na–Ca–K–Sr–Cs) 

The sorption of fission products, such as Sr-90 and Cs-137, was studied primarily as part of the 

ion-exchange experimental program, which included many other alkaline and alkaline-earth 

cations. These experiments and their results are discussed in Section 3.2. 

2.1.2 Uranium 

Sorption experiments focused primarily on the actinides uranium and neptunium. Uranium was 

an important component of the experiments because (i) by mass it was the most prevalent 

radionuclide in spent fuel, (ii) there were known instances in groundwaters of the contamination 

and transport of oxidized U(VI), and (iii) the long half-life of various uranium isotopes meant that 

uranium would be part of the spent-fuel inventory and a potential contributor to dose for a long 

period of time. Moreover, aqueous chemistry data for uranium were more readily available, and 

there were numerous methods available for analyzing and quantifying uranium in solution. In 

the oxidizing Yucca Mountain groundwaters relevant to the sorption experimental program, 

U(VI) is expected to exist as the uranyl species (UO2

2+) (e.g., Allard, et al., 1984). 

Experiments investigating uranium sorption utilized the isotope U-233, which was available in a 

National Institute of Standard and Technology-traceable purified form from Isotope Products 

Laboratories (Burbank, California). Like the other radionuclides used in the sorption 

experimental program, the U-233 was quantified in solution by liquid scintillation analysis. 

Liquid scintillation analysis enabled analyses of a relatively high throughput of experimental 

solutions with a minimum of sample chemical pretreatment. 

Uranium at various concentrations (~10−6

 to 10−7 M) was reacted with the minerals α-alumina, 

clinoptilolite, montmorillonite, and quartz to understand uranium sorption behavior, produce data 

that enabled the development of surface complexation models, and confirm DOE analyses. 

Results of the numerous uranium sorption experiments indicated uranium sorption is highly 
2-3 

dependent on the pH of solution and, when CO2(g) is present, on the formation of uranium 

carbonate complexes. Uranium sorption increases with increasing solution pH and, when 

CO2(g) is present, displays maximum sorption at about pH of 6 before decreasing with further 

increases in pH. Specific sorption results for various experiments are discussed in Section 2.2. 

Uranium-related sorption experiments are summarized in Table 2.1.2-1. 

2.1.3 Neptunium 

Because of its long half-life (2.14 × 106 yr), radiotoxicity, occurrence in high-level waste, and 

reported low sorption characteristics, Np-237 was a particular concern to the safety suitability of 

the Yucca Mountain repository. Under the oxidizing conditions typical of the Yucca Mountain 

environment, neptunium is expected to exist in solution primarily in one oxidation state (V) as 

the neptunyl species, NpO2

+

 (e.g., Allard, et al., 1984; Fuger, 1992). Early in the sorption 

experimental program there were relatively few published investigations of the sorption 

behavior of neptunium (e.g., Allard, et al., 1984; Bidoglio, et al., 1988; Lieser and Mühlenweg, 

1988; Nakayama and Sakamoto, 1991). As DOE characterization activities progressed 

(e.g., Triay, et al., 1993), it was clear that neptunium exhibited relatively low Kd values, and the 

range of reported neptunium Kd values suggested that neptunium could potentially be 

transported through the natural system within regulatory time frames. Thus, an extensive effort 

was made in the CNWRA program to reduce uncertainties in the magnitude and sorption 

behavior of neptunium. 

Experiments investigating neptunium sorption utilized the isotope Np-237, which was available 

in a NIST-traceable purified form from Isotope Products Laboratories (Burbank, California). Like 

the other radionuclides used in the sorption experimental program, the Np-237 was quantified in 

solution by liquid scintillation analysis. The liquid scintillation analysis equipment used 

(Packard 2550TR/AB and later 3100TR/AB) enabled separation of the alpha activity signal of 

Np-237 and the beta activity signal of its immediate daughter Pa-233 so that no chemical 

separation or preprocessing of experimental solution samples was required. 

Like the early uranium experiments, the early neptunium experiments were conducted using a 

background electrolyte of NaNO3 or NaClO4. Later experiments used actual groundwater 

collected from the Yucca Mountain region or synthetic groundwater at ionic strengths of about 

1 × 10−3

 M. Solutions with neptunium concentrations of approximately 2 × 10−6 M or less were 

reacted with quartz, clinoptilolite, α-alumina, montmorillonite, calcite, and alluvium over the 

course of the experiments. 

Results of the neptunium sorption experiments indicated neptunium sorption was highly 

dependent on solution pH and carbonate concentration. Similar to observations of uranium 

sorption, neptunium speciation (hydrolysis and carbonate complexation) play key roles in 

neptunium sorption behavior. Maximum measured neptunium Kd values were much lower 

(more than two orders of magnitude) than those observed for uranium, and the neptunium 

sorption peak in solutions where CO2(g) was present occurred at pH between 8–8.5. 

Specific sorption results for various experiments are discussed in Section 2.2. 

Neptunium-related sorption experiments are summarized in Table 2.1.3-1. 

2.1.4 Technetium 

Under chemically oxidizing conditions typical of Yucca Mountain groundwaters, technetium has 

high solubility and exists as the pertechnetate anion (TcO4

−

), which has a low sorption potential. 
2-4 

The fission product Tc-99 has a relatively long half-life (2 × 105

 yr) and sufficient mass inventory 

in spent fuel to make it a potential contributor to dose at regulatory time scales. Limited 

experiments were conducted to investigate the potential for the sorption of Tc-99 onto aquifer 

materials. The experiments were conducted on clinoptilolite and calcite over a range of pH at 

technetium concentrations of 3–4 × 10−7

 M. The results of both sets of experiments show that 

for the conditions studied there was no discernable sorption of Tc-99 (Figure 2.1-1), which was 

consistent with the representation of Tc-99 in performance assessment as a 

nonsorbing radionuclide. 

2.2 Batch Experiments 

The laboratory sorption investigations were conducted as batch experiments in which the solid 

substrate was immersed in a solution of known composition containing the radionuclide(s) of 

interest. In general, experiments were conducted so that sorption reactions and solutions would 

come to equilibrium before the solutions were sampled for radionuclide concentration and 

chemistry. Several confirmatory kinetics experiments were conducted with uranium, neptunium, 

and technetium to evaluate the minimum required duration of experiments. In all cases, the 

sorption reactions equilibrated within 48 hours. The limiting factor for solution equilibration was 

the rate of gas exchange between the experimental solutions and atmosphere, which took 

several days. Thus, care was taken to ensure adequate exposure to atmosphere and mixing of 

the solutions, and experiments were conducted for typical periods of 7–10 days. 

The batch experiments were conducted using polycarbonate bottles or test tubes. 

Polycarbonate was selected based on results of tests to evaluate sorption of uranium and 

neptunium on container materials including Teflon-FEP, polypropylene, and polycarbonate. The 

results of those tests showed that in the absence of a competing substrate, the polycarbonate 

sorbed less uranium than the other materials tested. When a competing substrate was added, 

sorption on the container was reduced further (Figure 2.2-1). Some uranium sorption results 

were corrected as needed to account for container sorption (Pabalan, et al., 1998). Neptunium 

sorption onto containers was negligible in the presence of even a minimally sorbing 

competing substrate. 

A main goal of the sorption experiments was to evaluate the effects of variations in solution 

chemistry on sorption behavior. Varying pH conditions were typically established by adding 

small amounts of acid or base to solutions. The partial pressure of CO2(g) in solutions was 

established by exposing the experimental solutions to lab atmosphere or conducting the 

experiments inside an atmosphere-controlled glove box in which a known PCO2 (higher or lower 

than atmosphere) was maintained. As an alternative to glove box experiments, some 

low-CO2(g) conditions were tested by using tightly capped vials to prevent gas exchange with 

lab atmosphere. 

Minerals used in the majority of experiments were selected based on (i) expected or identified 

occurrences in Yucca Mountain strata or (ii) differences in surface characteristics. For example, 

the reported points of zero charge (pHpzc)—the pH at which the net electrical surface charge of 

the mineral is neutral or zero—for quartz, clinoptilolite, and α-alumina are 2.9, 3.0, and 9.1, 

respectively (Davis and Kent, 1990; Gainer, 1990), and pHpzc values reported for 

montmorillonite range from 6.5 to 8 depending on the ionic strength of the background 

electrolyte (Wanner, et al., 1994). Single component minerals were acquired from readily 

available, commercial sources. Except for the α-alumina, all were physically and/or chemically 

pretreated to isolate particular size fractions and to remove unwanted mineral impurities and 

grain coatings. Natural alluvium used in later experiments was collected from Early Warning 
2-5 

Drilling Program well core samples and was treated as necessary for a particular 

experiment design. The Early Warning Drilling Program was conducted by Nye County, in 

part, to characterize the geology and hydraulic properties of the alluvial aquifers south of 

Yucca Mountain. 

The external surface areas of the sorbent materials were determined using a multipoint N2-BET 

isotherm measured using a Coulter SA3100 surface area analyzer. The surface area samples 

were prepared by outgassing for 24 hours at 350 °C [662 °F] {100 °C [212 °F ] in the case of 

montmorillonite}. The composition and purity of the mineral sorbents were checked by 

petrographic examination and x-ray diffraction, scanning electron microscope/energy dispersive 

x-ray spectroscopy, x-ray fluoresence, and inductively coupled plasma analyses. 

2.2.1 Quartz 

Quartz was obtained as quartz sand (Wedron #510) from Wedron Silica Company (Illinois). 

The quartz sand was sieved to isolate the 60–100 mesh fraction {0.250–0.149 mm 

[0.0098–0.0059 in]} and was pretreated to remove impurities in a fashion similar to that used for 

clinoptilolite. A portion of the 60–100 mesh fraction was crushed using an agate mortar and 

pestle to create a fine-grained fraction {0.044–0.004 mm [0.0017–0.00016 in]} with higher 

surface area for use in evaluating the effects on sorption due to changes in surface area. The 

N2-BET-measured surface areas of the 60–100 mesh fraction and the fine-grained fraction were 

0.03 ± 0.01 and 0.5 ± 0.05 m2

/g, respectively. 

Sorption experiments involving quartz were conducted for both U-233 and Np-237. Variables 

tested included pH, solid mass to solution volume ratio, radionuclide concentration, PCO2, and 

surface area. Results of the experiments investigating U-233 sorption on quartz revealed 

patterns similarly observed for all other minerals studied in the sorption program: (i) in the 

absence of CO2(g), sorption increased with increasing pH (see Figure 2.2-2); (ii) in the presence 

of CO2(g), a sorption maximum was observed at pH values of ~6.5 and decreased with further 

increase in pH (see Figure 2.2-4); (iii) increasing the PCO2 content of solutions reduced the 

observed maximum sorption and shifted the pH of peak sorption to lower pH values 

(not shown); (iv) for the pH range and uranium concentration ranges studied, uranium sorption 

can be represented as a Freundlich isotherm at constant pH values (not shown); and 

(v) normalization of sorption data to effective surface area produces an overlap of sorption data 

for minerals of different surface types (see Figure 2.2-4) (Pabalan, et al., 1998). Because of its 

relatively low specific surface area, quartz sorbed the least uranium of the minerals tested in the 

experiments (Pabalan, et al., 1998). 

Results of the experiments studying Np-237 sorption on quartz revealed patterns similarly 

observed for all other minerals studied in the sorption program: (i) in the absence of CO2(g), 

sorption increased with increasing pH (see Figure 2.2-3); (ii) in the presence of CO2(g), a 

sorption maximum was observed at pH values of ~8.25 and decreased with further increase in 

pH (see Figure 2.2-3); (iii) increasing the PCO2 content of solutions reduced the observed 

maximum sorption and shifted the pH of peak sorption to lower pH values (not shown); (iv) over 

the range of neptunium concentrations studied, neptunium sorption appeared to be 

linear (not shown); and (v) normalization of sorption data to effective surface area produces 

an overlap of sorption data for minerals of different surface types (see Figure 2.2-5) 

(Bertetti, et al., 1998). Because of its relatively low specific surface area, quartz sorbed the 

least neptunium of the minerals tested in the experiments (Bertetti, et al., 1998). 
2-6 

2.2.2 Alumina 

The α-alumina (α-Al2O3) samples used in the experiments were obtained as powders from 

NIST, which issued the material as a certified reference material for measurements of specific 

surface area of powders. Reference materials 8005, 8006, and 8007, with reported surface 

areas of 2.09, 0.229, and 0.0686 m2

/g, were used in the sorption experiments. To minimize 

altering the surface of the α-alumina, the powder was not washed or pretreated prior to its use. 

Sorption experiments involving quartz were conducted for both U-233 (atmospheric CO2 only) 

and Np-237 (no CO2 only). Variables tested included pH and surface area. Results of 

experiments investigating U-233 sorption on α-alumina indicated the pH-related 

sorption behavior was typical of that observed for quartz and other aluminosilicate minerals 

(Pabalan, et al., 1998). In the presence of CO2(g), U-233 sorption peaked at pH of ~6.5 and 

decreased at higher and lower pH values (Figure 2.2.-4). Modeling indicated that the decrease 

in sorption at high pH resulted from formation of uranium-carbonate complexes in solution 

(Prikryl, et al., 1993). 

Results of studies of Np-237 sorption on α-alumina showed that in the absence of CO2(g), 

sorption increased with increases in pH throughout the pH range studied (Figure 2.2-5) (Bertetti, 

et al., 1998). Normalization of sorption data to account for effective surface area of the αalumina resulted in an overlap of similarly processed data for silicate minerals (Figure 2.2-5). 

Because the α-alumina samples had known surface areas, their results provided confidence in 

the process of normalizing sorption data to effective surface areas to represent sorption across 

multiple mineral types. 

2.2.3 Clinoptilolite 

Clinoptilolite was obtained from a clinoptilolite-rich tuff from Death Valley Junction, 

California (Minerals Research Company). The tuff was crushed and sieved to separate the 

100–200 mesh fraction {0.150–0.074 mm [0.0059–0.0029 in]} and chemically pretreated to 

remove soluble salts, carbonates, iron-oxides, and mineral impurities. The pretreatment is 

detailed in Pabalan (1994). The clinoptilolite was then converted to sodium-form by ion 

exchange with 3 M NaCl solutions. The measured N2-BET surface area for the clinoptilolite 

was 10.1 ± 0.3 m2

/g. 

Results of experiments investigating the sorption of U-233 on clinoptilolite revealed the similar 

pH-related behavior observed for other minerals (quartz, α-alumina, and montmorillonite) 

(Pabalan, et al., 1998) (Figures 2.2-2 and 2.2-4). Sorption maximum was reached at about a 

pH of 6 in the presence of CO2(g), while sorption increased with increasing pH throughout the 

pH range tested under conditions where CO2(g) was absent. The uranium sorption behavior is 

linked to U(VI)-speciation in solution (e.g., Pabalan, et al., 1998). Sorption occurs when the 

UO2

2+ species undergoes significant hydrolysis (pH ~6), and sorption is diminished with the 

formation of uranium-carbonate species at higher pH values (~7–8) (see Figure 2.2-4). For the 

ionic strengths tested in the experiments (0.1 and 1.0 M NaNO3), results indicate that ionic 

strength of the solution had little influence on U-233 sorption on clinoptilolite. It is likely that 

some ion exchange of uranium in clinoptilolite will occur at lower pH values (below 5) in more 

dilute solutions (Zachara and McKinley, 1993; McKinley, et al., 1995), but this was not tested in 

the experiments. 

Similar to the data for quartz, results of experiments investigating the sorption behavior of Np(V) 

onto clinoptilolite (Figure 2.2-6) also show pH dependence (Bertetti, et al., 1998; Bertetti and 
2-7 

Werling, 2005). Significant sorption begins near pH 7 for conditions with and without 

atmospheric PCO2 present. For solutions at equilibrium with atmospheric PCO2, Np-237 sorption 

onto clinoptilolite reaches a maximum near pH 8 and decreases again to a minimum near pH 

9.5. Experimental solutions with no CO2(g) exhibit a continued increase in sorption as solution 

pH increases, with the maximum sorption attained at the highest pH studied. There is little 

observable uptake of Np(V) below pH 7. Additional information garnered from the Np-237 

sorption experiments showed that variation in ionic strength of the NaNO3 matrix from 0.1 to 

0.01 M did not promote any discernible increase in sorption for the pH range studied. However, 

at lower pH and ionic strengths Np(V) may be sorbed through an ion-exchange mechanism 

(e.g., Kozai, 1994). This behavior may be reflected in the results of the neptunium–alluvium 

sorption experiments (see Figure 2.2-11) (Bertetti and Werling, 2005), which show net positive 

sorption on clinoptilolite and montmorillonite-bearing alluvium at pH values between 4 and 6.5, 

below the pH range where significant surface adsorption is expected for NpO+

. 

2.2.4 Montmorillonite 

Montmorillonite (SAz-1, Apache County, Arizona) was obtained from the Source Clays 

Mineral Repository. The clay was processed using ion exchange with 3 M NaCl to make a 

sodium–montmorillonite form, and the less than 2-μm [3.9 × 10−5

 in] sized fraction was 

isolated and saved for use. The clay was lyophilized for storage prior to use in experiments. 

The N2-BET-measured surface area of the sodium–montmorillonite was 97 ± 2 m2

/g, 

(Bertetti, et al., 1998). 

Results of experiments studying U-233 sorption on montmorillonite revealed pH-dependent 

sorption of U(VI) similar to that observed for quartz, clinoptilolite, and α-alumina (Figure 2.2-4) 

(Pabalan, et al., 1998). The uranium-speciation drive sorption effects produced sorption 

maxima near pH 6 under conditions where CO2(g) was present. The magnitude of U-233 

sorption on montmorillonite was the greatest of any of the minerals studied, reaching Kd values 

on the order of 104

 mL/g at atmospheric PCO2. Like the experiments with clinoptilolite, 

U(VI)-sorption experiments with montmorillonite used solutions at ionic strengths of 0.1 and 

0.01 M NaNO3, which inhibited the ion exchange of U(VI) at lower pH values (below 5). 

Normalization of the uranium–montmorillonite sorption data to effective surface area 

[10 percent of the measured surface area (Pabalan, et al., 1998; Wanner, et al., 1994)] 

resulted in an overlap with similarly normalized data from other minerals, suggesting that the 

KA,eff parameter (see Section 2.4.3) would be useful in performance assessment models 

(Figure 2.2-4) (Pabalan, et al., 1998). 

Over the pH range considered in experiments, the magnitude of Np-237 sorption onto 

sodium–montmorillonite is greater than that observed for the other minerals studied 

(Figure 2.2-7) (Bertetti, et al., 1998; Bertetti and Werling, 2005; Turner, et al., 1999). However, 

the sorption trends with change in pH are very similar to the behavior observed for quartz and 

clinoptilolite (Figures 2.2-5 and 2.2-7). Np(V) sorption onto montmorillonite begins to increase 

significantly at a solution pH of about 7. For solutions in equilibrium with atmospheric PCO2, the 

magnitude of sorption peaks near a pH of 8.25 and decreases as pH increases, forming a high 

pH sorption edge. Solutions undersaturated with respect to atmospheric PCO2 show a 

continuous increase in sorption with increasing pH through the pH range examined. Unlike the 

sorption behavior observed in experiments with quartz and clinoptilolite, there is a net positive 

uptake (Kd values on the order of 10 mL/g) of Np-237 from pH values of about 4 to 7, regardless 

of PCO2 conditions. This net positive sorption may be attributed to ion exchange between NpO2

+

and Na+

 in the clay. Similar sorption occurred at lower pH values in the neptunium–alluvium 
2-8 

experiments likely due to the montmorillonite content (which is interpreted to control sorption in 

alluvium) of those samples (Bertetti and Werling, 2005). 

Results of the neptunium–montmorillonite sorption experiments were used to develop and test 

the applicability of a diffuse-layer model surface complexation modeling approach to represent 

sorption in the Yucca Mountain region (Figure 2.2-7) (Turner, et al., 2002, 1998). The 

diffuse-layer model is detailed in Sections 3.3 and 4.4. 

2.2.5 Calcite 

Reagent-grade calcite [CaCO3(s), Fischer Scientific], aged, dried, and separated for storage, 

was used in the calcite sorption experiments. The calcite was chemically and 

mineralogically free of impurities. Details of calcite preparation and analyses are provided in 

Bertetti (2002, 2001). The calcite had a surface area of approximately 0.23 ± 0.02 m2

/g as 

measured using N2-BET analysis, which was consistent with results of similarly prepared calcite 

from other studies (e.g., Stipp, et al., 1992; Zachara, et al., 1991). 

Results of Np-237 and calcite sorption experiments conducted at ~1.6 × 106 M Np-237 for a 

range of pH from 7.25 to 9, at equilibrium with atmospheric PCO2 and at or near equilibrium with 

calcite in solution. The results confirmed the pH dependence of Np-237 sorption on calcite and 

showed calcite is an effective sorber of Np-237 for the pH range studied (Bertetti, 2002, 2001) 

(Figure 2.2-8). The Np-237 sorption magnitude observed on calcite was similar to or greater 

than the sorption of Np-237 by montmorillonite for the same pH range (Bertetti, 2002, 2001; 

Bertetti, et al., 1998). The experiments also indicated that the magnitude of sorption on calcite 

was more variable between repeat experiments than observed for other minerals tested. 

This behavior was possibly due to the sensitivity of the calcite surfaces to changes in 

solution chemistry. 

A constant capacitance surface complexation model [used because of the availability of 

thermodynamic data from Van Cappellen, et al. (1993)] provided a good fit to experimental data 

but was less successful in predicting sorption of other solid mass to solution volume ratios 

(Bertetti, 2002). Model results indicate that Np-237 uptake was reduced by sorption of Ca2+

onto the calcite surface and by formation of neptunyl carbonate complexes in solution. These 

results were confirmed by later studies (e.g., Zavarin, et al., 2005) 

2.2.6 Iron (Hydr)oxides (Corrosion Products) 

Radionuclides may attach to carbon steel corrosion products (e.g., iron oxyhydroxides) 

produced by the degradation of carbon steel components within waste packages. Iron 

oxyhydroxides have been shown to have a high sorption affinity for many metals including some 

radionuclides (Dzombak and Morel, 1990). As a preliminary test of the capability of carbon 

steel corrosion products to sorb neptunium, sorption experiments were conducted using the 

carbon steel corrosion products and solutions produced in corrosion test cells described in 

He, et al. (2007). 

The experiments consisted of removing a quantity of corrosion product grains from the test cells 

and reacting the solids with Np-237 at concentrations of ~1 × 10−6

 M in filtered water from the 

test cells (simulated J-13 well water) (He, et al., 2007). 

The resulting Np-237 Kd values for the carbon steel corrosion products ranged from 350 to 

790 mL/g (Figure 2.2-4). Experimental group A, whose solutions generally had lower 
2-9 

equilibrium pH values, has an average Np-237 Kd of 514 mL/g, while group B had an average 

Np-237 Kd of 650 mL/g. The measured Kd values for each experimental group were not 

statistically different from each other; there is substantial overlap between the values. The 

magnitude of neptunium sorption is substantial {by comparison, montmorillonite clay has a 

measured Kd for neptunium of about 40 mL/g at similar pH values} and is somewhat expected 

for iron oxyhydroxides (Dzombak and Morel, 1990). 

Unfortunately, chemical analyses of the solutions and x-ray diffraction analyses of the solids 

revealed trace amounts of calcite (He, et al., 2007). Although unlikely given the amounts 

present, the potential confounding effects of the sorption of Np-237 on the calcite could not be 

dismissed with the data available (Figure 2.2-9) and further investigations were recommended. 

2.2.7 Mineral Mixtures 

2.2.7.1 Quartz and Clinoptilolite 

Uranium sorption experiments involving quartz and clinoptilolite mixtures were conducted 

to evaluate the ability of surface complexation models to predict U(VI) sorption onto 

mineral mixtures based on parameters derived from single mineral experiments 

(Prikryl, et al., 1999). The experiments were conducted at an initial uranium aqueous 

concentration of ∼2.0 × 10−7

 mol·L−1 (in a 0.1 mol·L−1 NaNO3 matrix) and over the pH 

range ∼2.5 to ∼9.5. The experiments were carried out under atmospheric PCO2 conditions 

(in loosely capped containers) or under limited or low PCO2 conditions (in capped containers or in 

a glove box). 

Results of the experiments were consistent with the magnitude and pH and PCO2-dependent 

behavior observed in previous work (Pabalan, et al., 1998) (Figure 2.2-10). Data from sorption 

experiments on quartz at atmospheric PCO2 conditions were used to derive binding constants for 

a diffuse-layer model. The diffuse-layer model was then used with surface area as a scaling 

factor to predict sorption of U(VI) onto clinoptilolite and clinoptilolite/quartz mixtures under both 

atmospheric and low PCO2 conditions. The calculations reproduced many aspects of the 

pH-dependent sorption behavior. This approach could be used as a relatively simple method 

for improving radionuclide transport models in performance assessment calculations. 

2.2.7.2 Alluvium 

Multiple sorption experiments were conducted using groundwater and alluvium samples 

collected from the Fortymile Wash region to gain preliminary insight into the sorption behavior of 

Np-237 onto alluvium and to help corroborate Kd values used in performance assessments. 

To enhance understanding of Np-237 sorption onto alluvium under varying groundwater 

compositions, simulated groundwaters were created and used in the experiments (Bertetti and 

Werling, 2005). 

Results of the sorption experiments with natural alluvium substrate and groundwater or 

simulated groundwater show a Np-237 sorption maximum {Kd values of approximately 25 to 

30 mL/g over a range of pH from about 7.5 to 8.5 (Figure 2.2-11). Although Np-237 sorption 

generally decreases away from the pH range of peak sorption, at high pH (greater than 8.7) 

some experiments produced apparent Kd values that were as much as two orders of magnitude 

greater than those observed in the range of 7.5 to 8.5. The presence of both calcite and small 

amounts of iron oxides in the alluvium samples likely contributed to these larger sorption values. 

The broad Np-237 sorption peak and the location of that peak may be influenced by the 
2-10 

presence of clinoptilolite or small amounts of calcite in the alluvium samples—alluvium samples 

with calcite removed exhibited sorption behavior more similar to that observed in previous 

experiments on aluminosilicates (Figure 2.2-11). 

A comparison of the alluvium sorption data to a diffuse-layer model developed to describe 

Np-237 sorption on montmorillonite (Turner, et al., 2002, 1998) reveals that the model does an 

excellent job of reproducing the experimental observations for Np-237 sorption on alluvium 

(Figure 2.2-11). In this case the model was scaled using the effective surface area for 

montmorillonite and replotted using the estimated effective surface area for the alluvium 

samples (Bertetti, et al., 2004). The comparison provides confidence in the generic diffuse-layer 

model approach used to represent sorption in alluvium in performance assessment modeling. 

2.3 References 

Allard, B., U. Olofsson, and B. Torstenfelt. “Environmental Actinide Chemistry. Inorganica 

Chimica Acta. Vol. 94. pp. 205–221. 1984. 

Bechtel SAIC Company, LLC. “EBS Radionuclide Transport Abstraction.” 

ANL–WIS–PA-000001. Rev. 1. Las Vegas, Nevada: Bechtel SAIC Company, LLC. 2004. 

Bertetti, F.P. “Laboratory and Modeling Studies of Neptunium Uptake on Calcite.” 

San Antonio, Texas: CNWRA. 2002. 

Bertetti, F.P. “Laboratory Experiments of Neptunium Uptake on Calcite—Letter Report.” 

San Antonio, Texas: CNWRA. 2001. 

Bertetti, F.P. and B. Werling. “Sorption of Neptunium-237 on Alluvium Collected from Fortymile 

Wash, Nye County, Nevada—Status Report.” San Antonio, Texas: CNWRA. 2005. 

Bertetti, F.P., J. Prikryl, and B. Werling. “Development of Updated Total-System Performance 

Assessment Parameter Distributions for Radionuclide Transport in the Saturated Zone.” 

San Antonio, Texas: CNWRA. 2004. 

Bertetti, F.P., R.T. Pabalan, and M.G. Almendarez. “Studies of Neptunium(V) Sorption on 

Quartz, Clinoptilolite, Montmorillonite, and α-alumina.” Adsorption of Metals by Geomedia: 

Variables, Mechanisms, and Model Variations. E. Jenne, ed. New York, City, New York: 

Academic Press, Inc. pp. 131–148. 1998. 

Bidoglio, G., A. Avogadro, A. DePlano, and G.P. Lazarri. “Reaction Pathways of Pu and Np in 

Selected Natural Water Environments.” Radiochimica Acta. Vol. 44/45. pp. 29–32. 1988. 

Bish D.L. and S.J. Chipera. “Revised Mineralogic Summary of Yucca Mountain, Nevada.” 

LA–11497–MS. Los Alamos, New Mexico: Los Alamos National Laboratory. 1989. 

Davis, J.A. and D.B. Kent. “Surface Complexation Modeling in Aqueous Geochemistry. 

Reviews in Mineralogy: Volume 23.” Mineral-Water Interface Geochemistry. M.F. Hochella, 

Jr., and A.F. White, eds. Washington, DC: Mineralogical Society of America. pp. 177–260. 

1990. 

Dzombak, D.A. and F.M.M. Morel. Surface Complexation Modeling: Hydrous Ferric Oxide. 

New York City, New York: John Wiley & Sons. 1990. 
2-11 

Fuger, J. “Thermodynamic Properties of Actinide Species Relevant to Geochemical Problems. 

Radiochimica Acta. Vol. 59. pp. 81–91. 1992. 

Gainer, G.M. “Boron Adsorption on Hematite and Clinoptilolite. M.S. Thesis. University of 

Texas at El Paso. p. 122. 1990. 

He, X., V. Jain, F.P. Bertetti, and D. Pickett. “Evolution of Fluid Chemistry Inside a Waste 

Package Due to Carbon Steel and Simulated High-Level Waste Glass Corrosion-Progress 

Report.” San Antonio, Texas: CNWRA. 2007. 

Kozai, N. “Sorption Characteristics of Neptunium by Smectite.” In Progress Report on Safety 

Research on Radioactive Waste Management for the Period April 1992 to March 1993. 

S. Muraoka, M. Senoo, and K. Sekine, eds. Tokai, Japan: Japan Atomic Energy Research 

Institute. pp. 39–41. 1994. 

Lieser, K.H. and U. Mühlenweg. “Neptunium in the Hydrosphere and Geosphere. I. Chemistry 

of Neptunium in the Hydrosphere and Sorption of Neptunium From Groundwaters on Sediments 

Under Aerobic and Anaerobic Conditions.” Radiochimica Acta. Vol. 43. pp. 27–35. 1988. 

McKinley, J.P., J.M. Zachara, S.C. Smith, and G.D. Turner. “The Influence of Hydrolysis and 

Multiple Site-Binding Reactions on Adsorption of U(VI) on Montmorillonite.” Clays and Clay 

Minerals. Vol. 43. pp. 586–598. 1995. 

McMurry, J. and F.P. Bertetti. “Selection of Sorption-Related Values For Unsaturated Zone and 

Saturated Zone Transport In Total-System Performance Assessment.” CNWRA Letter Report. 

San Antonio, Texas: CNWRA. June 2005. 

Nakayama, S. and Y. Sakamato. “Sorption of Neptunium on Naturally-Occurring 

Iron-Containing Minerals. Radiochimica Acta. Vol. 52/53. pp. 153–157. 1991. 

Ogard, A.E. and J.F. Kerrisk. LA–10188-MS, “Groundwater Chemistry Along Flow 

Paths Between a Proposed Repository Site and the Accessible Environment.” 

Los Alamos, New Mexico: Los Alamos National Laboratory. 1984. 

Pabalan, R.T. “Thermodynamics of Ion-Exchange Between Clinoptilolite and 

Aqueous Solutions of Na+

/K+ and Na+/Ca2+.” Geochimica et Cosmochimica Acta. Vol 58. 

pp. 4,573–4,590. 1994. 

Pabalan, R.T., D.R. Turner, F.P. Bertetti, and J.D. Prikryl. “Uranium(VI) Sorption onto Selected 

Mineral Surfaces: Key Geochemical Parameters.” Adsorption of Metals by Geomedia: 

Variables, Mechanisms, and Model Variations. E. Jenne, ed. New York City, New York: 

Academic Press. pp. 99–130. 1998. 

Perfect, D.L., C.C. Faunt, W.C. Steinkampf, and A.K. Turner. “Hydrochemical Data Base for 

the Death Valley Region, California and Nevada. USGS Open-File Report 94-305. 

Denver, Colorado: U.S. Geological Survey. 1995. 

Prikryl, J.D., A. Jain, R.T. Pabalan, and D.R. Turner. “Uranium (VI) Sorption Behavior on Mixed 

Silicate Minerals.” Journal of Contaminant Hydrology. Vol. 47, Issues 2–4. pp. 241–253. 

1999. 
2-12 

Prikryl, J.D., R.T. Pabalan, and B.W. Leslie. “Uranium Sorption on Alumina: Effects of pH and 

Surface Area/Solution Volume Ratio.” Fourth International Conference on the Chemistry and 

Migration Behavior of the Actinides and Fission Products in the Geosphere Abstract, 

MIGRATION '93. Charleston, South Carolina: December 12–17, 1993. 1993. 

Stipp, S.L., M.F. Hochella, G.A. Parks, and J.O. Leckie. “Cd2+ Uptake by Calcite, Solid-State 

Diffusion, and the Formation of Solid-Solution: Interface Processes Observed with 

Near-Surface Sensitive Techniques (XPS, LEED, and AES).” Geochimica et Cosmochimica 

Acta. Vol. 56. pp. 1,941–1,954. 1992. 

Triay, I.R., B.A. Robinson, R.M. Lopez, A.J. Mitchell, and C.M. Overly. “Neptunium Retardation 

With Tufts and Groundwaters From Yucca Mountain.” Proceedings of the 4th Annual 

International Conference on High-Level Radioactive Waste Management, Las Vegas, Nevada, 

April 1993. La Grange Park, Illinois: American Nuclear Society. pp. 1,504–1,508. 1993. 

Turner, D.R. and R.T. Pabalan. “Abstraction of Mechanistic Sorption Model Results for 

Performance Assessment Calculations at Yucca Mountain, Nevada.” Waste Management. 

Vol. 19. pp. 375–388. 1999. 

Turner, D.R., F.P. Bertetti, and R.T. Pabalan. “The Role of Radionuclide Sorption in High-Level 

Waste Performance Assessment: Approaches for the Abstraction of Detailed Models.” 

Geochemistry of Soil Radionuclides. Special Publication 59. Soil Science Society of America. 

P.-C. Zhang and P.V. Brady, eds. Madison, Wisconsin. pp. 211–252. 2002. 

Turner, D.R., R.T. Pabalan, J.D. Prikryl, and F.P. Bertetti. “Radionuclide Sorption at Yucca 

Mountain, Nevada - Demonstration of an Alternative Approach for Performance Assessment.” 

J. Lee and D. Wronkiewicz, eds. Materials Research Society Symposium Proceedings: 

Scientific Basis for Nuclear Waste Management—XXII. Pittsburgh, Pennsylvania: Materials 

Research Society: 583-590. 1999. 

Turner, D.R., R.T. Pabalan, and F.P. Bertetti. “Neptunium (V) Sorption on Montmorillonite: An 

Experimental and Surface Complexation Modeling Study.“ Clays and Clay Minerals. Vol. 46. 

pp. 256–269. 1998. 

Van Cappellen, P., L. Charlet, W. Stumm, and P. Wersin. “A Surface Complexation Model of 

the Carbonate Mineral-Aqueous Solution Interface.” Geochimica et Cosmochimica Acta. 

Vol. 57. pp. 3,505–3,518. 1993. 

Wanner, H., Y. Albinsson, O. Kml, E. Wieland, P. Wersin, and L.Charlet. “The Acid Base 

Chemistry of Montmorillonite. Radiochimica Acta. Vol. 66/67. pp. 733–738. 1994. 

Zachara, J.M. and J.P. McKinley. “Influence of Hydrolysis on the Sorption of Metal Cations by 

Smectites: Importance of Edge Coordination Reactions.” Aquatic Sciences. Vol. 55. 

pp. 250–261. 1993. 

Zachara, J.M., C.E. Cowan, and C.T. Resch. “Sorption of Divalent Metals on Calcite.” 

Geochimica et Cosmochimica Acta. Vol. 55. pp. 1,549–1,562. 1991. 

Zavarin, M., S.K. Roberts, N. Hakem, A.M. Sawvel, and A. B. Kersting. “Eu(III), Sm(III), Np(V), 

Pu(V), and Pu(IV) Sorption to Calcite.” Radiochimica Acta. Vol. 93. pp. 93–102. 2005. 
2-13 

2.4 X-Ray Absorption Fine-Structure Spectroscopy Study of 

Uranium(VI) Sorption 

Traditional sorption methods focus on the macroscopic aspects of the interaction of an aqueous 

species with a mineral surface (i.e., Kd), but give no direct information on the structural and 

chemical environment around a single atom or ion. X-ray absorption fine structure 

spectroscopy, which includes extended x-ray absorption fine structure and x-ray absorption 

near-edge structure spectroscopy, is an ideal tool for the direct determination of the structure of 

surface-adsorbed complexes and can be used to probe the mechanism of adsorption on the 

molecular scale. In this study, x-ray absorption fine structure spectroscopy was used to 

elucidate the structure and oxidation state of uranium species sorbed onto the clay mineral 

montmorillonite and the zeolite mineral clinoptilolite, which are important minerals at 

Yucca Mountain. Understanding of specific surface complexes on those minerals offered some 

insights into the most appropriate surface reactions to be incorporated into detailed sorption 

process models. Both minerals contain two types of sorption sites: (i) permanently charged 

cation-exchange sites and (ii) variably charged surface hydroxyl groups. The former is due to 

isomorphic substitution in the structure [e.g., of Al(III) for Si(IV)] within the tetrahedral layer of 

montmorillonite or within the zeolite framework causing a permanent negative charge that is 

compensated by cations interacting with the interlayer or intracrystalline exchange sites. The 

latter is due to partially coordinated aluminum and silicon exposed at the clay crystallite edges 

or zeolite surfaces, which hydrolyze to form aluminol (AlOH) and silanol (SiOH) groups. These 

hydroxylated sites exhibit acid/base behavior and coordinative properties similar to those of 

oxide (e.g., SiO2) surfaces. 

Uranium(VI) sorption onto mineral surfaces is a strong function of pH [Figure 2.3-1(a)]. At 

atmospheric PCO2(g), uranyl sorption is highest at near-neutral pHs where aqueous uranyl 

species are mainly in the form of hydroxyl complexes [Figure 2.3-1(b)]. Uranyl sorption is low at 

alkaline pHs where uranyl-carbonate species are predominant. For minerals with no 

permanently charged cation-exchange sites, such as quartz, uranyl sorption is also low at acidic 

pHs where aqueous uranyl exists primarily as the mononuclear, ionic species UO2

2+. However, 

uranyl sorption onto negatively charged cation-exchange sites of montmorillonite and 

clinoptilolite from solutions of low pH and low ionic strength has been shown to be important 

(Zachara and McKinley, 1993; Andreeva and Chernyavskaya, 1982). 

2.4.1 Experimental 

The Ca–montmorillonite (SAz-1; source locality: Cheto, Arizona) and Na–clinoptilolite 

(source locality: Death Valley Junction, California) used in the study were pretreated to remove 

carbonates and iron oxides, and dried prior to the experiments. Samples for x-ray absorption 

fine structure analysis were prepared by reacting 1 gram of mineral powder with 450 mL of 

uranyl nitrate solution (4.6 × 10–5 or 9.2 × 10–5 M U-238). Samples were prepared at pH ~3 and 

~6 so the effect of pH on the sorption mechanism could be evaluated. The reaction bottles 

were placed on a gyratory shaker, and air was bubbled through the solutions to maintain 

atmospheric PCO2. Periodic adjustments of pH were made with HNO3 or NaOH to maintain a pH 

of ~3 or ~6. After about 2 weeks, the mixtures were centrifuged and the moist pastes were 

loaded onto x-ray absorption fine structure sample holders. The uranium concentrations before 

the addition of solid and after sorption equilibrium was achieved were analyzed by inductively 

coupled plasma to determine the uranyl uptake. 
2-14 

X-ray absorption fine structure spectra were collected on the moist paste samples at beamline 

X11A of the National Synchrotron Light Source at Brookhaven National Laboratory. Data 

collection and analysis are described in Reeder, et al. (2001). 

2.4.2 Results 

The near-edge regions of the uranium L2-edge absorption spectra are shown in Figure 2.3-2(a). 

For all samples, the edge positions, relative to U(IV) and U(VI) reference standards, and the 

presence of a characteristic shoulder above the absorption maximum confirm the uranium 

remains in the 6+ oxidation state as the O=U=O moiety. No obvious difference in the near-edge 

structure is observed among the samples. Fourier transform magnitudes are shown in 

Figures 2.3-2(b) and 2.3-2(c). All Fourier transform magnitudes show a principal peak at 1.4 Å, 

corresponding to two axial oxygens at ~1.8 Å in the uranyl moiety. Peaks at ~2 Å in the 

Fourier transform correspond to equatorial oxygen shells. For the montmorillonite and 

clinoptilolite reacted at pH ~3, the Fourier transform magnitudes show a single peak well 

separated from the axial oxygen component. In contrast, both minerals at pH ~6 show separate 

and weaker peaks, suggesting split equatorial oxygen coordination. No significant peaks are 

apparent at higher R, indicating no significant backscattering from silicon, aluminum, or other 

uranium atoms and unlikely formation of a second uranyl-containing solid phase. A weak peak 

at ~3 Å in the Fourier transform reflects a multiple-scattering path within the UO2 moiety. 

For all fits, a multiple-scattering contribution at ~3.58 Å was fitted with the four-legged 

Oax1-U-Oax2-U path. 

Best fit results are reported in Table 2.3-1. All fits show two axial oxygens in the range 

1.77–1.79 Å. The uranyl sorbed onto montmorillonite at pH 3.3 shows a single equatorial shell 

of five–six oxygens at 2.41 Å. At pH 6.3, the montmorillonite is best fitted with two equatorial 

shells (each with ~three oxygen atoms) at 2.28 and 2.44 Å. Generally similar fit results were 

found for the uranyl sorbed on clinoptilolite. The clinoptilolite reacted at pH 6.4 shows two 

equatorial oxygen shells at 2.28 and 2.45 Å. For the clinoptilolite sample reacted at pH 3.2, the 

single peak in the Fourier transform suggested a single equatorial oxygen shell, and fitting 

yielded ~five oxygen atoms at 2.45 Å. However, the fit quality was not as good as for the 

montmorillonite at pH 3.3. The fit was improved by an additional, weak equatorial oxygen 

component, giving best fit results of ~one oxygen at 2.21 Å and ~four oxygens at 2.42 Å. The 

significance of the weak contribution at 2.21 Å needs additional clarification. Attempts to fit 

uranium–aluminum and uranium–silicon paths generally resulted in very small coordination 

number values that were significantly less than the estimated errors. The lack of uranium 

backscattering also suggests that sorbed uranyl species are mononuclear. 

The results on montmorillonite derived from this study are in agreement with the results 

Sylwester, et al. (2000) published. As shown in Table 2.3-2, for montmorillonite reacted with 

uranium solutions at a pH near 3, both studies indicate the presence of a single equatorial shell, 

with five to six oxygens at a distance of ~2.42 Å. Sylwester, et al. (2000) interpreted their 

results as an indication of an outer-sphere mechanism for uranyl sorption, with the uranyl 

existing as a monomeric oxo-cation surrounded by an outer hydration shell. For uranium 

sorbed onto montmorillonite at a pH near 6, both studies observe a split equatorial shell in the 

uranyl structure. Sylwester, et al. (2000) interpreted this equatorial splitting as evidence of an 

inner-sphere complexation with the surface. 

The results of Sylwester, et al. (2000) for uranyl sorbed onto silica are also shown in the 

Table 2.3-2 for comparison with data from this study. Silica does not have permanently charged 

cation-exchange sites, as found in montmorillonite and clinoptilolite. In contrast to uranyl sorbed 
2-15 

on montmorillonite, uranyl sorbed on amorphous silica exhibits equatorial splitting over the pH 

range 3 to 6.5, which Sylwester, et al. (2000) interpreted as evidence of inner-sphere surface 

complexes at that pH range. 

Also tabulated are the results of Hudson, et al. (1999) for uranyl sorbed onto vermiculite and 

hydrobiotite, which both have a layer structure. The results for these minerals are quite similar 

to those for montmorillonite. At low pH, a single equatorial shell is observed, with coordination 

numbers and uranium–oxygen distances about the same as those observed for uranium 

sorbed on montmorillonite. At near-neutral pH, vermiculite and hydrobiotite both show 

equatorial splitting as observed for montmorillonite, although the coordination number and 

uranium–oxygen distances are somewhat different compared to montmorillonite. 

2.4.3 Conclusions 

The results of this study show that the uranium remains in the 6+ oxidation state as the uranyl 

ion moiety, with two axial oxygens ~1.8 Å distance from the uranium atom. There is a 

difference in the equatorial coordination of the uranyl sorbate as a function of pH for both 

montmorillonite and clinoptilolite. Split equatorial shells are evident for both samples at pH ~6, 

whereas primarily a single shell exists at pH ~3. The results for montmorillonite are 

consistent with the extended x-ray absorption fine structure data Sylwester, et al. (2000) 

reported. The structure of U(VI) sorbed on montmorillonite is similar to that of U(VI) sorbed on 

vermiculite and hydrobiotite (Hudson, et al., 1999). 

The difference in uranium–oxygen distances in the equatorial oxygen shell for the samples at 

pH ~6 likely reflect a difference in the bonding of these oxygens with the sorption sites of the 

minerals. Hence, the split equatorial shells probably indicate discrete equatorial oxygens form 

chemical bonds at surface functional groups, as would be expected for an inner-sphere-type 

surface complex. In contrast, the single equatorial shell for samples at pH ~3 suggests a more 

uniform bonding environment for the oxygens as would be expected for an outer-sphere-type 

complex. Such an environment is consistent with ion exchange at cation-exchange sites of the 

sorbents. However, the experiments do not provide direct evidence that the uranyl ion is 

located in the interlayer exchange sites of montmorillonite or in the intracrystalline channels of 

clinoptilolite. The alternate x-ray absorption fine structure fit indicating equatorial splitting for 

clinoptilolite reacted at pH ~3 is consistent with distortion of the equatorial shell due to steric 

limitations imposed by the zeolite structure. Further investigations are needed to refine the 

zeolite sorption mechanisms. Additional work using polarized x-ray absorption fine structure 

could help in providing such evidence.

2.4.4 References (Section 2.3) 

Andreeva, N.R. and N.B. Chernyavskaya. “Uranyl Ion Sorption by Mordenite and Clinoptilolite.” 

Radiokhimiya. Vol. 24. pp. 9–13. 1982. 

Bertetti, F.P., R.T. Pabalan, and M.G. Almendarez. “Studies of Neptunium(V) Sorption on 

Quartz, Clinoptilolite, Montmorillonite, and α-alumina.” Adsorption of Metals by Geomedia: 

Variables, Mechanisms, and Model Variations. E. Jenne, ed. New York City, New York: 

Academic Press, Inc. pp. 131–148. 1998. 

Hudson, E.A., L.J. Terminello, B.E. Viani, M. Denecke, T. Reich, P.G. Allen, J.J. Bucher, 

D.K. Shuh, and N.M. Edelstein. “The Structure of U6+ Sorption Complexes on Vermiculite and 

Hydrobiotite.” Clays and Clay Minerals. Vol. 47. pp. 439–457. 1999. 
2-16 

Pabalan, R.T., D.R. Turner, F.P. Bertetti, and J.D. Prikryl. “UraniumVI Sorption Onto Selected 

Mineral Surfaces.” Adsorption of Metals by Geomedia. E.A. Jenne, ed. New York City, 

New York: Academic Press, Inc. pp. 99–130. 1998. 

Reeder, R.J., M. Nugent, and R.T. Pabalan. “Local Structure of Uranium(VI) Sorbed on 

Clinoptilolite and Montmorillonite.” Water-Rock Interaction. R. Cidu, ed. 

Lisse, The Netherlands: A.A. Balkema Publishers. pp. 423–426. 2001. 

Sylwester, E.R., E.A. Hudson, and P.G. Allen. “The Structure of Uranium(VI) Sorption 

Complexes on Silica, Alumina, and Montmorillonite.” Geochimica et Cosmochimica Acta. 

Vol. 64. pp. 2,431–2,438. 2000. 

Turner, G.D., J.M. Zachara, J.P. McKinley, and S.C. Smith. “Surface-Charge Properties and 

UO2

2+ Adsorption of a Surface Smectite.” Geochimica et Cosmochimica Acta. Vol. 60 

pp. 3,399–3,414. 1996. 

Wanner, H., Y. Albinsson, O. Kml, E. Wieland, P. Wersin, and L.Charlet. “The Acid Base 

Chemistry of Montmorillonite. Radiochimica Acta. Vol. 66/67. pp. 733–738. 1994. 

Zachara, J.M. and J.P. McKinley. “Influence of Hydrolysis on the Sorption of Metal Cations by 

Smectites: Importance of Edge Coordination Reactions.” Aquatic Sciences. Vol. 55. 

pp. 250–261. 1993. 

2.5 Key Geochemical Parameters Controlling Radionuclide Sorption 

2.5.1 Effects of Aqueous Speciation 

For actinides, sorption behavior typically varies as a function of its aqueous speciation, with a 

close correspondence between the pH dependence of sorption behavior, the predominance 

field of actinide-hydroxy complexes, and the presence of complexing ligands such as carbonate 

(Bertetti, et al., 1998; Pabalan, et al., 1998). Under low pH conditions, actinide sorption tends to 

be weak, except under low ionic strength conditions for cation exchangers such as 

montmorillonite and, to a lesser extent, clinoptilolite (e.g., Pabalan, et al., 1993; Turner, et al., 

1996). With increasing pH, actinide sorption increases, with a maximum typically in the pH 

range where the hydroxy complexes are important. In carbonate-free laboratory systems, 

actinide sorption continues to increase with increasing pH and increasing hydrolysis 

(e.g., Bertetti, et al., 1998; Pabalan, et al., 1998; Turner, et al., 1998), but actinide sorption 

behavior is sensitive to the presence of carbonate or other ligands in solution that affect 

aqueous speciation. For example, in carbonate-bearing systems, actinide sorption tends to 

decrease with increasing pH and/or increasing carbonate concentration (Bertetti, et al., 1998; 

Pabalan, et al., 1998). 

2.5.2 Physical Properties 

In contrast to the relatively pronounced effects of aqueous speciation on actinide sorption, the 

similarity in the pH dependence of actinide sorption on a wide variety of minerals, such as 

quartz, alumina, clinoptilolite, montmorillonite, amorphous silica, kaolinite, and titanium oxide, 

suggests a relative insensitivity to surface charge characteristics of the sorbent as compared to 

the effect of changing the total number of available sites. For example, the CNWRA data 

demonstrate that U(VI) sorption on quartz, α-alumina, clinoptilolite, and montmorillonite in 

the presence of atmospheric CO2 (PCO2 = 10−3.5 atm) is strongly affected by solution pH 
2-17 

(Pabalan, et al., 1998). Although the minerals used in the experiments have different 

mineralogic and surface properties, U(VI) sorption on these minerals is similar with respect to 

dependence on pH. In all cases, U sorption is at a maximum at near-neutral pH (~6.0 to ~6.8) 

and decreases sharply toward more acidic or more alkaline conditions. In experiments, ionic 

strength effects up to about 1 M seem to be limited for actinide surface complexation reactions 

(Pabalan, et al., 1998). Elevated ionic strength may also stabilize aqueous complexes that can 

affect actinide sorption behavior. Similar behavior was observed for Np(V) sorption (Bertetti, et 

al., 1998), although the sorption maximum occurred at pH of around 8, reflecting the higher pH 

for the formation of Np(V)-hydroxy aqueous species. 

2.5.3 Representing Actinide Sorption Data 

Experimental sorption results are typically plotted in terms of percentage sorbed versus pH. In 

the U(VI)–H2O–CO2 system, the amount of U(VI) sorbed (in percent) relative to the initial 

amount of U(VI) in solution increases with increasing M/V ratio, giving a broader sorption 

“envelope.” The apparent M/V effect, however, is mostly eliminated if the results are plotted in 

terms of Kd. As demonstrated in Pabalan, et al. (1998), U(VI) sorption on quartz, alumina, 

clinoptilolite, and montmorillonite is similar with respect to pH dependence. However, the Kd

values for the different minerals vary over three orders of magnitude. This variation is an artifact 

of normalizing the data to the sorbent mass and representing sorption data in terms of Kd. 

Similar observations were reported for Np(V) sorption (Bertetti, et al., 1998). 

Surface areas measured by gas adsorption (e.g., N2-BET) methods are a relative index of the 

number of sorption sites on the mineral surface, and it may be more useful to represent sorption 

data normalized to the specific surface area of the mineral sorbent (Bertetti, et al., 1998; 

Pabalan, et al., 1998). Surface area normalized sorption data for clinoptilolite and 

montmorillonite are indistinguishable, whereas surface area normalized sorption data for quartz 

and alumina are almost coincident. 

Surface areas determined by N2-BET methods most likely overestimate the amount of sorption 

sites on layered silicates, such as montmorillonite, and zeolitic minerals, such as clinoptilolite. 

For example, it is believed that surface complex formation of U(VI) on montmorillonite occurs on 

the hydroxylated edge sites of the mineral (Turner, et al., 1996). Wanner, et al. (1994) 

estimated that only 10 percent of the N2-BET specific surface area is accounted for by the 

crystallite edges of montmorillonite. Assuming that the “effective” surface area (Aeff) for 

montmorillonite and clinoptilolite is equivalent to about 10 percent of the measured surface area 

(A), sorption data for montmorillonite and clinoptilolite can be recast in terms of KA,eff where 

KA,eff = Kd / Aeff (2.5-1) 

For nonlayered and nonporous minerals such as quartz and alumina, KA = KA,eff. KA values 

for quartz, clinoptilolite, and montmorillonite, which have distinct mineralogic and surface 

properties, are essentially equivalent when recast in terms of KA,eff. This suggests that at 

least for aluminosilicate minerals, expressing sorption in terms of KA,eff provides a generalized 

basis for performance assessment transport calculations. The Kd values for a given 
2-18 

hydrostratigraphic unit can then be backed out assuming an effective specific surface area and 

the relationship 

Kd = KA,eff × Aeff (2.5-2) 

Similar results have been demonstrated for Np(V) (Bertetti, et al., 1998). 

2.6 References (Section 2.4) 

Bertetti, F.P., R.T. Pabalan, and M.G. Almendarez. “Studies of NeptuniumV Sorption on 

Quartz, Clinoptilolite, Montmorillonite, and -Alumina.” Adsorption of Metals by Geomedia. 

E.A. Jenne, ed. New York City, New York: Academic Press. pp. 131–148. 1998. 

Pabalan, R.T., J.D. Prikryl, P.M. Muller, and T.B. Dietrich. “Experimental Study of Uranium(6+) 

Sorption on the Zeolite Mineral Clinoptilolite.” C. Interrante and R. Pabalan, eds. 

MRS Symposium Proceedings Volume 294: Scientific Basis for Nuclear Waste 

Management XVI. Pittsburgh, Pennsylvania: Material Research of Society. pp. 777–782. 

1993. 

Pabalan, R.T., D.R. Turner, F.P. Bertetti, and J.D. Prikryl. “UraniumVI Sorption Onto Selected 

Mineral Surfaces.” Adsorption of Metals by Geomedia. E.A. Jenne, ed. New York City, 

New York: Academic Press. 1998. 

Turner, D.R., R.T. Pabalan, and F.P. Bertetti. “Neptunium(V) Sorption on Montmorillonite: An 

Experimental and Surface Complexation Modeling Study.” Clays and Clay Minerals. Vol. 46. 

pp. 256–269. 1998. 

Turner, G.D., J.M. Zachara, J.P. McKinley, and S.C. Smith. “Surface-Charge Properties and 

UO2

2+ Adsorption of a Surface Smectite.” Geochimica et Cosmochimica Acta. Vol. 60. 

pp. 3,399–3,414. 1996. 
2-19 

Figure 2.1-1. Results of Experiments Investigating Tc-99 Sorption on Calcite (Triangles) 

and Clinoptilolite (Circles). Estimates of the Minimum Uncertainty Associated With the 

Measurements Are Shown by the Error Bars. Data for Calcite Experiment Represent an 

Average of Four Separate Runs at Each pH. A Red Line Indicating Kd = 0 Is Shown as a 

Guide to the Reader. 

Figure 2.2-1. Plot of the Percentage U-233 Sorbed Onto Polycarbonate Experimental 

Containers Before (Blue) and After (Yellow) Addition of Quartz Substrate 
2-20 

Figure 2.2-2. Plot of U-233 Sorption on Quartz in the Absence of CO2(g) and Varying pH, 

Solid Mass to Solution Volume Ratios (M/V), and U-233 Concentrations. Error Bars Show 

the Calculated Total Uncertainty for Data. 

Figure 2.2-3. Plot of Np-237 Sorption on Quartz Under Varying pH, PCO2, Solid Mass to 

Solution Volume Ratios (M/V), and Np-237 Concentrations. Error Bars Show Calculated 

Total Uncertainty for Selected Data. 
2-21 

Figure 2.2-4. Plot of U-233 Sorption on α-Alumina, Clinoptilolite, Montmorillonite, and 

Quartz Under Atmospheric PCO2 Conditions and Normalized to Effective Surface Area 

(KA,eff) of Each Mineral. Error Bars Show Calculated Total Uncertainty for Data. 

Figure 2.2-5. Plot of Np-237 Sorption on α-Alumina, Clinoptilolite, Montmorillonite, and 

Quartz in the Absence of CO2(g) and Normalized to Effective Surface Area (KA,eff) of 

Each Mineral 
2-22 

Figure 2.2-6. Plot of Np-237 Sorption on Clinoptilolite Under Varying PCO2 Conditions 

Figure 2.2-7. Plot of Np-237 Sorption on Montmorillonite Under Varying PCO2 Conditions. 

A Diffuse Layer Model Was Fit to the non-CO2 Data and the Parameters Used To Predict 

Sorption at Atmospheric PCO2. 
2-23 

Figure 2.2-8. Plot of Np-237 Sorption on Calcite Under Atmospheric PCO2 and Varying 

Solid Mass to Solution Volume Ratios (M/V). Error Bars Show the Range of Values for 

Replicate Experiments in an Example Experimental Series (NpCA2). 

Figure 2.2-9. Plot of Measured Np-237 Kd Values for the Carbon Steel Corrosion 

Products, Calcite-Bearing Alluvium, and the Range of Values DOE Used 

(Bechtel SAIC Company, LLC, 2004) for Iron Oxides. The Solid Blue Circle Indicates the 

Average Measured Np-237 Kd for the Alluvium Samples at High pH; the Error Bars for the 

Alluvium Represent One Standard Deviation. The DOE Range for Np-237 Sorption Onto 

Iron Oxide Is Not Linked to Any Specific pH Value and Is Shown at pH of 8.7 as an 

Example Only. 

Np K D for Carbon Steel Corrosion Products and Calcite-Bearing Alluvium

0

200

400

600

800

1000

1200

1400

1600

1800

8.00 8.20 8.40 8.60 8.80 9.00 9.20 9.40 9.60

pH

Np 

KD (mL/g)

NpCS A

NpCS B

Avg A

Avg B

Avg Np CAL

Np CAL

DOE Fe-oxide Range
2-24 

Figure 2.2-10. Plot of the Sorption of U-233 as a Function of Effective Surface Area for 

the Quartz–Clinoptilolite Mixed Mineral Experiments. Error Bars Show Calculated 

Uncertainty for Selected Samples. 

Figure 2.2-11. Plot of Np-237 Sorption on Alluvium Sample Composites (CAL) Collected 

From Fortymile Wash, Nevada. Experiments Were Conducted Under Atmospheric PCO2

Conditions. The White Squares (CAL 7–11) Represent the Average Kd Values on Well 

NC–EWDP–19PB-Composite Alluvium for Experiments CAL7 Through CAL11 

(Bertetti and Werling, 2005). Results of Subsequent Experiments Using 

Composite Alluvium, Some of Which Was Pretreated to Remove Calcite, From Wells 

NC–EWDP–19PB and NC–EWDP–22PC Are Also Shown. The Solid Curve Represents a 

Model Prediction Based on the Diffuse-Layer Model Used for Np-237 Sorption on 

Montmorillonite (See Figure 2.2-7) and Scaled Using the Effective Surface Area for 

Montmorillonite and the Effective Surface Area for the Alluvium Composites 

(Bertetti, et al., 2004). Error Bars Represent ±1 Standard Deviation of the Averaged CAL 7 

to CAL 11 Data at Each pH Value. 
2-25 

 (a) (b) 

Figure 2.3-1. Comparison of (a) Uranyl Sorption on Na–Montmorillonite, 

Na–Clinoptilolite, and Quartz and (b) Uranyl Aqueous Speciation (ΣUi = 2.1 × 10−7

 M; PCO2

= 10−3.5 atm; 0.1 M NaNO3 Matrix). Sorption Data From Pabalan, et al. (1998). The Thick 

Solid Curve in (b) Is the Total Molality of Uranyl Hydroxy Complexes. Potential Ion 

Exchange of UO2

2+ With Na+

 in Montmorillonite and Clinoptilolite at pH <4 Is Suppressed 

by the 0.1 M NaNO3 Background Electrolyte of the Experiments. 
2-26 

(a) (b) 

(c) 

Figure 2.3-2. (a) Near-Edge Regions of Uranium L2-Edge Absorption Spectra and Fourier 

Transform Magnitudes (k3

-Weighting; Uncorrected for Phase Shifts) for Uranyl Sorbed on 

(b) Montmorillonite and (c) Clinoptilolite at Different pH Values 

Uranium L2 absorption edges

Photon energy (eV)

20900 21000 21100

Normalized absorption

0.0

0.5

1.0

1.5

Clinopt pH 3.2

Clinopt pH 6.4

Mont pH 3.3

Mont pH 6.3

Fourier transforms

R (Å)

0123456

Transform magnitude

Mont pH 3.3

Mont pH 6.3

Oax

Oeq

Oeq

Fourier transforms

R (Å)

0123456

Transform magnitude

Clinopt pH 3.2

Clinopt pH 6.4

Oax

Oeq

Oeq

Oeq?
2-27 

Table 2.1-1. Summary of U(VI) Sorption Experiments on Various Minerals 

Experiment Mineral 

M/V 

(g/L) U conc. (M) PCO2 pH Solution 

A1 α-alumina 2.88 5.00 × 10−

7

 10−

3.5 2–9 0.1 M NaNO3

A2 α-alumina 2.79 4.84 × 10−

7

 10−

3.5 2–9 0.1 M NaNO3

A3 α-alumina 2.81 4.90 × 10−

7

 10−

3.5 2–9 0.1 M NaNO3

C1 Na-clinoptilolite 2.04 2.17 × 10−

7

 10−

3.5 2–9 0.1 M NaNO3

C2 Na-clinoptilolite 2.09 2.22 × 10−

6

 10−

3.5 2–9 0.1 M NaNO3

C3 Na-clinoptilolite 2.05 1.90 × 10−

8

 10−

3.5 2–9 0.1 M NaNO3

C4 Na-clinoptilolite 2.07 2.17 × 10−

7

 10−

3.5 2–9 0.1 M NaNO3

C5 Na-clinoptilolite 20.28 2.10 × 10−

7

 10−

3.5 2–9 0.1 M NaNO3

C6 Na-clinoptilolite 2.43 2.10 × 10−

7

 10−

2.0 2–9 0.1 M NaNO3

M1 Na-montmorillonite 3.2 2.45 × 10−

7

 10−

3.5 2–9 0.1 M NaNO3

M2 Na-montmorillonite 0.27 2.06 × 10−

7

 10−

3.5 2–9 0.1 M NaNO3

M3 Na-montmorillonite 0.028 2.10 × 10−

7

 10−

3.5 2–9 0.1 M NaNO3

M4 Na-montmorillonite 0.28 2.16 × 10−

6

 10−

3.5 2–9 0.1 M NaNO3

Q1 quartz 2 2.14 × 10−

7

 10−

3.5 2–9 0.1 M NaNO3

Q2 quartz 20 2.11 × 10−

7

 10−

3.5 2–9 0.1 M NaNO3

Q3 quartz 50 2.06 × 10−

7

 10−

3.5 2–9 0.1 M NaNO3

Q4 quartz 20 2.00 × 10−

8

 10−

3.5 2–9 0.1 M NaNO3

Q5 quartz 50 2.15 × 10−

6

 10−

3.5 2–9 0.1 M NaNO3
2-28 

Table 2.1.3-1. Summary of Np(V) Sorption Experiments on Various Minerals

Experiment(s) Mineral M/V (g/L) Np conc. (M) PCO2 pH Solution 

NpCA1, 1–3 calcite 25 2.5 × 10−

6 10−3.5 7–9 0.1 M NaClO4

NpCA2, 1–3 calcite 5 2.5 × 10−

6 10−3.5 7–9 0.1 M NaClO4

NpCA3, 1–2 calcite 10 2.5 × 10−

6 10−3.5 7–9 0.1 M NaClO4

NpCA4, 1–3 calcite 3.3 1.6 × 10−

6 10−3.5 7–9 0.1 M NaClO4

NpCA5, 1–3 calcite 6.6 1.6 × 10−

6 10−3.5 7–9 0.1 M NaClO4

NpCA6, 1–3 calcite 15 1.6 × 10−

6 10−3.5 7–9 0.1 M NaClO4

NpCA7, 1–4 calcite 3.3 1.6 × 10−

6 10−3.5 7–9 0.1 M NaClO4

NpCA8, 1–4 calcite 13.33 1.6 × 10−

6 10−3.5 7–9 0.1 M NaClO4

NpCA9, 1–4 calcite 3.33 8.2 × 10−

7 10−3.5 7–9 0.1 M NaClO4

NpCA10, 1–4 calcite 13.33 8.2 × 10−

7 10−3.5 7–9 0.1 M NaClO4

NpCA11, 1–3 calcite 5 2 × 10−

6 10−3.5 7–9 0.1 M NaClO4

NpCA12, 1–3 calcite 10 2 × 10−

6 10−3.5 7–9 0.1 M NaClO4

NpQ1 quartz 40 1 × 10−

7 10−3.5 4–10 0.1 M NaNO3

NpQ2 quartz 80 1 × 10−

7 10−3.5 4–10 0.1 M NaNO3

NpQ3 quartz 40 1 × 10−

6 10−3.5 4–10 0.1 M NaNO3

NpQ4 quartz 40 1 × 10−

6 capped 6–11 0.1 M NaNO3

NpQ5 quartz 4 1 × 10−

6 capped 6–11 0.1 M NaNO3

NpC1 Na-clinoptilolite 4 1 × 10−

6 10−3.5 3–10 0.1 M NaNO3

NpC2 Na-clinoptilolite 4 1 × 10−

6 capped 4–11 0.01 M NaNO3

NpC3 Na-clinoptilolite 8 1 × 10−

6 10−3.5 3–10 0.01 M NaNO3

NpC4 Na-clinoptilolite 8 1 × 10−

6 no CO2 4–11 0.1 M NaNO3

NpM1 Na-montmorillonite 4 1 × 10−

6 capped 5–10 0.1 M NaNO3

NpM2 Na-montmorillonite 4 1 × 10−

6 10−3.5 3–10 0.1 M NaNO3

NpM3 Na-montmorillonite 4 1 × 10−

6 no CO2 5–10 0.1 M NaNO3

NpA1 a-alumina 4 1 × 10−

6 capped 3–11 0.01 M NaNO3

NpA2 a-alumina 4 1 × 10−

6 no CO2 3–11 0.01 M NaNO3
2-29 

Table 2.2.7.1-1. Summary of U(VI) Sorption Experiments on Mixed Minerals 

Experiment Mineral(s) 

M/V (g/L) 

Zeolite 

M/V (g/L) 

Quartz 

Zeolite/ 

Quartz 

Ratio PCO2 pH U Conc. (M) 

C20 Na-clinoptilolite 20 – – 10−3.5 3–10 2.06 × 10−7

CU20 Na-clinoptilolite 20 – – No CO2 3–10 2.09 × 10−7

Q quartz – 28.6 – 10−3.5 3–10 2.03 × 10−7

QU quartz – 28.6 – capped 3–10 2.04 × 10−7

QC1 Na-clinoptilolite/quartz 1 28.6 0.035 10−3.5 3–10 2.04 × 10−7

QC10 Na-clinoptilolite/quartz 10 28.6 0.35 10−3.5 3–10 1.92 × 10−7

QC10U Na-clinoptilolite/quartz 10 28.6 0.35 capped 3–10 2.04 × 10−7

QC20 Na-clinoptilolite/quartz 20 28.6 0.7 10−3.5 3–10 2.10 × 10−7

QC20U Na-clinoptilolite/quartz 20 28.6 0.7 No CO2 3–10 2.03 × 10−7
2-30 

Table 2.2.7.2-1. Summary of Np(V) Sorption Experiments on Alluvium and Related 

Minerals Using Actual or Simulated Groundwaters 

Experiment Substrate M/V (g/L) 

Np Conc. 

(M) PCO2 pH Solution 

AL1 alluvium, single depth* 16.7 7.9 × 10−7 10−3.5 single 19PB shallow string† 

AL2 alluvium, single depth* 16.7 5.5 × 106 10−3.5 single 19PB shallow string† 

AL3 alluvium, single depth* 16.7 7.8 × 10−7 10−3.5 single 19PB shallow string† 

AL4 alluvium, single depth* 3.3 7.7 × 10−7 10−3.5 6.5–9 19PB shallow string† 

AL5 alluvium, single depth* 3.3 7.8 × 10−7 10−3.5 single Simulated GW‡ 

AL6 alluvium, single depth* 3.3 7.8 × 10−7 10−3.5 single Simulated GW‡ 

without calcium 

C1 Na-clinoptilolite 3.3 8.0 × 10−7 10−3.5 single 19PB shallow string† 

M1 Na-montmorillonite 3.3 8.0 × 10−7 10−3.5 single 19PB shallow string† 

M2 Na-montmorillonite 3.3 7.8 × 10−7 10−3.5 6.5–9 19PB shallow string† 

M3 Na-montmorillonite 3.3 7.8 × 10−7 10−3.5 single Simulated GW‡ 

M4 Na-montmorillonite 3.3 7.8 × 10−7 10−3.5 single Simulated GW‡ 

without calcium 

M5 Na-montmorillonite 3.3 7.8 × 10−7 10−3.5 6.5–9 Simulated GW‡ 

M6 Na-montmorillonite 3.3 7.7 × 10−7 10−3.5 6.5–9 Simulated GW‡ 

without calcium 

M7 Na-montmorillonite 3.3 8.5 × 10−7 10−3.5 6.5–9 Simulated GW‡ 

CAL1 composite alluvium§ 3.3 7.8 × 10−7 10−3.5 single Simulated GW‡ 

CAL2 composite alluvium 3.3 7.8 × 10−7 10−3.5 single Simulated GW‡ 

without calcium 

CAL3 composite alluvium 3.3 7.7 × 10−7 10−3.5 6.5–9 Simulated GW‡ 

CAL4 composite alluvium 3.3 7.6 × 10−7 10−3.5 6.5–9 Simulated GW‡ 

without calcium 

CAL5 composite alluvium 3.3 8.3 × 10−7 10−3.5 6.5–9 Simulated GW‡ 

CAL6 composite alluvium 3.3 8.3 × 10−7 10−3.5 6.5–9 Simulated GW‡ 

without calcium 

CAL7 composite alluvium 3.3 8.4 × 10−7 10−3.5 4–9 Simulated GW‡ 

CAL8 composite alluvium 3.3 8.4 × 10−7 10−3.5 6.5–9 Simulated GW‡ 

without calcium 

CAL9 composite alluvium 3.3 8.2 × 10−7 10−3.5 4–9 Simulated GW‡ 

CAL10 composite alluvium 3.3 8.2 × 10−7 10−3.5 6.5–9 Simulated GW‡ 

without calcium 

CAL11 composite alluvium 3.3 8.5 × 10−7 10−3.5 6.5–9 Simulated GW‡ 

 composite alluvium 3.3 8.7 × 10−7 10−3.5 4–9 Simulated GW‡ 

 composite alluvium 3.3 8.7 × 10−7 10−3.5 4–9 Simulated GW‡ 

 composite alluvium 3.3 8.7 × 10−7 10−3.5 4–9 Simulated GW‡ 

 composite alluvium 3.3 8.7 × 10−7 10−3.5 4–9 Simulated GW‡ 

*Alluvium from Borehole NC-EWDP-19PB interval 123.8 to 124.2 m 

†Groundwater collected from NC-EWDP-19PB shallow string filtered through a 0.45 μm filter ‡

Simulated GW = Simulated groundwater with 19PB composition 

§Composite alluvium sample from 13 intervals listed in Table 3-1 
2-31 

Table 2.3-1. Best-Fit X-Ray Absorption Fine Structure Parameters for Uranyl Sorbed on 

Montmorillonite and Clinoptilolite 

Montmorillonite pH 3.3 Montmorillonite pH 6.3 

Shell CN R (Å) σ2

 (Å2) Shell CN R (Å) σ2 (Å2)

U-Oax 2 1.77 0.001 U-Oax 2 1.79 0.002

 

U-Oeq1 5.5 2.41 0.007 U-Oeq1 3.5 2.28 0.010

 

— — — — U-Oeq2 3.4 2.44 0.010

 

MS2 2.6 3.54 0.003 MS2 2.4 3.59 0.002

 

ΔE0 4.5 ΔE0 4.4 

Clinoptilolite pH 3.2 Clinoptilolite pH 6.4 

Shell CN* R (Å)† σ2

 (Å2)‡ Shell CN R (Å) σ2 (Å2)

 

U-Oax 2§ 1.78 0.001 U-Oax 2 1.79 0.001

 

U-Oeq1 1.4 2.21 0.002 U-Oeq1 2.0 2.28 0.003

 

U-Oeq2 4.4 2.42 0.004 U-Oeq2 3.1 2.45 0.005

 

(U-Oeq

(preliminary 

fit) 

4.8 2.45 0.008 

MS2 2.6 3.56 0.005 MS2 2.3 3.57 0.004

 

ΔE0¶ 4.2 ΔE0 5.1 

*Coordination numbers (CN) have errors of ± 20 percent

†Errors on interatomic distance, R, are ± 0.02 Å

‡Errors on Debye-Waller-type disorder parameters are ± 0.001 Å2

§Fixed at two for most fits, but refined to a value near two for test fits

2Four-legged multiple scattering path Oax1-U-Oax2-U

¶Global energy threshold varied during fitting
2-32 

Table 2.3-2. Comparison of X-Ray Absorption Fine Structure Data From This Study With 

Literature Values 

Sorbent pHeq Shell CN* R (Å)† σ2

 (Å2) 

Montmorillonite 3.3 U-Oax 2 1.77 0.001

(This study) U-Oeq 5.5 2.41 0.007

 

Montmorillonite‡ 3.2 U-Oax 2 1.79 0.003

 U-Oeq 5.4 2.43 0.010

 

Vermiculite§ 2.6 U-Oax 2.1 1.78 0.002

 U-Oeq 4.6 2.43 0.008

 

Hydrobiotite§ 2.6 U-Oax 2.1 1.78 0.002

 U-Oeq 4.8 2.43 0.008

Montmorillonite 6.3 U-Oax 2 1.79 0.002

(This study) U-Oeq1 3.5 2.28 0.010

 U-Oeq2 3.4 2.44 0.010

 

Montmorillonite‡ 6.4 U-Oax 2 1.77 0.002

 U-Oeq1 2.97 2.30 0.003

 U-Oeq2 2.72 2.48 0.003

 

Vermiculite§ 6.9 U-Oax 2.23 1.80 0.003

 U-Oeq1 3.09 2.28 0.012

 U-Oeq2 1.02 2.48 0.002

 

Hydrobiotite§ 7.1 U-Oax 2.10 1.79 0.001

 U-Oeq1 1.27 2.24 0.008

 U-Oeq2 3.09 2.42 0.006

*Coordination number

†Interatomic distance

‡Sylwester, E.R., E.A. Hudson, and P.G. Allen. “The Structure of Uranium(VI) Sorption Complexes on Silica, 

Alumina, and Montmorillonite.” Geochimica et Cosmochimica Acta. Vol. 64. pp. 2,431–2,438. 2000.

§Hudson, E.A., L.J. Terminello, B.E. Viani, M. Denecke, T. Reich, P.G. Allen, J.J. Bucher, D.K. Shuh, and 

N.M. Edelstein. “The Structure of U6+ Sorption Complexes on Vermiculite and Hydrobiotite.” Clays and Clay 

Minerals. Vol. 47. pp. 439–457. 1999.
3-1 

3 SORPTION MODELS 

3.1 Empirical Models 

Empirical equations relating dissolved radionuclide concentration to sorbed concentrations liquid 

phases can be generated for individual data sets through fitting curves and empirical 

coefficients to experimental sorption data. Because experimental sorption data are generally 

obtained at a single, constant temperature, these empirical equations are known as sorption 

“isotherms.” Because of their general simplicity, empirical radionuclide sorption models are 

typically the method used to represent radionuclide sorption in performance assessment 

models. Empirical models, however, are lumped parameter models that cannot be used to 

discriminate between different processes that contribute to radionuclide sorption. For this 

reason, they are generally unsuited to simulating rapidly changing systems, and are limited to 

the conditions investigated in the experiments from which the model parameters (sorption 

coefficients) are derived. Therefore, to evaluate the adequacy of an empirical approach, it 

is critical to have an understanding of the magnitude of changes anticipated in the system 

of interest. 

3.1.1 Linear Adsorption (Kd Approach) Isotherm 

As described previously, the simplest type of adsorption isotherm is a linear sorption coefficient, 

Kd (in mL/g or m3

/kg) 

K S

C

V

d = M





 









 



 (3.1-1) 

where S represents the amount of radionuclide sorbed {g/g [fl. oz]}, C represents the amount in 

solution (g/mL [oz/fl. oz]), V represents the solution volume (mL), and M represents the mass of 

solid {g [oz]}. The Kd is then used to represent the relative contaminant transport velocity 

through a retardation factor, Rf (unitless) (Freeze and Cherry, 1979), such that 

R g r o u n d w a t e r v el o ci t y

c o n t a mi n a n t v el o ci t y = 1 + K f d = 



 



 ρ

θ

b (3.1-2) 

where ρb = bulk density and θ = porosity. The lower the value of Kd, the lower the retardation 

factor, and the faster a reactive species migrates through the subsurface. For a nonadsorbing 

species, Kd = 0, Rf reduces to 1 and the species migrates at the flow velocity. 

The Kd is a lumped parameter and cannot be used by itself to discriminate the contributions of 

different sorption processes to contaminant retardation. An additional limiting characteristic of 

the Kd approach is the inability to recognize a maximum adsorption limit. In actuality, because 

there are a finite number of adsorption sites, adsorption will reach a practical upper limit. 

Additional uncertainty arises from the sensitivity of the parameter to aqueous chemical 

conditions, such as pH, alkalinity, or concentrations of complexing ligands, that may be 

encountered along a groundwater flow path (Kohler, et al., 1996; Davis, et al., 2004, 1998; 

Bethke and Brady, 2000). For example, the Kd for uranium(VI) adsorption on ferrihydrite at pH 8 

decreases by four orders of magnitude as the partial pressure of carbon dioxide gas, PCO2, 

increases from its value in air (0.032 percent) to 1 percent (Davis, et al., 2004). This is an 

important variation to understand, because the PCO2 in aquifers commonly reaches values of 
3-2 

1–5 percent, while most Kd values have been determined in laboratory experiments equilibrated 

with or exposed to air. Moreover, PCO2 often increases with transport after groundwater 

recharge, and this spatial/temporal trend in chemical conditions can greatly affect the 

retardation of contaminants. For these reasons, constant Kd models for adsorption do not 

adequately account for spatial variability in mineralogy and hydrochemistry along groundwater 

flow paths. 

3.1.2 Freundlich Adsorption Isotherm 

The Freundlich adsorption isotherm (Freundlich, 1926) is nonlinear and defined by 

the relationship 

S K CF r

n

= (3.1-3)

where S and C are defined as for Eq. (3.1-1) and KFr and n are empirical coefficients. For the 

special case where n = 1, Eqs. (3.1-1) and (3.1-3) are identical. If adsorption/concentration data 

can be represented by a Freundlich isotherm, a plot of log S versus log C should result in a 

straight line with a slope equal to n and an intercept of log KFr. As is the case for a linear 

adsorption isotherm, no maximum is reached upon uptake through adsorption. 

3.1.3 Langmuir Adsorption Isotherm 

In adapting the Langmuir adsorption isotherm (Langmuir, 1918) to solution chemistry, the idea 

of an upper limit to surface adsorption was introduced. Assuming that all adsorption sites are 

homogeneous, the general form for the Langmuir isotherm is 

S

K b C

1 + K

L a

L a = C (3.1-4)

where b is the maximum adsorption capacity of the substrate (g solute/g adsorbent) and KLa is a 

constant representing the strength with which the solute is bound to the substrate (l/meq). 

Values of b and KLa can be determined by plotting a variety of possible linearizations or by 

nonlinear regression analysis (Kinniburgh, 1986). 

The Langmuir isotherm accounts for the decrease in Kd values that occurs as an adsorbing 

surface becomes partially saturated with adsorbed species. The relationship is usually 

determined for a specific set of constant chemical and physical conditions while adsorbing ion 

concentration is varied. 

3.1.4 Parameters for Empirical Equilibrium Adsorption Models 

A variety of compilations of model-dependent adsorption parameters is available in the 

literature. Because of its simplicity, the largest number of adsorption parameter collections is 

available for the adsorption coefficient (Kd). In using different subsurface reactive transport 

computer codes, there are often default adsorption coefficients values in the input files. These 

values may or may not be linked to experimental data or site suitable conditions, and the user 

can subsequently revised them for a given simulation on a site-specific basis. 

A number of computer codes developed to assess radiological dose have internal or default 

sorption coefficients that the user can choose to implement in the absence of detailed 
3-3 

site-specific information. For example, the DandD code (Kennedy and Strenge, 1992), 

developed to screen scenarios and exposure pathways for decommissioning and license 

termination, provides default Kd values derived from various sources or methods. For example, 

if the radionuclide of interest is assumed to be mobile, a Kd value of 0.0 is assigned to maximize 

the migration rate. As possible, the experimental values from the compilation of Sheppard and 

Thibault (1990) were used, with the smallest (most conservative) value selected from the four 

soil types considered in the compilation. For C-14 in soil, data from Sheppard, et al. (1991) 

were used. When experimental data were not available, a correlation between Kd values and 

soil-to-plant concentration ratios and measured soil-to-plant concentration ratios was used 

(Thibault, et al., 1990). Similarly, the RESRAD dose assessment software (Yu, et al., 1993a,b) 

uses default Kd values selected from several sources available (Baes and Sharp, 1983; Nuclear 

Safety Associates, 1980; Isherwood, 1981; NRC, 1980; Gee, et al., 1980; Staley, et al., 1979). 

The MEPAS environmental analysis and dose assessment code (Strenge and Peterson, 1989; 

Whelan, et al., 1996) uses a modified approach to consider the effects of solution chemistry and 

mineralogy on radionuclide sorption. A MEPAS user can enter a value for pH and the total 

percentage of clay, organic matter, and iron and aluminum oxides for the geologic media. The 

software then accesses a list of default Kd values that contains up to nine different values for 

each contaminant. The available Kd values are organized into three pH categories (pH 5, 

pH 5–9, and pH 9). Within each pH category, the Kd values are further organized into three soil 

categories: the combined total percentage of clay, organic matter, and iron and aluminum 

oxides in three ranges (<10 percent, 10–30 percent, and 30 percent). 

A number of adsorption coefficient databases have been developed specifically to address 

radionuclide transport issues. Examples of Kd databases include the Sandia Sorption Data 

Management System (Siegel, et al., 1989), the Atomic Energy of Canada, Ltd. database 

(Thibault, et al., 1990), the SKI Project-90 database (Andersson, 1988), and the Nuclear Energy 

Agency adsorption database (Rüegger and Ticknor, 1992). Ames and Rai (1978) reviewed of 

available data and relevant chemistry for radionuclide interactions with soil and rock media. 

Based on literature review and onsite laboratory and field experiments, Looney, et al. (1987) 

have compiled estimates of Kd values applicable to the Savannah River Site at Aiken, South 

Carolina. A similar effort by Center for Nuclear Waste Regulatory Analyses (CNWRA®) staff 

developed Kd values to be used in evaluating U.S. Department of Energy (DOE) waste tank 

cleanup activities at the Savannah River Site and at Idaho National Laboratory (Prikryl and 

Pickett, 2007). Cantrell, et al., (2003) compiled Kd values and supporting data applicable to the 

DOE Hanford Site at Richland, Washington. 

McKinley and Scholtis (1991) compiled and compared radionuclide adsorption coefficient 

databases used in performance assessment. Although developed for the purposes of 

radionuclide transport, the database reviewed in this work covers nearly half of the periodic 

table of elements and includes information on adsorption onto buffers and backfills 

(concrete, bentonite, clay/rock mixes), a wide range of host rocks (e.g., crystalline, sediments), 

and surface soils. 

The U.S. Environmental Protection Agency (EPA) published a three-volume set of documents 

on understanding variation in Kd values (EPA, 2002; 1999a,b). Volume II (EPA, 1999b) 

contains a review of the geochemistry and Kd values for cadmium, cesium, chromium, lead, 

plutonium, radon, strontium, thorium, tritium (3

H), and uranium. Volume III (EPA, 2002) contains 

a review of the geochemistry and Kd values for americium, arsenic, curium, iodine, neptunium, 

radium, and technetium. 
3-4 

3.1.5 References (Section 3.1) 

Ames, L.L. and D. Rai. “Radionuclide Interactions With Rock and Soil Media. Vol. I. 

EPA 520/6-78-007-a. Las Vegas, Nevada: EPA 1978. 

Andersson, K. “SKI Project–90: Chemical Data.” SKI Technical Report 91:21. 

Stockholm, Sweden: Swedish Nuclear Power Inspectorate. 1988. 

Baes, C.F. and R.D. Sharp. “A Proposal For Estimation of Soil Leaching and Leaching 

Constants for Use in Assessment Models.” Journal of Environmental Quality. Vol. 12. 

pp. 17–28. 1983. 

Bethke, C.M. and P.V. Brady. “How the Kd Approach Undermines Ground Water Cleanup.” 

Ground Water. Vol. 38. pp. 435–443. 2000. 

Cantrell, K.J., R.J. Serne, and G.L. Last. “Hanford Contaminant Distribution Coefficient 

Database and Users Guide.” PNNL–13895. Rev. 1. Richland, Washington: Pacific Northwest 

National Laboratory. 2003. 

Davis, J.A., D.E. Meece, M. Kohler, and G.P. Curtis. “Approaches to Surface Complexation 

Modeling of Uranium(VI) Adsorption on Aquifer Sediments.” Geochimica et Cosmochimica 

Acta. Vol. 68. pp. 3,621–341. 2004. 

Davis, J.A., J.A. Coston, D.B. Kent, and C.C. Fuller. “Application of the Surface Complexation 

Concept to Complex Mineral Assemblages.” Environmental Science & Technology. Vol. 32. 

pp. 2,820–2,828. 1998. 

EPA. “Understanding Variation in Partition Coefficient, Kd, Values, Vol. III: Review of 

Geochemistry and Available Kd Values for Americium, Arsenic, Curium, Iodine, Neptunium, 

Radium, and Technetium.” EPA 402–R–99–004C. Washington, DC: EPA. 2002. 

EPA. “Understanding Variation in Partition Coefficient, Kd, Values, Vol. I: The Kd Model, 

Methods of Measurement, and Application of Chemical Reaction Codes.” 

EPA 402–R–99–004A. Washington, DC: EPA 1999a. 

EPA. “Understanding Variation in Partition Coefficient, Kd, Values, Vol. II: Review of 

Geochemistry and Available Kd Values for Cadmium, Cesium, Chromium, Lead, Plutonium, 

Radon, Strontium, Thorium, Tritium (3

H), and Uranium.” EPA 402–R–99–004B. 

Washington, DC: EPA. 1999b. 

Freeze, R.A. and J.A. Cherry. Groundwater. Englewood Cliffs, New Jersey: Prentice-Hall, Inc. 

1979. 

Freundlich, H. Colloid and Capillary Chemistry. London, England: Methuen. 1926. 

Gee, G.W., A.C. Campbell, D.R. Sherwood, R.G. Strickert, and S.J. Phillips. NUREG/CR–1494, 

PNL–3381, “Interaction of Uranium Mill Tailings Leachate with Soils and Clay Liners.” 

Washington, DC: Office of Nuclear Regulatory Research. NRC. 1980. 

Isherwood, D. NUREG/CR–0912, “Geoscience Data Base Handbook for Modeling a Nuclear 

Waste Repository.” Vols. 1 and 2. Washington, DC: NRC. 1981. 
3-5 

Kennedy, J.D. and D.L. Strenge. NUREG/CR–5512, PNL–7994, “Residual Radioactive 

Contamination from Decommissioning: Technical Basis for Translating Contamination 

Levels to Annual Total Effective Dose Equivalent.” Washington, DC: NRC. 

<http://techconf.llnl.gov/radcri/java.html > 1992. 

Kinniburgh, D.G. “General Purpose Adsorption Isotherms.” Environmental Science & 

Technology. Vol. 20. pp. 895–904. 1986. 

Kohler, M., G.P. Curtis, D.B. Kent, and J.A. Davis. “Experimental Investigation and Modeling of 

Uranium(VI) Transport Under Variable Chemical Conditions.” Water Resource Research. 

Vol. 32. pp. 3,539–3,551. 1996. 

Langmuir, D. “The Adsorption of Gases on Plane Surfaces of Glass, Mica, and Platinum.” 

Journal of the American Chemical Society. Vol. 40. pp. 1,361–1,403. 1918. 

Looney, B.B., M.W. Grant, and C.M. King. “Estimation of Geochemical Parameters for 

Assessing Subsurface Transport at the Savannah River Plant.” DPST–85–904. 

Aiken, South Carolina: Savannah River Laboratory. 1987. 

McKinley, I.G. and A. Scholtis. “Compilation and Comparison of Radionuclide Sorption 

Databases Used in Recent Performance Assessments.” Radionuclide Sorption From the Safety 

Evaluation Perspective, Proceedings of an NEA Workshop, Interlaken, Switzerland, 

October 16–18, 1991. Paris, France: Nuclear Energy Agency. pp. 21–55. 1991. 

NRC. NUREG–0706, “Final Generic Environmental Impact Statement on Uranium 

Milling—Vol. 1: Summary and Text; Vol. 2: Appendices A–F; Vol. 3: Appendices G–V 

Washington, DC: NRC, Office of Nuclear Material Safety and Safeguards. 1980. 

Nuclear Safety Associates, Inc. “Comparison of Alternatives for Long-Term Management of 

High-Level Radioactive Waste at the Western New York Nuclear Service Center.” 

Appendix IIIC. Bethesda, Maryland: Nuclear Safety Associates, Inc. 1980 

Prikryl, J.D. and D.A. Pickett. “Recommended Site-Specific Sorption Coefficients for Reviewing 

Non-High-Level Waste Determinations at the Savannah River Site and Idaho National 

Laboratory.” San Antonio, Texas: CNWRA. 2007. 

Rüegger, B. and K.V. Ticknor. “The NEA Sorption Data Base (SDB): Radionuclide Sorption 

from the Safety Evaluation Perspective.” Proceedings of a Nuclear Energy Agency Workshop. 

Interlaken, Switzerland, October 16–18, 1991. Paris, France: Organization for Economic 

Co-Operation and Development, Nuclear Energy Agency. pp. 57–78. 1992. 

Sheppard, M.L., S.C. Sheppard, and B.D. Amiro. “Mobility and Plant Uptake of Inorganic 14C 

and 14C-Labeled PCB in Soils of High and Low Retention.” Health Physics. Vol. 61. 

pp. 481–492. 1991 

Sheppard, M.L. and D.H. Thibault. “Default Soil Solid/Liquid Partition Coefficients, Kds, for Four 

Major Soil Types: A Compendium.” Health Physics. Vol. 59. pp. 471–478. 1990. 
3-6 

Siegel, M.D., R. Rechard, K.L. Erickson, J.O. Leckie, D.B. Kent, D.A. Grover, S. Philips, 

R. Guzowski, and S. Faith. “Progress in Development of a Methodology for 

Geochemical Sensitivity Analysis for Performance Assessment—Volume 2: Speciation, 

Sorption, and Transport in Fractured Media.” Project NRC FIN A1756. Albuquerque, New 

Mexico: Sandia National Laboratories. 1989. 

Staley, G.B., G.P. Turi, and D.L. Schreiber. “Radionuclide Migration from Low-Level Waste: A 

Generic Overview.” Management of Low-Level Radioactive Waste. M.W. Carter, et al., ed. 

New York City, New York: Pergamon Press. pp. 1,041–1,072. 1979. 

Strenge, D.L. and S.R. Peterson. “Chemical Data Bases for Multimedia Environmental Pollutant 

Assessment System (MEPAS).” Version 1. PNL-7145. Richland, Washington: 

Pacific Northwest Laboratory. 1989. 

Thibault, D.H., M.I. Sheppard, and P.A. Smith. “A Critical Compilation and Review of Default 

Soil Solid/Liquid Partition Coefficients, Kd, for Use in Environmental Assessments.” 

AECL-100125. Atomic Energy of Canada Limited. Whiteshell Nuclear Research Establishment. 

Pinawa, Manitoba, Canada: 1990. 

Whelan, G., J.P. McDonald, and C. Sato. “Multimedia Environmental Pollutant 

Assessment System (MEPAS): Groundwater Pathway Formulations.” PNNL–10907. 

Richland, Washington: Pacific Northwest National Laboratory. 1996. 

Yu, C., A.J. Zielen, J.-J. Cheng, Y.C. Yuan, L.G. Jones, D.J. LePoire, Y.Y. Wang, C. Loureiro, 

E. Gnanapragasam, E. Faillace, A. Wallo, W.A. Williams, and H. Peterson. “Manual for 

Implementing Residual Radioactive Material Guidelines Using RESRAD, Version 5.0.” 

ANL/EAD/LD–2. Argonne, Illinois: Argonne National Laboratory. 

<http://web.ead.anl.gov/resrad/> 1993a. 

Yu, C., C. Loureiro, J.-J. Cheng, L.G. Jones, Y.Y. Wang, Y.P. Cia, and E. Faillace. “Data 

Collection Handbook to Support Modeling the Impacts of Radioactive Material in Soil.” 

ANL/EAIS–8. Argonne, Illinois: Argonne National Laboratory. 1993b. 

3.2 Experimental and Modeling Study of Multicomponent 

Ion-Exchange Equilibria on Zeolite Minerals 

3.2.1 Introduction 

A major technical consideration in evaluating Yucca Mountain, Nevada, as a potential repository 

site for high-level wastes is the presence of thick lateral zones of diagenetically altered, 

zeolite-rich (clinoptilolite, heulandites, and mordenite) rhyolitic tuffs (Broxton, et al., 1987, 1986) 

that could serve as barriers to radionuclide migration to the accessible environment. Zeolites 

are crystalline, hydrated aluminosilicates of alkali and alkaline earth cations characterized by an 

ability to hydrate/dehydrate reversibly and to exchange some of their constituent cations with 

aqueous solutions, both without a major change in structure. Due to the favorable ion-exchange 

selectivity of zeolites for certain cations, such as Cs+ and Sr2+, naturally occurring zeolites have 

been studied for potential use in the treatment of nuclear wastewaters (Baxter and Berghauser, 

1986; Howden and Pilot, 1984; Pansini, 1996; Robinson, et al., 1995) and in the remediation of 

sites contaminated with fission products such as Sr-90, Cs-135, and Cs-137 (Leppert, 1988; 

Valcke, et al., 1997a,b). Measured Yucca Mountain zeolite mineral compositions show lateral 

and vertical variations, [e.g., sodium-potassium-bearing and increasingly sodium-rich with depth 
3-7 

on the western side and calcium-potassium-bearing and increasingly calcium-rich with depth on 

the eastern side of Yucca Mountain (Broxton, et al., 1987, 1986)]. Because zeolite 

ion-exchange properties depend on factors including solid and aqueous phase compositions 

as well as aqueous solution concentrations, spatial variations in zeolite composition that 

have been observed at Yucca Mountain and natural or repository-induced changes in 

groundwater chemistry may influence the effectiveness of the zeolite minerals to retard 

radionuclide migration. 

To provide a thermodynamic basis for understanding zeolite–water interactions at Yucca 

Mountain and other geochemical systems, ion-exchange experiments were conducted between 

aqueous solutions and the zeolite mineral clinoptilolite. Clinoptilolite was used in the 

experiments because it is the predominant zeolite mineral present in altered pyroclastic and 

volcaniclastic rocks (Mumpton, 1978; Mumpton and Ormsby, 1976), particularly those present at 

Yucca Mountain (Broxton, et al., 1987, 1986). The experiments were conducted using binary, 

ternary, and quaternary combinations of the cations Na+

, K+, Cs+, Sr2+, or Ca2+. In most cases, 

multiple solution concentrations were studied. A general thermodynamic model also was 

applied to enable predictions of ion-exchange equilibria in multicomponent systems typical of 

natural systems like Yucca Mountain. 

The experiments and thermodynamic models are summarized in the following sections. More 

detailed descriptions are provided in Pabalan (1994, 1991), Pabalan and Bertetti (2001, 1999, 

1994), and Bertetti and Pabalan (2003). 

3.2.2 Ion-Exchange Experiments 

Prior to the isotherm experiments, clinoptilolite material was prepared by crushing and purifying 

a sample of clinoptilolite-rich tuff from Death Valley Junction, California (Pabalan, 1994). The 

material was crushed using a mortar and pestle and then sieved to isolate and obtain size 

fractions (100–200 and 200–325 mesh, equivalent to 0.150–0.074 and 0.074–0.045 mm in 

diameter, respectively) suitable for use in the experiments. The 200–325 mesh fraction 

(indicating material that passed through the 200-mesh screen and was retained on the 

325-mesh screen) was selected for use in this study because a greater mass of this size 

fraction was available following crushing. The clinoptilolite powder was cleaned of residual fines 

by ultrasonication and further purified following the methods described in Pabalan (1994). 

Homoionic forms of Na+

, K+, and Ca2+ clinoptilolite were prepared by reacting the purified zeolite 

with concentrated solutions (3 M) of Na+

, K+, or Ca2+ (as chloride). The newly prepared 

sodium-form, potassium-form, and calcium-form clinoptilolite powders were dried at 65 °C 

[149 °F] overnight, then placed in a desiccator over a saturated NaCl solution and allowed to 

come to equilibrium with the water vapor in the desiccator prior to use. 

The clinoptilolite powders used in the study (original material, purified form, and homoionic 

forms) were characterized for mineralogy using x-ray diffractometry and for chemistry using 

x-ray fluorescence spectrometry. The homoionic form chemical data were used to calculate the 

cation-exchange capacity of each form. 

3.2.2.1 Binary Ion-Exchange Experiments 

The binary ion-exchange experiments were conducted by reacting weighed amounts of 

homoionic clinoptilolite with known volumes of solution mixtures of the cations of interest. In 

general, a series of reference, or starting, solutions were prepared at various ratios of each 
3-8 

cation using chloride salts and doubly deionized, reagent-grade (>18MΩ) water. Total 

solution concentrations of the mixtures were held constant (e.g., 0.005, 0.05, or 0.5 N) while 

the ratio (equivalent cationic fraction) of each cation was varied according to the individual 

experimental design. Reference solutions were then dispensed and mixed with a mass of 

clinoptilolite powder to start each experiment. The zeolite mass to solution volume ratios and 

the aqueous mixture compositions used in the experiments were optimized in an attempt to 

yield (i) significant differences in the initial and final concentrations of the cations in solution and 

(ii) a relatively evenly spaced distribution of points along the ion-exchange isotherm. The 

experimental mixtures (clinoptilolite + salt solution) were contained in capped polypropylene 

bottles (of various volumes depending on the experiment) and were agitated on a gyratory 

shaker for at least 1 week. Experiments were maintained at 24 ± 1 °C [75.2 ± 1.8 °F]. 

At the end of each experiment, reference and experimental solution compositions were 

measured using one or multiple analytical techniques as required. The difference between 

initial experimental solution concentrations (determined by the reference solution values) and 

final equilibrium concentrations was used to calculate the changes in zeolite composition. 

Isotherms, representing the ion-exchange equilibrium of the system at the specified 

concentration and temperature, were plotted. The isotherm data were then used, in conjunction 

with the calculated cation exchange capacity and the thermodynamic model, to generate a 

thermodynamic description of the ion-exchange system. The binary systems studied are 

summarized in Table 3.2-1. 

The Na+

, K+, and Ca2+ concentrations in the reference and experimental solutions were 

measured using ion-selective electrodes, flame atomic absorption spectroscopy, or inductively 

coupled atomic emission spectrometry. Cs+

 and Sr2+ concentrations were determined indirectly 

using Sr-90 and Cs-137 as tracers. These radioisotopes provide a sensitive method of 

detection and can reduce the uncertainty in measured cation concentrations. In general, the 

radiotracers were used by adding aliquots to reference solutions, which were then used to mix 

the individual experimental solutions. The initial activity of the reference solutions and the final 

activity of the experimental solutions at equilibrium were used as surrogates for the 

concentration of the cation of interest (Sr2+ or Cs+

). The activity of the radioisotopes was 

measured using a liquid scintillation analyzer (Packard 3100TR) in beta counting mode. 

3.2.2.2 Ternary Ion-Exchange Experiments 

Ternary ion-exchange experiments were conducted following the general procedure 

Fletcher, et al. (1984) developed. This procedure was based on a systematic approach to the 

initial solution composition and distribution as plotted on a ternary diagram. The systematic 

approach provided a good statistical coverage of the entire range of conditions and thus a more 

robust examination of model performance. 

Reference solutions were made using cation chloride salts and deionized water in the same 

manner as the binary experiments. The reference solutions were then mixed with a mass of 

homoionic zeolite and allowed to react for at least 1 week on a gyratory shaker. Reference 

(initial) and equilibrium experimental solutions (final) were then sampled for chemical analysis. 

Ternary system experiments are summarized in Table 3.2-2. 

Solid zeolite samples from some of the Na+

/K+/Ca2+, Na+/K+/Sr2+, Na+/K+/Cs+, and 

Na+

/K+/Ca2+/Sr2+ experimental systems were also taken and submitted for chemical analyses. 
3-9 

3.2.2.3 Quaternary Ion-Exchange Experiment 

The quaternary ion-exchange experiment was conducted in the same manner as the ternary 

system experiments. A systematic experimental design was used, and a known mass of 

homoionic zeolite powder was reacted with solutions of varying cation composition, but at a 

constant ionic strength. Samples of the reference and experimental solutions as well as the 

equilibrium solid were taken for chemical analysis. The quaternary system experiment is 

summarized in Table 3.2-2. 

3.2.3 Thermodynamic Model 

The basic thermodynamic formulations for ion exchange are based on principles developed long 

ago by researchers on inorganic exchange materials, especially clays (Gaines, et al., 1953; 

Gapon, 1933; Kielland, 1935; Vanselow, 1932). These formulations are still widely used in 

current ion-exchange literature, irrespective of the nature of the exchanger under study. 

3.2.3.1 Ion-Exchange Isotherm 

For a binary ion exchange involving cations AzA+

and BzB+ , the basic reaction may be written as 

z A + z BL z B + zB ALz

z +

A z A z +B A

B

B

A ⇔ (3.2-1)

where zA+ and zB+ are the valences of the respective cations and L is defined as a portion of 

zeolite framework holding unit negative charge. Anions are also present in the aqueous solution 

and maintain electroneutrality in that phase. 

The binary exchange equilibrium can be described conveniently by an ion-exchange isotherm, 

which is a plot of the equilibrium concentration of an exchanging ion in solution against the 

equilibrium concentration of that same ion in the zeolite at constant temperature and solution 

concentration. The isotherm is usually plotted in terms of the equivalent cation fraction of the 

ion in solution against that in the solid (Dyer, et al., 1981). The equivalent cationic fractions 

ofAzA+ andBzB+ ( EA and EB , respectively) in the zeolite phase are defined as 

where nA and nB are the number of moles of ionsAzA+ and B , zB+ respectively, in the zeolite. 

Equivalent cationic fractions in the aqueous solution can be defined similarly as 

where nA and nB are the number of moles of AzA+and BzB+ , respectively, BzB+ in the 

aqueous phase. 

z n + z n

z n ; E = z n + z n

z n E = A A B B

B B

B

A A B B

A A

A (3.2-2)

z n + z n

z n ; E = z n + z n

z n E = A A B B

B B

B

A A B B

A A

A (3.2-3)
3-10 

The ion-exchange isotherm can then be plotted from the equilibrium values of EA and EA

(or EB and EB ). For the binary exchange reaction shown in Eq. (3.2-1) 

Thus, the isotherm fully defines the equilibrium at a specified temperature and 

solution concentration. 

3.2.3.2 Thermodynamic Equilibrium Constant 

The thermodynamic equilibrium constant, K(A,B), for Eq. (3-1) is given by 

where a represents activities of the aqueous species and a represents activities of the zeolite 

components. The equilibrium constant is a measure of the relative affinity between the zeolite 

and the two cations involved in the exchange. The zeolite may be considered as a solid 

solution of two components ALzA and BLzB (Barrer and Klinowski, 1977; Ekedahl, et al., 1950; 

Freeman, 1961), where LzA is the amount of anionic framework associated with an AzA+ ion and 

carrying anionic charge zA− and LzB is the amount of framework associated with BzB+ and 

carrying anionic charge zB−. The number of moles of zA AL and BLzB are then respectively equal 

to the total number of moles nA and nB of ions AzA + and BzB+ in the zeolite, and solid phase 

compositions can be described in terms of cationic mole fractions of AzA+ and BzB+ in the zeolite 

Eq. (3.2-5) may be expanded to give 

or, 

EB =1-EA; EB =1-EA (3.2-4) 

(a ) ( a )

( a ) (a ) K = z B z A

z B z A

(A,B) B A

B A

(3.2-5) 

n +n

n ; X =

n +n

n

X =

A B

B

B

A B

A

A (3.2-6) 

( )

( )

(f )

(f )

(X ) (M )

(X ) (M ) K = z

A

z

B

z B

z A

z A z

B

z B z

A

(A,B) B

A

A

B

A B

B A

γ

γ × × (3.2-7)

(f )

(f ) K =K z B

z A

(A,B) v(A,B) A

B

× (3.2-8)
3-11 

where Kv(A,B) is the Vanselow corrected selectivity coefficient (Townsend, 1986; Vanselow, 

1932) defined by 

and MA and MB are the molarities of AzA+ and BzB+ in the aqueous phase. The quantities γA

and γB are single-ion activity coefficients for the aqueous cations and account for nonideal 

behavior in the aqueous phase. The quantities fA and fB are rational (i.e., in terms of mole 

fractions) activity coefficients for the zeolite components and account for nonideality in the 

zeolite phase. 

It is necessary to define the standard states of the various components to allow the evaluation 

of K(A,B), fA, and fB from experimental data using the Gibbs-Duhem relation. For the aqueous 

electrolyte solution external to the zeolite phase, the usual standard state is that of a 

hypothetical, one molar solution referenced to infinite dilution. For the exchanger phase, it has 

been normal practice to make the standard state for each exchanging cation the appropriate 

homoionic form of the zeolite in equilibrium with an infinitely dilute solution of the same cation 

(Gaines and Thomas, 1953; Sposito, 1981). With that standard state, the criterion for ideal 

behavior in the zeolite solid solution is that ai =Xifor all Xi . 

3.2.3.3 Activity Coefficients of Aqueous Ions 

An evaluation of equilibrium constants and zeolite phase activity coefficients from experimental 

data involves activity correction for the aqueous phase. Ion-exchange studies (Fletcher and 

Townsend, 1985) demonstrated the importance of correctly evaluating aqueous solution activity 

coefficients for accurate interpretation of exchange equilibria, particularly on systems with mixed 

background anions. In previous CNWRA studies (Pabalan, 1994; Pabalan and Bertetti, 1999, 

1994, 2001), the ion-interaction model Pitzer developed (1991, 1987, and 1973) was used to 

calculate the activity coefficients of aqueous ions. However, other aqueous activity coefficient 

models are equally useful. In the work summarized in this report, activity coefficients of 

aqueous ions were calculated using the B-dot equation of Helgeson (1969). The model is valid 

to an ionic strength of about 1 molal, which covers the range of most ion-exchange studies, and 

has been developed to span a wide range of temperatures {up to 300 °C [572 °F]}. The B-dot 

equation was used in this study to permit future use of the ion-exchange model with 

geochemical codes such as EQ3/6 (Wolery, 1992) and Geochemist’s Workbench 

(Bethke, 1996). 

3.2.3.4 Activity Coefficients of Zeolite Components 

Various models have been proposed to represent the activity coefficients of exchangeable ions 

or solid solutions (Chu and Sposito, 1981; Elprince and Babcock, 1975; Elprince, et al., 1980; 

Grant and Sparks, 1989; Mehablia, et al., 1996; Morgan, et al., 1995; Pabalan, 1994). Two 

models that have been used in ion-exchange studies are the Margules and Wilson equations. 

These equations have been widely applied to describe nonideal behavior in both solid and liquid 

solutions (Pitzer, 1995). 

Margules Model

The Margules model has been used successfully in studies of ion-exchange equilibria involving 

zeolite minerals (Pabalan, 1994; Shibue, 1998). In this model, the molar excess Gibbs energy 

( )

( )

(X ) (M )

(X ) (M ) K = z

A

z

B

A z

B

z B z

A

v(A,B) B

A

A ZB

B A

γ

γ × (3.2-9) 
3-12 

of mixing, GE , for a zeolite solid solution with two-components, ALzA and BLzB , is represented 

by the equation 

where WA and WB are empirical parameters that are functions only of temperature and pressure. 

From Eq. (3-9) and the Gibbs-Duhem relation, the zeolite activity coefficients, fA and fB, can be 

expressed in terms of WA and WB as 

For solid solutions with three or more components, analogous expressions can be derived 

(Grant and Sparks, 1989; Mukhopadhyay, et al., 1993). 

The Vanselow selectivity coefficient, Kv(A,B), can then be represented by 

Values of K(A,B), WA, and WB can be derived by nonlinear regression of Eq. (3-12) to isotherm 

data. If the zeolite phase behaves ideally, fA = fB = 1 and gex = 0 for all values of XA and XB , 

and K(A,B) = Kv(A,B). Values of WA and WB are zero for ideal solid solutions. 

Wilson Model

The Wilson model also has been applied successfully to studies of ion-exchange equilibria 

(e.g., de Lucas, et al., 1992; Elprince and Babcock, 1975; Ioannidis, et al., 2000; 

Mehablia, et al., 1996; Shallcross, et al., 1988; Shibue, 1999). For a two-component zeolite 

solid solution, the molar excess Gibbs energy is taken to be 

where ΛAB and ΛBA are empirical parameters. In this model, the measure of nonideality is the 

departure of the parameters from 1.0. For a solid solution that behaves ideally, ΛAB = ΛBA = 1.0. 

The corresponding activity coefficients for the binary solid solution are 

and 

= X X (X W + X W ) RT

G

A B B A A B

E

(3.2-10)

= X [W +2X (W - W )] f ln f = X [W +2X (W - W )]; ln B B A B

2

A A B A B A

2

A B (3.2-11)

lnK =lnK + z X [W +2X (W - W )]- z X [W +2X (WB - WA)] A A

2

B B A B B B

2

v(A,B) (A,B) A A (3.2-12)

= - X ln (X + X ) - X ln (X + X ) RT

G

BA A B A A AB B B

E

Λ Λ (3.2-13)

















Λ

Λ

Λ

Λ Λ

X + X

-

X + X

ln f = -ln(X + X ) + X

BA A B

BA

A AB B

AB

A A AB B B (3.2-14)

















Λ

Λ

Λ

Λ Λ

X + X

-

X + X

ln f = -ln(X + X )+ X

AB B A

AB

B BA A

BA

B B BA A A (3.2-15)
3-13 

In a manner similar to the Margules model, the Vanselow selectivity coefficient, KV(A,B), can then 

be represented by 

Eqs (3.2-14) and (3.2-15) can be substituted for ln fA and ln fB, respectively, in Eq. (3.2-16) to 

create an expression analogous to Eq. (3.2-12), where ln KV(A,B), ΛAB, and ΛBA can be derived by 

nonlinear regression to isotherm data. For multicomponent systems, the Wilson equation can 

be generalized to 

and 

where Λii, Λjj, Λkk, and so on are equal to 1.0 and the other Λij are just the binary parameters. 

The absence of parameters beyond the binary terms makes the Wilson model attractive for 

application to ternary or more complex mixtures. However, Pitzer (1995) points out that 

interactions of three different species in a mixture do occur, and a provision for their 

representation is needed when they are significant. 

3.2.3.5 Modeling 

To develop a more uniform approach to modeling ion-exchange reactions in zeolite, the two 

thermodynamic models described in the preceding section used to represent activity coefficients 

of exchangeable ions in solid solutions were evaluated for use in interpreting and predicting 

experimental results. Both equations have been used successfully in studies of ion-exchange 

equilibria, and both rely on empirical parameters to represent aspects of the solid solution 

behavior. The Margules model has been exclusively used in previous CNWRA ion-exchange 

studies. However, a limitation of the Margules approach is the need to derive parameters to 

represent ternary ion-interactions, in addition to those derived from binary ion-exchange 

experiments. The Wilson approach, on the other hand, is formulated such that parameters 

developed for binary systems can be used to represent ternary system behavior, without the 

need for additional parameters. 

Initially, an algebraic expression representing the binary form of Wilson model [Eq. (3.2-16)] was 

derived for incorporation into a curve-fitting algorithm using the software TableCurve Version 3.0 

(Jandel Scientific). Confirmatory testing of the curve-fitting algorithm was conducted to test for 

consistency and for capability of the algorithm to reproduce thermodynamic parameters from 

previous experimental data. Three parameters can be directly obtained from the nonlinear fit 

the equilibrium constant [K(A,B)] of the reaction {often expressed as ln [K(A,B)]} and the two 

empirical parameters, ΛAB and ΛBA. The empirical parameters represent nonideality of the 

lnKv(A,B)=lnK(A,B)+ zAln fB – zBln fA (3.2-16)















 = − Λ = =

m

j 1

ij j

m

i 1

i

E

X ln X

RT

G

(3.2-17)





























Λ

Λ

















Λ



 

X

X =1 – ln X – f ln

j ij

m

j=1

ik i m

i=1

kj j

m

j=1

k (3.2-18) 
3-14 

exchange and interaction of the exchanging cations. Interestingly, the obtained fit parameters 

are highly sensitive to the weighting used for each data set. Following testing, a rather 

extensive literature search was performed to gather appropriate ion-exchange data for systems 

of interest, including those not directly included in the experimental plan of this study. The 

Wilson model was applied to several experimental data sets in several binary ion-exchange 

systems, and the model was compared to Margules model results from the same systems. 

3.2.4 Results And Discussion 

3.2.4.1 Binary Ion-exchange Results 

Several important binary ion-exchange experiments were conducted. These experiments were 

designed to confirm results obtained from the literature and to provide additional data for 

development of thermodynamic parameters. The experimental systems studied 

included Na+

/Cs+, K+/Na+, K+/Cs+, K+/Sr2+, K+/Ca2+, and Ca2+/Sr2+, and the data are plotted in 

Figure 3.2-1. The error bars shown in each figure were calculated using the equations 

presented in Pabalan (1994). For each binary system, isotherm points were calculated from the 

initial and final concentrations of both cations participating in the ion-exchange reaction. In 

general, the isotherm points calculated independently from the concentrations of the two cations 

agree very well. The disagreements for some isotherm points shown in Figure 3.2-1(f) for the 

Sr2+/Ca2+ system are probably related to analytical problems. 

3.2.4.2 Comparison of Margules and Wilson Models 

The derived parameters for the Margules and Wilson models are provided in Table 3.2-3. 

Isotherms calculated using the derived Wilson parameters for each system are shown in 

Figures 3.2-2 to 3.2-5, which also plot ion-exchange data from this study and from the 

literature.1

 The regression results indicate that (i) the equilibrium constants (listed in the 

Table 3.2-3 as ln K values) derived from the same set of data using either model are very close 

(exceptions are found where the isotherm points do not cover a wide range of solution 

equivalent mole fraction), (ii) the ln Ks derived from experimental data at different solution 

concentrations (i.e., from previous experiments conducted at CNWRA) vary slightly, (iii) the 

Margules and Wilson empirical parameters can vary widely depending on the data used in the 

regression, and (iv) the Wilson model “behaves” better than the Margules model (i.e., the Wilson 

model is not as sensitive to the lack of data at the extreme ends of the isotherm) (the Margules 

model tends to become unstable where limited data are available). 

3.2.4.3 Results of Ternary and Quaternary Ion-Exchange Experiments 

Ternary ion-exchange experiments were conducted on three systems: Na+

/K+/Cs+, 

Na+

/Ca2+/K+, and Na+/K+/Sr2+. The first system involves monovalent cations only, whereas the 

latter two involve heterovalent exchange reactions. All three systems were studied at 

concentrations of 0.05 N and 0.005 N. A quaternary ion-exchange experiment studying the 

Na+

/K+/Ca2+/Sr2+ (0.05 N) system was also conducted and completed. Not all systems were 

analyzed for the equilibrium zeolite compositions. The systems in which solid phase samples 

were separated after equilibration with the aqueous solution and analyzed are the 0.005 N 

 

1

Isotherms calculated using the Wilson and Margules models are similar. For clarity, only isotherms calculated using 

the Wilson models are plotted. 
3-15 

Na+

/K+/Ca2+, 0.05 N Na+/K+/Ca2+, 0.05 N Na+/K+/Sr2+, and 0.05 N Na+/K+/Ca2+/Sr2+ systems. 

Solid samples from the 0.005 N Na+

/K+/Cs+ system were also taken and analyzed using 

inductively coupled plasma atomic emission spectrometry, but due to an error in specifying 

the required analytical protocol, the Cs+

 concentration in the zeolite samples was not 

analyzed—only the Na+

 and K+ concentrations were analyzed. 

Na+

/K+/Ca2+ (0.005 N)

The initial and equilibrium solution compositions in the 0.005 N Na+

/K+/Ca2+ system are shown 

in Figure 3.2-6(a). The high selectivity of clinoptilolite for K+

 relative to Na+ and for Ca2+ relative 

to Na+

 is reflected in the enrichment of Na+ in the aqueous phase. The preference of 

clinoptilolite for K+

 and Ca2+ relative to Na+ is also demonstrated in Figure 3.2-6(b), which 

compares the equilibrium zeolite composition with the equilibrium solution composition. Note 

that the experiments used a homoionic Na form of clinoptilolite, which would plot at the ENa

corner of the diagram. 

Figure 3.2-6(b) also compares the measured equilibrium solution composition (red circles) and 

the solution compositions (yellow circles) predicted from the thermodynamic model, using as 

input values the measured zeolite composition. Considering the uncertainties in analytical data, 

the agreement between measured and predicted values is very good. 

Na+

/K+/Ca2+ (0.05 N)

The initial and equilibrium solution compositions in the 0.05 N Na+

/K+/Ca2+ system are shown 

in Figure 3.2-7(a). The results also indicate enrichment of Na+

 in the aqueous phase, 

although it was not as strong as that observed at 0.005 N [Figure 3.2-6(a)]. On the other hand, 

Figure 3.2-7(b) illustrates the much stronger selectivity of clinoptilolite for K+

 relative to both Na+

and Ca2+. The equilibrium zeolite compositions plot mostly toward the EK, indicating that the 

initially homoionic Na-clinoptilolite has become mostly K-rich. 

Figure 3.2-7(b) also compares the measured equilibrium solution composition (red circles) and 

the solution compositions (yellow circles) predicted from the thermodynamic model, using as 

input values the measured zeolite composition. The agreement between measured and 

predicted values is good considering the uncertainties in analytical data. 

Na+

/K+/Sr2+ (0.005 N)

The initial and equilibrium solution compositions in the 0.005 N Na+/K+/Sr2+ system are shown in 

Figure 3.2-8(a). The high selectivity of clinoptilolite for the K+

 relative to Na+ and for Sr2+ relative 

to Na+

 is reflected in the enrichment of Na+ in the aqueous phase. No zeolite samples were 

taken for measurement of equilibrium composition. The zeolite compositions plotted in 

Figure 3.2-8(b) were estimated based on the initial and final concentrations of Na+

, K+, and Sr2+

in the aqueous phase. Figure 3.2-8(b) illustrates the preference of clinoptilolite for K+

 and Sr2+

relative to Na+

. Note that the experiment used a homoionic Na form of clinoptilolite, which 

would plot at the ENa corner of the diagram. 

Na+

/K+/Sr2+ (0.05 N)

The initial and equilibrium solution compositions in the 0.05 N Na+

/K+/Sr2+ system are shown 

in Figure 3.2-9(a). The results also indicate enrichment of Na+

 in the aqueous phase, 

although it was not as strong as that observed at 0.005 N (Figure 3.2-8a). On the other hand, 
3-16 

Figure 3.2-9(b) illustrates the stronger selectivity of clinoptilolite for K+

 relative to both 

Na+

 and Sr2+. The initially Na form of clinoptilolite became K-rich after reaction with 

Na+/K+/Sr2+ solutions. 

Figure 3.2-9(b) also compares the measured equilibrium solution composition (red circles) and 

the solution compositions (yellow circles) predicted from the thermodynamic model, using as 

input values the measured zeolite composition. The agreement between measured and 

predicted values is very good considering the uncertainties in analytical data. 

Na+

/K+/Cs+ (0.005 N)

The initial and equilibrium solution compositions in the 0.005 N Na+/K+/Cs+ system are shown in 

Figure 3.2-10(a). The high selectivity of clinoptilolite for the K+

 relative to Na+ and for Cs+

relative to Na+

 is reflected in the enrichment of Na+ in the aqueous phase. Zeolite samples were 

taken for measurement of equilibrium composition, but due to an error in specifying the 

analytical protocol for the zeolite samples, only Na and K analyses were done. The zeolite 

compositions plotted in Figure 3.2-10(b) were estimated based only on the measured Na and K 

concentrations of the zeolite. Figure 3.2-10(b) illustrates the preference of clinoptilolite for K+

and Cs+

 relative to Na+. Note that the experiment used a homoionic Na form of clinoptilolite, 

which would plot at the ENa corner of the diagram. 

Figure 3.2-10(b) also compares the measured equilibrium solution composition (red circles) and 

the solution compositions (yellow circles) predicted from the thermodynamic model, using as 

input values the estimated zeolite composition. Considering that the input values used for the 

thermodynamic model calculations are only estimated values, the agreement between 

measured and predicted values is relatively good. 

Na+

/K+/Cs+ (0.005 N)

The initial and equilibrium solution compositions in the 0.05 N Na+

/K+/Cs+ system are shown in 

Figure 3.2-11(a). Note that this system was the first ternary system studied and the initial 

solutions had different cationic ratios from those used in later experiments. Only 6 different 

initial solution compositions were used to generate the 18 isotherm points. One of those six 

compositions is a pure Cs+

 solution, four were Na+/Cs+ mixtures, and one is a Na+/K+/Cs+

mixture. Also, in this experiment only, the K form of clinoptilolite was used, as opposed to the 

Na form used in later experiments. The data shown in Figure 3.2-11(a) show enrichment of K+

in the aqueous phase, resulting from replacement of the K+

 initially in the zeolite by the Cs+ and 

Na+

 initially in the aqueous phase. 

No zeolite samples were taken for measurement of equilibrium composition. The zeolite 

compositions plotted in Figure 3.2-11(b) (green squares) were estimated based on the initial 

and final concentrations of Na+

, K+, and Cs+ in the aqueous phase. Figure 3.2-11(b) shows that 

the zeolite composition, which was initially the K form and would plot in the EK corner of the 

triangle, became Na+

 and Cs+ rich after ion-exchange with the aqueous solutions. 

Figure 3.2-11(b) also compares the measured equilibrium solution composition (red circles) and 

the solution compositions (yellow circles) predicted from the thermodynamic model, using as 

input values the estimated zeolite composition. The agreement between measured and 

predicted values is fair to poor and likely reflects both analytical uncertainty and the imprecision 

of the method used to estimate the equilibrium zeolite composition. 
3-17 

Quaternary System Na+

/K+/Ca2+/Sr2+ (0.05 N)

The results for the quaternary Na+

/K+/Ca2+/Sr2+ system at 0.05 N are shown in 

Figure 3.2-12(a–d) as a series of ternary diagrams. In the figures, tie lines connect the 

equilibrium compositions of the zeolite and aqueous phases. Most of the equilibrium zeolite 

compositions plot toward the EK corner of the ternary plots, illustrating the high selectivity of 

clinoptilolite for K+. No comparison is made between predicted and measured values because 

time constraints did not permit modifying the thermodynamic model to permit calculations of ionexchange equilibria for quaternary systems. 

3.2.5 Summary and Conclusions 

Ion-exchange experiments were conducted involving the zeolite mineral clinoptilolite and binary, 

ternary, and quaternary aqueous mixtures of Na+

, K+, Cs+, Ca2+, and Sr2+. The results of 

experiments involving binary cation mixtures, supplemented by data from published literature, 

were used to derive parameters for a thermodynamic model for zeolite component activity 

coefficients based on the Wilson equation. To help constrain the range of fitted model 

parameters and reduce the variance in derived parameter values, a correlation method that has 

been used in the literature to predict the formation constants of hydroxo-metal complexes was 

applied. Excellent agreement is obtained between values calculated with the model and 

experimental data on binary mixtures. The predictive capability of the thermodynamic model, 

which uses parameters derived from binary mixtures only, was tested by comparing calculated 

values and experimental data from experiments involving ternary cation mixtures. In general, 

the agreement between measured and calculated values is very good. Although further work is 

needed to compare the model predictions with the experimental data involving quaternary cation 

mixtures, the results for the ternary systems suggest that the thermodynamic model based on 

the Wilson equation can successfully predict ion-exchange equilibria in multicomponent systems 

of interest in geochemistry and chemical engineering. Although these models apply to shortlived fission products, and have not been incorporated into the performance assessment models 

for Yucca Mountain, they do provide insights in different sorption mechanisms that may be 

important in other waste management issues. 

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1986. 
3-18 

Bethke, C.M. Geochemical Reaction Modeling. Oxford, England: Oxford University Press. 

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3-19 

Freeman, D.H. “Thermodynamics of Binary Ion-Exchange Systems.” Journal of Chemical 

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Interfaces.” Journal of Physical Chemistry. Vol. 99, No. 23. pp. 9,458–9,465. 1995. 

Mukhopadhyay, B., S. Basu, and M.J. Holdaway. “A Discussion of Margules-Type Formulations 

for Multicomponent Solutions With a Generalized Approach.” Geochimica et Cosmochimica 

Acta. Vol. 57. pp. 277–283. 1993. 

Mumpton, F.A. “Natural Zeolites: A New Industrial Mineral Commodity.” Natural Zeolites: 

Occurrence, Properties, Use. L.B. Sand and F.A. Mumpton, eds. Elmsford, New York: 

Pergamon Press. pp. 3-27. 1978. 
3-20 

Mumpton, F.A. “Development of Uses for Natural Zeolites: A Critical Commentary.” 

Occurrence, Properties, and Utilization of Natural Zeolites. D. Kallo and H.S. Sherry, eds. 

Budapest, Hungary: Akademiai Kiado. pp. 333–366. 1988. 

Mumpton, F. A. and W.C. Ormsby. “Morphology of Zeolites in Sedimentary Rocks by Scanning 

Electron Microscopy.” Clays and Clay Minerals. Vol. 24. pp. 1–23. 1976. 

Pabalan, R.T. “Nonideality Effects on the Ion Exchange Behavior of the Zeolite Mineral 

Clinoptilolite.” Scientific Basis for Nuclear Waste Management XIV. T. Abrajano and 

L. Johnson, eds. MRS Symposium Proceedings 212. Pittsburgh, Pennsylvania: Materials 

Research Society. pp. 559–567. 1991. 

Pabalan, R.T. “Thermodynamics of Ion-Exchange Between Clinoptilolite and Aqueous 

Solutions of Na+

/K+ and Na+/Ca2+.” Geochimica et Cosmochimica Acta. Vol. 58. 

pp. 4,573–4,590. 1994. 

Pabalan, R.T. and F.P. Bertetti. “Thermodynamics of Ion-Exchange Between Na+

/Sr2+ Solutions 

and the Zeolite Mineral Clinoptilolite.” Scientific Basis for Nuclear Waste Management XVI. 

A. Barkatt and R. Van Konynenberg, eds. MRS Symposium Proceedings 333. 

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Pabalan, R.T. and F.P. Bertetti. “Experimental and Modeling Study of Ion Exchange Between 

Aqueous Solutions and the Zeolite Mineral Clinoptilolite.” Journal of Solution Chemistry. 

Vol. 28, No. 4. pp. 367–393. 1999. 

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Mineralogical Society of America. 2001. 

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E.J. Taylor, and M.E. Inman. “Hanford Tank Waste Remediation System Pretreatment 

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Pansini, M. “Natural Zeolites as Cation Exchangers for Environmental Protection.” Mineralium 

Deposita. Vol. 31, No. 6. pp. 563–575. 1996. 

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Pitzer, K.S. “A Thermodynamic Model for Aqueous Solutions of Liquid-Like Density.” Reviews 

in Mineralogy. Vol. 17. pp. 97–142. 1987. 

Pitzer, K.S. “Ion Interaction Approach: Theory and Data Correlation.” Activity Coefficients in 

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D.W. Tedder and F.G. Pohland, eds. Washington, DC: American Chemical Society. 

pp. 133–152. 1991. 
3-21 

Robinson, S.M., T.E. Kent, and W.D. Arnold. “Treatment of Contaminated Wastewater at Oak 

Ridge National Laboratory by Zeolites and Other Ion Exchangers.” Natural Zeolites ‘93: 

Occurrence, Properties, Use. D.W. Ming and F.A. Mumpton, eds. Brookport, New York: 

International Committee on Natural Zeolites. pp. 579–586. 1995. 

Shallcross, C.D., C.C. Hermann, and J.B. McCoy. “An Improved Model for the Prediction of 

Multicomponent ion Exchange Equilibria.” Chemical Engineering Science. Vol. 43. 

pp. 279–288. 1988. 

Sherry, H.S. “Cation Exchange on Zeolites.” Molecular Sieve Zeolites-I Advances in Chemistry 

Series 101. E.M. Flanigen and L.B. Sand, eds. American Chemical Society. pp. 350–379. 

1971. 

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Between Si-Rich and Si-Poor Phillipsites.” Separation Science and Technology. Vol. 33, 

No. 3. pp. 333–355. 1998. 

Shibue, Y. “Calculations of Fluid-Ternary Solid Solution Equilibria: An Application of the Wilson 

Equation to Fluid-(Fe,Mn,Mg)TiO3 Equilibria at 600 ±C and 1 kbar.” American Mineralogist. 

Vol. 84, No. 9. pp. 1,375–1,384. 1999. 

Sposito, G. The Thermodynamics of Soil Solutions. New York City, New York: 

Oxford University Press. 1981. 

Townsend, R.P. “Ion Exchange in Zeolites: Some Recent Developments in Theory and 

Practice.” Pure and Applied Chemistry. Vol. 58, No. 10. pp. 1,359–1,366. 1986. 

Townsend, R.P. and M. Loizidou. “Ion-Exchange Properties of Natural Clinoptilolite, Ferrierite, 

and Mordenite—I: Sodium-Ammonium Equilibria.” Zeolites. Vol. 4. pp. 191–195. 1984. 

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and Radiostrontium-Contaminated Soils: A Soil-Chemical Approach—1: Cs-K Exchange in 

Clinoptilolite and Mordenite.” Zeolites. Vol. 18, Nos. 2–3. pp. 205–211. 1997a. 

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and Radiostrontium–Contaminated Soils: A Soil-Chemical Approach—2: Sr-Ca Exchange in 

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Colloids, and Zeolites.” Soil Science. Vol. 33. pp. 95–113. 1932. 

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1992. 
3-22 

3.3 General Features of a Surface Complexation Modeling Approach 

3.3.1 Introduction to Surface Complexation Modeling 

The surface charge of many minerals varies as a function of pH (Kent, et al., 1988; Davis and 

Kent, 1990; Turner and Sassman, 1996; Sverjensky and Sahai, 1996; Sahai and Sverjensky, 

1997a,b). Surface complexation models address this pH dependence assuming an amphoteric 

surface composed of hydroxyl groups (>SOH0

). By adding a hydrogen ion (protonation), a 

positively charged surface site (>SOH2

+

) is developed. Conversely, losing a proton 

(deprotonation) leads to the development of a negatively charged surface (>SO-

). At low pH, 

the >SOH2

+

 sites outnumber the >SO- sites and the net surface charge is positive. At higher 

pH, the >SO-

 sites are more numerous and the net surface charge is negative. At some 

intermediate pH, referred to as the zero point of charge (pHZPC), the sites will balance and the 

surface will not exhibit any net charge. Depending on pH, therefore, the electrostatic attraction 

from these sites can lead to the specific adsorption of cations or anions from solution. 

In most surface complexation models, the acid-base behavior of a mineral surface is simulated 

using equilibrium protonation/deprotonation reactions in the form 

> SOH H SOH 0

2

+ + => + (3.3-1)

> SOH SO H 0 => + − + (3.3-2) 

where >SOH0

 represents a neutral surface site. The equilibrium constants for these protonation 

and deprotonation reactions, K+ and K−

, defined by mass action as 

[ ]

[ ][ ]

K

> S O H

> S O H H

2

0 +

+

+ = (3.3-3) 

[ ][ ]

[ ]

K

> S O H

> S O H 0 −

− +

= (3.3-4) 

are referred to as intrinsic surface acidity constants. Eqs. (3.3-3) and (3.3-4) show the pH 

dependence of surface charge development. 

By assuming an analogy to aqueous speciation reactions, surface adsorption can be described 

using a combination of equilibrium protonation/deprotonation and complexation reactions 

(Davis and Leckie, 1978; Hayes, et al., 1990; Davis and Kent, 1990; Dzombak and Morel, 1990; 

Langmuir, 1997; Gunnarsson, 2002; Payne, et al., 2004). Mass balance and mass action 

relations are then used in a manner analogous to that employed by geochemical aqueous 

speciation codes to determine the distribution of the elements between those dissolved in the 

bulk solution and those specifically sorbed onto the solid. For example, the sorption of cation 

Mm+ at the mineral–water interface can be written as 

> S O H 0 M m S O M(

m 1

)

H + => + + −+ (3.3-5) 
3-23 

The corresponding equilibrium constant for the reaction is defined such that 

[ ][ ]

[ ][ ]

K

> S O M H

R x n 3 . 3 - 5 > S O H M

(

m 1

)

= 0 m

− +

+ (3.3-6) 

and is used to constrain the mass action of the geochemical equilibrium model of the 

mineral–water interface. 

Depending on the specific surface complexation models approach used, additional parameters 

may be needed to include the electrostatic effects of a charged mineral surface. Electrical work 

performed in moving ions across the zone of charge influence adjacent to the interface will 

affect the activity of aqueous species near the charged surface relative to the bulk solution. In 

the surface complexation models approach, the change in activity of species (e.g., H+

 and Mm+) 

near the surface due to electrostatic forces is assumed to be governed by the Boltzmann 

relation such that 

{} {} [ ] ( ) M m M e x p - F / R T s u rf a c e

m

b ul k j

m

+ + = ψ (3.3-7) 

where exp[-FΨj /RT] is the Boltzmann factor; Ψj is the model-dependent electrostatic potential of 

the jth layer; m is the valence of the ion F and R are the Faraday (J·volt equiv−1

) and ideal gas 

(J·K−1

·mole−1) constants, respectively; and T is absolute temperature (K). This correction is 

incorporated into the mass action expressions for surface reactions. 

Mass balance for the total concentration of available surface sites (T>SOH in mol sites·L−1

) is 

T O T (

N )

(

A )

(

M V )1 0

> S O H 6 . 0 2 3 1 0 s S P

1 8

2 3 = ⋅⋅ ⋅

×

(3.3-8) 

where NS is site density (sites/nm2

), ASP (m2/g) is the specific surface area of the mineral, 

M/V is the solid mass to solution volume ratio (g/L

), 1018 is the conversion from nm2 to m2, 

and 6.023 × 1023 is Avogadro’s number. 

Another consideration for surface complexation models is developed in the two-site conceptual 

model Dzombak and Morel (1990) introduced to account for surface loading effects observed for 

cation sorption. In this conceptual model, it is assumed that the surface consists of a low 

density of high-affinity surface functional groups [Type 1 or “strong” sites (>SOHs

0

)] and a much 

greater density of low-affinity groups [Type 2 or “weak” sites (>SOHw

0

)]. The strong sites are 

assumed to saturate first and are used to explain the change from a linear proportionality 

between the amount sorbed and the amount dissolved to a more asymptotic relationship that is 

typically observed in cation adsorption. The weak sites are assumed to be present in sufficient 

concentration to account for the observed sorption maxima. This two-site conceptual approach 

can successfully represent macroscopic sorption behavior, but in practice it introduces the 

potential for an increase in the number of model parameters that must be developed. 

3.3.2 Model-Specific Features 

Although general features for establishing surface complexation formation are shared by all 

SCM approaches, the models differ in how the mineral–water interface is divided between the 
3-24 

charged surface and the bulk solution, and in the charge/potential relationships used to 

describe the interface layers (Figure 3.3-1). Model-specific aspects are detailed elsewhere 

(Kent, et al., 1988; Westall and Hohl, 1980; Davis and Kent, 1990; Dzombak and Morel, 1990; 

Brendler, et al., 2004). Each is summarized here. 

3.3.2.1 Diffuse Layer Model 

The diffuse layer model assumes that protonation/deprotonation and ion adsorption only occur 

in one plane at the mineral–water interface and that only those ions specifically adsorbed in this 

“o plane” contribute to the total surface charge density [Figure 3.3-1(a)]. In the diffuse layer 

model, the Stern-Grahame extension of the Gouy-Chapman relationship for symmetrical 

electrolytes is used to describe the interdependence between ionic strength (I), charge 

(σd = -σo = -σs at the boundary with the o plane), and electrostatic potential (Ψd = Ψo). 

The diffuse layer model is the simplest surface complexation model with the fewest adjustable 

parameters. Because of its simplicity, while retaining the thermodynamic aspects of the surface 

complexation model approach, the diffuse layer model is the sorption model that was used for 

most of the CNWRA process model abstractions for performance assessment. 

3.3.2.2 Constant Capacitance Model 

Like the diffuse layer model, the constant capacitance model also assumes a one-layer 

interface, and the reactions, mass balance, and mass action used to describe surface 

phenomena are the same as those presented for the diffuse layer model. In contrast to the 

diffuse layer model, the constant capacitance model assumes that the charged surface is 

separated from the bulk solution by a layer of constant capacitance [Figure 3.3-1(b)]. Based on 

this assumption, surface charge (σd = -σo = -σS) is related to surface potential (Ψo = Ψd) through 

a linear potential gradient from the charged substrate to the bulk solution. The constant 

capacitance model is limited, however, to a specified ionic strength. In a strict sense, changes 

in ionic strength require recalculation of a linear capacitance, C1. Generally, the capacitance 

term is not provided as a characteristic property of a given system, but instead is applied as an 

empirical parameter fit to the data (Westall and Hohl, 1980; Hayes, et al., 1991; 1990). This 

adjustment has the advantage of providing a better fit to a given data set, but at the expense of 

the theoretical basis of the model. 

3.3.2.3 Triple-Layer Model 

As the name suggests, the triple-layer model employs three layers to represent the 

mineral–water interface [Figure 3.3-1(c)]. In the original construction of the triple-layer model 

(Davis, et al., 1978), protonation/deprotonation of surface sites (K− and K) was restricted to an 

innermost o plane, while specifically adsorbed ions were assigned to the β plane 

(i.e., outer-sphere complexes). Subsequent modifications (Hayes and Leckie, 1987) provided 

for inner-sphere complexes to describe strongly bound metals. The outermost diffuse layer, the 

d plane, is made up of a diffuse region of counter ions extending into the bulk solution. This 

conceptual model leads to the introduction of an additional set of reactions for sorption of 

outer-sphere complexes. Surface charges in the triple-layer model are divided between the 

three layers σo, σβ, and σd for the o, β, and d layers, respectively. At the boundary between the 

intermediate β layer and the outermost d layer, the diffuse layer charge (σd) is defined such that 

σo + σβ + σd = 0. A linear relationship is used to describe the charge/potential relationships for 

the inner two layers using constant capacitances C1 and C2 associated with the areas between 
3-25 

the o and β planes and β and d planes, respectively. The charge/potential relationship for the 

outermost diffuse layer is the same as that used for the diffuse layer model. 

3.3.2.4 Nonelectrostatic Surface Complexation Model 

In contrast to traditional surface complexation models that include charge/potential relationships 

and incorporate electrostatic corrections into the mass action expressions for surface equilibria 

(e.g., Eq. 3.3-7), nonelectrostatic surface complexation models (Koß, 1988; Davis, et al., 1998; 

Del Nero, et al., 1998; Zavarin and Bruton, 1999; Emren and Jacobsson, 2004; Koheler, et al., 

2004) treat adsorption strictly as a chemical reaction phenomenon. In this approach, there are 

no explicit terms in the mass action expression to represent coulombic energy effects and the 

activity coefficient of the surface specie(s) is assumed to be 1. In a fashion similar to parameter 

estimation for more traditional surface complexation models, nonelectrostatic model parameters 

are determined by fitting a suite of one or more surface reactions to sorption data gathered over 

a range in chemical conditions. In effect, a nonelectrostatic approach acknowledges the 

difficulty in representing the surface charge accurately at the submicroscopic scale for complex, 

multimineral assemblages found in nature. 

3.3.2.5 Generalized Composite and Component Additivity Modeling Approaches 

Ultimately, performance assessment studies are concerned with the potential transport of 

radionuclides through the geosphere. In contrast, surface complexation model approaches 

were initially developed to simulate sorption onto simple (hydr)oxides under well-controlled 

laboratory conditions. While (hydr)oxides, such as ferrihydrite, goethite, and MnO2, are 

common in nature, aluminosilicate minerals (e.g., clays and zeolites) also may contribute 

significantly to radionuclide sorption. In addition, many natural materials of interest are a 

complex mixture of minerals and mineral coatings. This makes the representation of the 

charge-potential relationship at the mineral–water interface uncertain and difficult to quantify 

under in-situ conditions. 

There are typically two approaches to address sorption on mineral mixtures (Payne, et al., 2004; 

Waite, et al., 2000). The component additivity approach assumes that the mixture is 

represented by proportions of one or more pure mineral phases with generic surface functional 

groups, such as silanol (>SiOH0

), aluminol (>AlOH0), or ferrinol (>FeOH0). The individual 

surface properties for these phases are determined from experimental sorption data obtained 

for simple (hydr)oxides, such as SiO2 and ferrihydrite (Altmann, 1984; Honeyman, 1984; Turner, 

et al., 1996; Pabalan, et al., 1998; Pabalan and Turner, 1997; Turner, et al., 1998; Waite, et al., 

2000; Arnold, et al., 2001; Prikryl, et al., 2001; Brendler, et al., 2004; Hayes, et al., 1990; Payne, 

et al., 2004). The component additivity approach relies on combining the results of sorption 

modeling for each of these mineral phases to predict sorption behavior for the mineral mixture. 

Payne, et al. (2004) refer to this as a “bottom-up” approach. 

In contrast, a generalized composite model is a “top-down” approach (Payne, et al., 2004) that 

assumes the surface properties of natural materials are not well known, and contributions of 

individual site types to sorption behavior cannot be characterized with certainty. In the 

generalized composite approach, one or more generic site types (Wanner, et al., 1994; Davis, et 

al., 1998; Waite, et al., 2000; Payne, et al., 2004) are assumed and sorption data are described 

using a set of plausible surface reactions. Although electrostatics can be included, the 

generalized composite approach is more commonly developed as a nonelectrostatic model 

because of the uncertainty in selecting an appropriate charge-potential relationship for the 

mineral surface (Bradbury and Baeyens, 1997; Del Nero, et al., 1998; Davis, et al., 2002, 1998; 
3-26 

Zavarin and Bruton, 1999; Davis, 2001; Davis and Curtis, 2003; Kohler, et al., 2004; Payne, et 

al., 2004). While extrapolation beyond the experimental conditions used to determine the 

generalized composite model parameters is uncertain, the use of apparent binding constants 

and surface equilibria incorporates the aqueous chemistry aspects of the system to simulate 

complex sorption behavior (Davis, et al., 1998). 

3.3.3 References (Section 3.3) 

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Del Nero, K. Ben Said, B. Madè, A. Clément, and G. Bontems. “Effect of pH and Carbonate 

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Kohler, M., G.P. Curtis, D.E. Meece, and J.A. Davis. “Methods for Estimating Adsorbed 

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3-28 

Pabalan, R.T. and D.R. Turner. “Uranium(6+) Sorption on Montmorillonite: Experimental and 

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Adsorption on Natural Mineral Assemblages.” Radiochimica Acta. Vol. 88. pp. 687–693. 

2000. 

Wanner, H., Y. Albinsson, O. Karnl, E. Wieland, P. Wersin, and L. Charlet. “The Acid/Base 

Chemistry of Montmorillonite.” Radiochimica Acta. Vols. 66 and 67. pp. 733–738. 1994. 

Westall, J.C. and H. Hohl. “A Comparison of Electrostatic Models for the Oxide/Solution 

Interface.” Advances in Colloid and Interface Science. Vol. 12. pp. 265–294. 1980. 

Zavarin, M. and C.J. Bruton. “A Non-Electrostatic Surface Complexation Approach to Modeling 

Radionuclide Migration: The Role of Iron Oxides and Carbonate. UCRL–JC–133474. 

Livermore, California: Lawrence Livermore National Laboratory. 1999. 

3.4 Molecular Dynamics Simulation of Uranium(VI) Sorption 

Sorption experiments, such as those described in Section 2, provide useful information on the 

effects of solution chemistry and mineral sorbent properties on uranyl sorption behavior. 

However, these experiments study only the macroscopic aspects of the interaction of uranium 
3-29 

with the mineral surface and give no direct information on the structure and local chemical 

environment of the sorbed species. X-ray absorption spectroscopy, which includes x-ray 

absorption near-edge structure and extended x-ray absorption fine structure spectroscopy, is 

useful in directly probing the structure and oxidation state of sorbed metals. Some x-ray 

absorption spectroscopy results are presented in Section 2.3. Although x-ray absorption fine 

structure provides information on the number of oxygen atoms coordinated to the uranium atom 

and on uranium–oxygen interatomic distances, it does not determine directly whether the 

coordinating oxygen atom is contributed by a solvating water, by a hydroxide or carbonate 

ligand, or by a surface hydroxyl group. Also, because of the low solubility of uranium, 

particularly at near-neutral to alkaline pH, x-ray absorption fine structure analysis can be 

complicated by formation of oligomeric uranyl species or precipitation of a uranium solid when 

relatively high uranyl concentrations are used to enhance the x-ray signal. 

Molecular simulation techniques can serve as a useful complement to sorption experiments and 

spectroscopic methods. The sorption behavior of specific uranyl species on different minerals 

can be studied directly through simulations of realistic models of molecular complexes and 

mineral surfaces. The complicating effects of oligomeric species formation and precipitation can 

be avoided by using a suitable number of molecular species in the simulation. This section 

describes the use of molecular simulations in a preliminary study of the sorption of uranyl 

species on a quartz surface. Quartz was selected as the sorbent for this study because it is a 

major rock-forming mineral in many geologic environments, it has a relatively simple 

composition, and experimental data are available on uranyl sorption on this mineral over wide 

ranges of uranium concentration, pH, and solid-mass to solution volume ratio. 

The molecular simulation approach is detailed in Greathouse, et al. (2002). It comprised a 

comprehensive series of molecular dynamics simulations of aqueous uranyl complexes with 

OH–

 and CO3

2– in bulk water and near the quartz (010) surface. The OH–

 and CO3

2– anions are 

common components in natural waters, and CO3

2– is likely a dominant anionic species in the 

groundwaters of Yucca Mountain, Nevada. The quartz (010) surface was chosen in this 

preliminary study with the goal of examining the effects of surface protonation and coordinating 

ligands on surface complexation and energy. 

3.4.1 Computational Methods 

The simulations were performed using the Open Force Field module of Cerius2 4.0 from 

Accelrys, Inc. (formerly Molecular Simulations, Inc.). The Crystal Builder module was used to 

create a 32-unit cell slab of α-quartz, and the Surface Builder module was used to create the 

(010) surface. The simulation supercell had x and y dimensions of 19.68 and 21.65 Å, 

respectively, corresponding to four unit cells of quartz in each direction. The quartz slab was 

approximately 2 unit cells (ca. 8 Å) in depth and consisted of 80 silicon atoms and 160 oxygen 

atoms. The quartz slab was terminated with hydrogen atoms (upper surface) and hydroxyl 

groups (lower surface), resulting in two singly protonated surfaces (336 atoms). The 

z dimension of the supercell was fixed at 32.00 Å to allow room for water molecules and 

aqueous species (UO2

2+, CO32–, and OH–

). 

To examine uranyl adsorption near deprotonated surface oxygen atoms, a partially 

deprotonated surface was created by moving protons from a cluster of six surface hydroxyl 

groups to distant hydroxyl groups. Potential parameters for aqueous species were based on the 

Consistent-Valence Forcefield, which employs the flexible version of the Simple Point Charge 

water model. Intramolecular motion (bond stretching and angle bending) was allowed for all 

aqueous species and for hydroxyl groups at the upper quartz surface. The remainder of the 
3-30 

quartz surface was held rigid throughout the simulations. The potential energy component for 

nonbonded interactions consisted of Coulombic and van der Waals terms between atoms i and j

at a separation of r. The potential energy term for bonded interactions consisted of 

angle-bending and bond-stretching terms. Other than the quartz slab, which was treated as a 

rigid body, no species contained more than three atoms bonded in a chain; hence, a torsional 

term was not used. 

For simulations in bulk solution, a cubic supercell with an 18.2-Å edge length containing 

200 water molecules and the necessary ionic species was used. All hydrated slab simulations 

were conducted with 300 water molecules. Three-dimensional periodic boundary conditions 

were applied to the supercell in all simulations, resulting in either a bulk solution or macroscopic 

quartz–water system. 

Before beginning a molecular dynamics simulation, the local water structure around the ions 

and the quartz surface was optimized for at least 500 steps using the OFF Minimizer module of 

Cerius2. After minimization, the uranyl ion had already formed its primary coordination shell. 

Molecular dynamics simulations were performed using the OFF Dynamics module of Cerius2 in 

the constant (NVT) ensemble with the Nosé-Hoover thermostat. Temperature was set to 300 K 

with a 0.1-ps relaxation time, although an average temperature of approximately 298 K was 

achieved in most simulations. A cutoff of 9 Å was used for short-range interactions, and 

Coulombic interactions were treated using the three-dimensional Ewald sum. Total simulation 

times were 100 ps with a timestep of 0.0005 ps. 

Equilibration was achieved after 10–20 ps, as determined by monitoring running averages and 

fluctuations in the potential energy and temperature. Equilibrium average values for potential 

energy and temperature were obtained, and the (x, y, z) trajectories of water molecules and 

aqueous species were plotted. Radial distribution functions were obtained in the usual manner 

(Allen and Tildesley, 1987) and were itemized according to either forcefield atom types 

(uranium–oxygenw) or by element type (uranium–oxygen). This approach allowed the 

components of the uranium–oxygen radial distribution functions peaks due to water oxygen 

atoms, surface oxygen atoms, or anion oxygen atoms to be distinguished. 

3.4.2 Results and Discussion 

Results for simulations in bulk solution are presented in Table 3.4-1. The equilibrium 

coordination shell for [UO2(H2O)5]

2+ consisted of five water molecules in a pentagonal 

bipyramidal geometry, with uranyl oxygen atoms in the axial positions. Figure 3.4-1 shows the 

uranium–oxygen and uranium–hydrogen radial distribution functions averaged over the 

equilibrium portion of the simulation. The uranium–oxygen radial distribution functions contain 

two peaks, one corresponding to the primary solvation shell at 2.49 Å and a second broader 

peak at 4.85 Å. This average uranium–oxygenw distance of 2.49 Å agrees well with the 

experimental value of 2.42 Å, obtained from proton nuclear magnetic resonance and x-ray 

diffraction experiments (Aberg, et al., 1983). 

Averaged uranium–oxygen radial distribution functions for simulations of hydroxide and 

carbonate complexes (OH–

 or CO3

2–) are shown in Figure 3.4-2(a), with peak distances 

tabulated in Table 3.4-1. The presence of OH–

 or CO3

2– in the uranyl complex results in a split 

equatorial shell about the uranium atom, with oxygen atoms from OH–

 or CO3

2– occupying 

positions closer than water oxygen atoms. For the uranyl hydroxy complexes, the calculated 

uranium–oxygen–hydrogen distances are consistent with gas-phase ab initio calculations of the 

complex [UO2(H2O)(OH)4]

2–, which gave U–OH and U–Ow distances of 2.43–2.47 Å and 2.65 Å, 
3-31 

respectively (Wahlgren, et al., 1999). The equatorial uranium–oxygen–hydrogen distances are 

also in agreement with recent extended x-ray absorption fine structure experiments of UO2

2+ in 

aqueous tetramethylammonium hydroxide solution, in which the uranium–oxygen–hydrogen 

distance was measured to be 2.22 Å and 2.24 Å (Clark, et al., 1999; Wahlgren, et al., 1999). 

Carbonate ions formed bidentate complexes with uranium, and a similar equatorial splitting was 

seen [Figure 3.4-2(a) and Table 3.4-1]. No experimental or ab initio results for the aqueous 

carbonate complexes studied here were available for comparison. However, the tricarbonato 

complex, [UO2(CO3)3]

4–, has been studied. Recent extended x-ray absorption fine structure 

experiments show the uranium–oxygen(carbonate) distance to be 2.42 Å for [UO2(CO3)3]

4– in 

the solid phase and 2.46 Å for the trimeric form, [(UO2)3(CO3)6]

6–, in aqueous solution (Allen, et 

al., 1995). Gas-phase calculations on [UO2(CO3)3]

4– show this length to be 2.58 Å. For both the 

hydroxy and carbonato complexes, the simulation results show equatorial uranium–oxygen 

distances in much better agreement with experiments than gas-phase quantum mechanical 

calculations, indicating that perhaps the presence of surrounding solvent molecules is key when 

performing calculations of aqueous uranyl complexes. 

Singly Protonated Surface. Simulations performed with the singly protonated quartz (010) 

surface are summarized in Figure 3.4-2(b) and Table 3.4-2. A model of the quartz–water 

interface initially was tested with a simulation supercell constructed with 300 water molecules. 

An equilibrium snapshot of this supercell is shown in Figure 3.4-3. To investigate uranyl 

adsorption onto the quartz (010) surface, a single uranyl ion was added to the 300-water 

supercell, at an initial uranium-surface distance of 6 Å. The equilibrated structure 

[Figure 3.4-4(a)] indicates that the uranyl ion maintained its five fold water solvation shell while 

forming an outer-sphere surface complex with quartz. As seen in Figure 3.4-4(b), the uranyl 

oxygen–uranium–oxygen axis maintained an angle of approximately 45 °C [113 °F] to the 

surface normal. The average uranium–oxygenw distance of 2.51 Å (Table 3.4-2) indicates that 

the first coordination shell geometry of UO2

2+ is the same near the quartz surface as in 

bulk water. 

Simulations in the presence of carbonate and hydroxide ions also were performed, with the 

carbonate or hydroxide ions initially placed in close proximity to the uranyl ion. For the 

hydroxide ion, separate simulations were performed with a single UO2

2+ ion with one, two, or 

three OH–

 in the aqueous layer. In all cases, the uranyl ion maintained a fivefold coordination 

shell, thus forming outer-sphere complexes with the singly protonated quartz surface. As 

with simulations in the bulk, the presence of OH–

 or CO3

2– is indicated by a split in the 

equatorial shell [Figure 3.4-2(b) and Table 3.4-2]. The averaged uranium–oxygen distances are 

nearly identical to those in bulk solution, indicating that the singly protonated surface has little 

effect on the uranyl coordination geometry. 

Deprotonated Surface. Results for simulations of UO2

2+ sorption near a partially deprotonated 

quartz surface are summarized in Figure 3.4-2(c) and Table 3.4-3. In the initial simulation of 

water near this surface, the oxygen–oxygen and oxygen–hydrogen radial distribution function 

peaks were identical to those values from the singly protonated surface. Next, a uranyl ion was 

placed near the partially deprotonated surface (at 4.0 or 2.50 Å distance). The uranyl ion 

maintained its fivefold water solvation shell and never formed an inner-sphere surface complex. 

The radial distribution function data showed an average uranium–oxygen distance of 2.50 Å 

with five water oxygen atoms occupying the first solvation shell. Only when the uranyl ion was 

initially placed at a distance of 2.0 Å from the quartz surface did an inner-sphere surface 

complex form. As Table 3.4-3 indicates, the resulting surface complex, {UO2(H2O)4[O]2+}, had 

an average uranium–oxygen distance of 2.50 Å. Here the notation [O] is used to indicate the 
3-32 

presence of surface oxygen atoms in the equatorial shell of uranium. The availability of 

deprotonated surface oxygen atoms appears to be a prerequisite for the formation of innersphere uranyl surface complexes. However, the results indicate that such surface complexes 

form only when the initial uranium–surface distance was less than 2.0 Å. In addition, the results 

show only a single equatorial shell for this adsorbed species, in contrast to the split equatorial 

shell suggested by extended x-ray absorption fine structure results of the uranyl ion adsorbed to 

silica surfaces (Reich, et al., 1996; Sylwester, et al., 2000). Again, such a split equatorial shell 

is observed in our simulations only when another ligand (OH–

 or CO3

2–) is present in the 

equatorial shell of UO2

2+. 

With one or two hydroxide ions coordinated to the uranium atom, inner-sphere surface 

complexes also formed, but only when the uranyl species was placed within 2.0 Å of the 

surface, with no water molecules in between. Figure 3.4-5(a) shows an equilibrium snapshot of 

the [(UO2)(H2O)(OH)2{O}2] surface complex. 

With carbonate ions in the simulation supercell, an inner-sphere surface complex formed when 

only one ion was coordinated to UO2

2+, and only with monodentate surface coordination. Such 

a surface complex is shown in Figure 3.4-5(b), with CO3

2– oriented such that hydrogen bonding 

occurs between one carbonate oxygen atom and surface protons. Table 3.4-3 indicates the 

presence of a split equatorial shell, consisting of an inner peak of carbonate oxygen atoms 

and an outer peak of water and surface oxygen atoms. The dicarbonato complex 

[UO2(H2O)(CO3)2]

2–, with each carbonate ion acting as a bidentate ligand, never formed an 

inner-sphere surface complex but remained anchored to the surface via hydrogen bonding 

between the coordinating water molecule and the surface. For the dicarbonato complex, 

average uranium–oxygenL and uranium–oxygenw distances are similar to those for the singly 

protonated surface. 

3.4.3 Conclusions 

The range of possible uranyl orientations and motion relative to the quartz surface is 

summarized in Figure 3.4-6, which shows the trajectory of the z coordinate of a uranium atom 

from three simulations. Near the partially deprotonated surface, the uranyl ion forms an 

inner-sphere complex involving one or two surface oxygen atoms. The uranium atom is 

fastened to the surface, as evidenced by the limited motion in the z direction. Near the singly 

protonated surface, the uranyl ion forms an outer-sphere complex with hydrogen bonding 

between a coordinated water molecule and the surface [Figure 3.4-4(a)]. As a result, the fully 

solvated ion hovers approximately 5 Å above the surface. Finally, a layer of nonsolvating water 

molecules can be interposed between a uranyl complex and the surface, resulting in a diffuse 

species. A diffuse uranyl complex is characterized by increased mobility relative to the surface, 

as seen in Figure 3.4-6. Two mechanisms exist for the formation of stable uranyl surface 

complexes. First, a uranyl ion coordinated by water molecules and/or anion ligands can form an 

outer-sphere surface complex, in which a solvating water molecule is hydrogen bonded to a 

surface oxygen atom. This arrangement results in a uranium–silicon distance of approximately 

6 Å (Table 3.4-2). Second, a uranyl ion can form an inner-sphere surface complex, via 

either monodentate or bidentate coordination to surface oxygen atom(s). The average 

uranium–silicon distance in this case is approximately 4 Å (Table 3.4-3). The combination of 

inner-sphere surface complexation between the uranium and surface oxygen atoms and 

additional hydrogen bonding between solvating oxygen atoms and surface protons results in an 

immobile surface complex. However, due to difficulties in adequate phase space sampling, 

inner-sphere surface complexes were not seen in the simulations unless the uranyl ion was 

initially placed less than 2.0 Å from the surface with no interposing water molecules. 
3-33 

3.4.4 References (Section 3.4) 

Aberg, M., D. Ferri, J. Glaser, and I. Grenthe. “Structure of the Hydrated Eioxouranium(VI) Ion 

in Aqueous Solution. An X-ray Diffraction and 1

H NMR Study.” Inorganic Chemistry. Vol. 22. 

pp. 3,986–3,989. 1983. 

Allen, M. P. and D.J. Tildesley. Computer Simulation of Liquids. Oxford, England: Clarendon 

Press. 1987. 

Allen, P.G., J.J. Bucher, D.L. Clark, N.M. Edelstein, S.A. Ekberg, J.W. Gohdes, E.A. Hudson, 

N. Kaltsoyannis, W.W. Lukens, M.P. Neu, P.D. Palmer, T. Reich, D.K. Shuh, C.D. Tait, and 

B.D. Zwick. “Multinuclear NMR, Raman, EXAFS, and X-ray Diffraction Studies of Uranyl 

Carbonate Complexes in Near-Neutral Aqueous Solution: X-ray Structure of 

[C(NH2)3]6[(UO2)3(CO3)6]⋅6.5H2O.” Inorganic Chemistry. Vol. 34. pp. 4,797–4,807. 1995. 

Clark, D.L., S.D. Conradson, R.J. Donohoe, D.W. Keogh, D.E. Morris, P.D. Palmer, 

R.D. Rogers, and C.D. Tait. “Chemical Speciation of the Uranyl ion Under Highly Alkaline 

Conditions: Synthesis, Structures, and Oxo Ligand Exchange Dynamics." Inorganic Chemistry. 

Vol. 38. pp. 1,456–1,466. 1999. 

Greathouse, J.A., R.J. O’Brien, G. Bemis, and R.T. Pabalan. “Molecular Dynamics Study of 

Aqueous Uranyl Interactions With Quartz (010).” Journal of Physical Chemistry B. Vol. 106. 

pp. 1,646–1,655. 2002. 

Reich, T., H. Moll, M.A. Denecke, G. Geipel, G. Bernhard, and H. Nitsche. “Characterization of 

Hydrous Uranyl Silicate by EXAFS.” Radiochimica Acta. Vol. 74. pp. 219–223. 1996. 

Sylwester, E. R., E. A. Hudson, and P.G. Allen. “The Structure of Uranium(VI) Sorption 

Complexes on Silica, Alumina, and Montmorillonite.” Geochimica et Cosmochimica Acta. 

Vol. 64. pp. 2,431–2,438. 2000. 

Wahlgren, U., H. Moll, I. Grenthe, B. Schimmelpfennig, L. Maron, V. Vallet, and O. Gropen. 

“Structure of Uranium(VI) in Strong Alkaline Solutions: A Combined Theoretical and 

Experimental Investigation.” Journal of Physical Chemistry A. Vol. 103. pp. 8,257–8,264. 

1999. 
3-34 

(a) (b)

(c) (d)

(e) (f)

Figure 3.2-1. Experimental Data for Ion Exchange Between Clinoptilolite and Aqueous 

Solutions of (a) Na+

+K+, (b) Cs++K+, (c) Cs++Na+, (d) Sr2++K+, (e) Ca2++K+, and (f) Sr2++Ca2+

ENa

0.0 0.2 0.4 0.6 0.8 1.0

ENa

0.0

0.2

0.4

0.6

0.8

1.0

Na+/K+ (0.05 N)

_

Na+ data

K+ data

ECs

0.0 0.2 0.4 0.6 0.8 1.0

ECs

0.0

0.2

0.4

0.6

0.8

1.0

_

Cs+/K+ (0.05 N)

K+

 data

Cs+ data

ECs

0.0 0.2 0.4 0.6 0.8 1.0

ECs

0.0

0.2

0.4

0.6

0.8

1.0

Cs+/Na+ (0.05 N)

_

Cs+ data

Na+ data

ESr

0.0 0.2 0.4 0.6 0.8 1.0

ESr

0.0

0.2

0.4

0.6

0.8

1.0

Sr2+/K+ (0.001 N)

K+ data

Sr2+ data

ECa

0.0 0.2 0.4 0.6 0.8 1.0

ECa

0.0

0.2

0.4

0.6

0.8

1.0

Ca2+/K+ (0.00149 N)

−

K+ data

Ca2+ data

ESr

0.0 0.2 0.4 0.6 0.8 1.0

ESr

0.0

0.2

0.4

0.6

0.8

1.0

−

Sr2+/Ca2+ (0.05 N)

Ca2+ data

Sr2+ data
3-35 

Figure 3.2-2. Comparison of Experimental Data on Ion Exchange Involving Clinoptilolite 

and Aqueous Solutions of Li+

/Na+, K+/Na+, Cs+/Na+, and Cs+/K+ With Isotherms 

(Solid Curve) Calculated Using the Wilson Model. The Model Used Parameters Derived 

From Regressing the Vanselow Selectivity Coefficients Calculated From the 

Ion-Exchange Data to Eqs. (3.2-14) to (3.2-16). R2

 Values Derived From Fitting Vanselow 

Selectivity Coefficients for the Systems Li+

/Na+, K+/Na+, Cs+/Na+, and Cs+/K+ Are 0.45, 0.92, 

0.15, and 0.49, Respectively. 

ELi

0.0 0.2 0.4 0.6 0.8 1.0

ELi

0.0

0.2

0.4

0.6

0.8

1.0

Li+/Na+

_

Chelishchev et al. (1973) - 0.1 N

ECs

0.0 0.2 0.4 0.6 0.8 1.0

ECs

0.0

0.2

0.4

0.6

0.8

1.0

Cs+/Na+

_

Ames (1964a) - 1.0 N

Chelischev et al. (1973) - 0.1 N

Howery and Thomas (1965) - 0.02 N

EK

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

EK

K+/Na+ ( 0.50 N)

Na+ data (Pabalan, 1994)

K+ data (Pabalan, 1994)

_

ECs

0.0 0.2 0.4 0.6 0.8 1.0

ECs

0.0

0.2

0.4

0.6

0.8

1.0

_

Cs+/K+

Ames (1968) - 1.0 N

Chelishchev et al. (1973) - 0.1 N
3-36 

Figure 3.2-3. Comparison of Experimental Data on Ion Exchange Involving Clinoptilolite 

and Aqueous Solutions of Cs+

/Rb+, NH4

+

/Na+, NH4

+

/K+, and NH4

+

/Ca2+ With Isotherms 

(Solid Curve) Calculated Using the Wilson Model. The Model Used Parameters 

Derived From Regressing the Vanselow Selectivity Coefficients Calculated From the 

Ion-Exchange Data to Eqs. (3.2-14) to (3.2-16). R2

 Values Derived From Fitting Vanselow 

Selectivity Coefficients for the Systems Cs+

/Rb+, NH4

+

/Na+, NH4

+

/K+, and NH4

+

/Ca2+ Are 

0.98, 0.32, 0.29, and 0.78, Respectively. 

ENH4

0.0 0.2 0.4 0.6 0.8 1.0

ENH4

0.0

0.2

0.4

0.6

0.8

1.0

NH4

+

/K+

_

Jama and Yucel (1990) - 0.1 N

ECs

0.0 0.2 0.4 0.6 0.8 1.0

ECs

0.0

0.2

0.4

0.6

0.8

1.0

_

Cs+/Rb+

Chelishchev et al. (1973) - 0.1 N

ENH4

0.0 0.2 0.4 0.6 0.8 1.0

ENH4

0.0

0.2

0.4

0.6

0.8

1.0

_

NH4

+

/Na+

Townsend and Loizidou (1984)

Jama and Yucel (1990)

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

NH4

+

/Ca2+

ENH4

ENH4

_

Jama and Yucel (1990) - 0.1 N
3-37 

Figure 3.2-4. Comparison of Experimental Data on Ion Exchange Involving Clinoptilolite 

and Aqueous Solutions of 0.005 N Ca2+/Na+

, 0.05 N Ca2+/Na+, 0.5 N Ca2+/Na+, and 0.05 N 

Ca2+/K+

 With Isotherms (Solid Curve) Calculated Using the Wilson Model. The Model 

Used Parameters Derived From Regressing the Vanselow Selectivity Coefficients 

Calculated From the Ion-Exchange Data to Eqs. (3.2-14) to (3.2-16). R2

 Values Derived 

From Fitting Vanselow Selectivity Coefficients for the Systems 0.005 N Ca2+/Na+

, 0.05 N 

Ca2+/Na+

, 0.5 N Ca2+/Na+, And 0.05 N Ca2+/K+ Are 0.68, 0.95, 0.98, and 0.95, Respectively. 

ECa

0.0 0.2 0.4 0.6 0.8 1.0

ECa

0.0

0.2

0.4

0.6

0.8

1.0

_

Ca2+/Na+ (0.5 N)

Na+ data (Pabalan, 1994)

Ca2+ data (Pabalan, 1994)

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

ECa

ECa

_

Ca2+/Na+ (0.005 N)

Na+ data (Pabalan, 1994)

Ca2+ data (Pabalan, 1994)

ECa

ECa

_

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Ca2+/Na+ (0.05 N)

Na+ data (Pabalan, 1994)

Ca2+ data (Pabalan, 1994)

ECa

0.0 0.2 0.4 0.6 0.8 1.0

ECa

0.0

0.2

0.4

0.6

0.8

1.0

Ca2+/K+ (0.05 N)

−

K+ data (Pabalan and Bertetti, 1999)

Ca2+ data (Pabalan and Bertetti, 1999)
3-38 

Figure 3.2-5. Comparison of Experimental Data on Ion Exchange Involving Clinoptilolite 

and Aqueous Solutions of 0.005 N Sr2+/Na+

, 0.05 N Sr2+/Na+, 0.5 N Sr2+/Na+, 1.0 N Sr2+/Ca2+, 

and 0.05 N Sr2+/K+

 With Isotherms (Solid Curve) Calculated Using the Wilson Model. The 

Model Used Parameters Derived From Regressing the Vanselow Selectivity Coefficients 

Calculated From the Ion-Exchange Data to Eqs (3.2-14) to (3.2-16). R2

 Values 

Derived From Fitting Vanselow Selectivity Coefficients for the Systems Sr2+/Na+

(0.005, 0.05, and 0.05 N), 1.0 N Sr2+/Ca2+, and 0.05 N Sr2+/K+

 Are 0.68, 90, and 

0.88, Respectively. 

ESr

0.0 0.2 0.4 0.6 0.8 1.0

ESr

0.0

0.2

0.4

0.6

0.8

1.0

−

0.005 N

0.05 N

0.5 N

Sr2+/Na+ (Pabalan and Bertetti, 1999)

Sr2+ data

Na+

 data

ESr

0.0 0.2 0.4 0.6 0.8 1.0

ESr

0.0

0.2

0.4

0.6

0.8

1.0

−

Sr2+/K+ (0.05N)

K+ data (Pabalan and Bertetti, 1999)

Sr2+ data (Pabalan and Bertetti, 1999)

ESr

0.0 0.2 0.4 0.6 0.8 1.0

ESr

0.0

0.2

0.4

0.6

0.8

1.0

−

Sr2+/Ca2+

Ames (1964b) - 1.0 N
3-39 

(a) (b)

Figure 3.2-6. (a) Initial and Equilibrium Solution Compositions in the 0.005 N Na+

/K+/Ca2+

System. (b) Solution and Zeolite Compositions at Equilibrium in the 0.005 N Na+

/K+/Ca2+

System. Also Shown in (b) Are the Solution Compositions Predicted From 

the Thermodynamic Model, Using As Input Values the Measured Equilibrium 

Zeolite Compositions. 

ENa 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

EK

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

ECa

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Na+/K+/Ca2+ System

(0.005 N)

initial solution composition

equilibrium solution composition

ENa 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

EK

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

ECa

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Na+/K+/Ca2+ System

(0.005 N)

equilibrium solution composition

equilibrium zeolite composition

solution composition predicted from zeolite analysis
3-40 

(a) (b) 

Figure 3.2-7. (a) Initial and Equilibrium Solution Compositions in the 0.05 N Na+

/K+/Ca2+

System. (b) Solution and Zeolite Compositions at Equilibrium in the 0.05 N Na+

/K+/Ca2+

System. Also Shown in (b) Are the Solution Compositions Predicted From 

the Thermodynamic Model, Using as Input Values the Measured Equilibrium 

Zeolite Compositions. 

ENa 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

EK

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

ECa

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Na+/K+/Ca2+ System

(0.05 N)

initial solution composition

equilibrium solution composition

ENa 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

EK

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

ECa

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Na+/K+/Ca2+ System

(0.05 N)

equilibrium zeolite composition

equilibrium solution composition

solution composition predicted from zeolite analysis
3-41 

(a) (b) 

Figure 3.2-8. (a) Initial and Equilibrium Solution Compositions in the 0.005 N Na+

/K+/Sr2+

System. (b) Equilibrium Solution Compositions (Red Circles) in the 0.005 N Na+

/K+/Sr2+

System. No Zeolite Samples That Equilibrated With the Aqueous Phase Were Analyzed. 

The Estimated Zeolite Compositions at Equilibrium Plotted as Green Squares 

Were Calculated From the Initial and Final Concentrations of Na+

, K+, and Sr2+ in 

the Aqueous Phase. 

ENa 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

EK

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

ESr

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Na+/K+/Sr2+ System

(0.005 N)

initial solution composition

equilibrium solution composition

ENa 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

EK

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

ESr

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Na+/K+/Sr2+ System

(0.005 N)

equilibrium solution composition

estimated equlibrium zeolite composition
3-42 

(a) (b) 

Figure 3.2-9. (a) Initial and Equilibrium Solution Compositions in the 0.05 N Na+

/K+/Sr2+

System. (b) Solution and Zeolite Compositions at Equilibrium in the 0.05 N Na+

/K+/Sr2+

System. Also Shown in (b) Are the Solution Compositions Predicted From the 

Thermodynamic Model, Using As Input Values the Measured Equilibrium Zeolite 

Compositions. Only Selected Zeolite Samples Were Analyzed, Thus the Data Points for 

Equilibrium Zeolite Compositions Are Fewer Than the Data Points for Initial and 

Equilibrium Solution Compositions Shown in Figure 3.2-9(a). 

ENa 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

EK

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

ESr

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Na+/K+/Sr2+ System

(0.05 N)

initial solution composition

equilibrium solution composition

ENa 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

EK

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

ESr

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Na+/K+/Sr2+ System

(0.05 N)

equilibrium solution composition

equilibrium zeolite composition

equilibrium solution composition predicted from zeolite analysis
3-43 

(a) (b) 

Figure 3.2-10. (a) Initial and Equilibrium Solution Compositions in the 0.005 N Na+

/K+/Cs+

System. (b) Solution and Zeolite Compositions at Equilibrium in the 0.005 N Na+

/K+/Cs+

System. Also Shown in (b) Are the Solution Compositions Predicted From 

the Thermodynamic Model, Using as Input Values the Estimated Equilibrium 

Zeolite Compositions. 

ENa 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

EK

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

ECs

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Na+/K+/Cs+ System

(0.005 N)

equilibrium solution composition

initial solution composition

ENa 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

EK

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

ECs

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Na+/K+/Cs+ System

(0.005 N)

equilibrium solution composition

equilibrium zeol comp from analy of Na&K in zeolite

equilibrium solution composition predicted from analysis of zeolite Na & K
3-44 

(a) (b) 

Figure 3.2-11. (a) Initial and Equilibrium Solution Compositions in the 0.05 N Na+

/K+/Cs+

System. (b) Solution and Zeolite Compositions at Equilibrium in the 0.05 N Na+

/K+/Cs+

System. Also Shown in (b) Are the Solution Compositions Predicted From 

the Thermodynamic Model, Using as Input Values the Estimated Equilibrium 

Zeolite Compositions. 

ENa 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

EK

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

ECs

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Na+/K+/Cs+ System

(0.05 N)

initial solution composition

equilibrium solution composition

ENa 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

EK

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

ECs

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Na+/K+/Cs+ System

equilibrium soln. comp. predicted from estimated zeol. comp.

equilibrium solution composition

estimated equilibrium zeolite composition
3-45 

Figure 3.2-12. Solution and Zeolite Compositions at Equilibrium in the Quaternary 

Na+

/K+/Ca2+/Sr2+ System at 0.05 N. Tie-Lines Connect Corresponding Zeolite 

and Aqueous Samples. 

EN 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

EK

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

ECa

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

equilibrium zeolite composition

equilibrium solution composition

ENa 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

EK

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

r

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

equilibrium zeolite composition

equilibrium solution composition

ENa 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

ECa

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

ESr

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

equilibrium zeolite composition

equilibrium solution composition

EK 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

ECa

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

ESr

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

equilibrium zeolite composition

equilibrium solution composition
3-46 

Figure 3.3-1 Schematic Diagram of the Mineral-Water Interface as Represented in 

Complexation Modeling Approaches. (a) Diffuse-Layer Model, (b) Constant Capac

(c) Triple Layer Model. (Modified from Hayes, et al., 1991).
3-47 

Figure 3.4-1. Plots of the U–O (Solid Line) and U–H (Dashed Line) Radial Distribution 

Functions From a Simulation of UO2

2+ in a Box of 200 Water Molecules. The Peak Due To 

Axial Oxygen Atoms in UO2

2+ Is Not Shown. Reproduced With Permission From 

Greathouse, J.A., R.J. O’Brien, G. Bemis, and R.T. Pabalan. Journal of Physical 

Chemistry B. Vol. 106. p. 1,646. 2002. Copyright 2002. American Chemical Society. 
3-48 

Figure 3.4-2. Plots of the U–O Radial Distribution Functions From Simulations in (a) Bulk 

Solution, (b) Singly Protonated Quartz (010) Surface, and (c) Partially Deprotonated 

Quartz (010) Surface. Ionic Species in the Aqueous Layer Are Indicated Next to Each 

Plot. Peaks Are Assigned in Tables 3.4-2 through 3.4-4. For the Results With 1 UO2

2+ and 

2 CO3

2–, the Dashed Line Represents the U–Ow Distribution (Ow = Water Oxygen). 

Reproduced With Permission From Greathouse, J.A., R.J. O’Brien, G. Bemis, and 

R.T. Pabalan. Journal of Physical Chemistry B. Vol. 106. p. 1,646. 2002. Copyright 

2002. American Chemical Society. 
3-49 

Figure 3.4-3. Equilibrium Snapshot (x, z Plane) of the Supercell Containing 300 Water 

Molecules and the Singly Protonated Quartz (010) Surface. The Dashed Line Represents 

the Supercell Boundaries, and Atomic Designations Are Si (Orange), O (Red), and 

H (White). Reproduced With Permission From Greathouse, J.A., R.J. O’Brien, G. Bemis, 

and R.T. Pabalan. Journal of Physical Chemistry B. Vol. 106. p. 1,646. 2002. 

Copyright 2002. American Chemical Society. 
3-50 

(a) (b) 

Figure 3.4-4. Results From the Simulation of UO2

2+ and 300 Water Molecules Near the 

Singly Protonated Quartz (010) Surface. (a) Equilibrium Snapshot (y, z Plane) of the 

Outer-Sphere Surface Complex [UO2(H2O)5]

2+, With Atomic Designations as in 

Figure 3.4-3 and U (blue). (b) Time Evolution of the O–U–O Vector Orientation to the 

Surface Normal. A Value of 90o Indicates That the Uranyl Ion Is Oriented Perpendicular to 

the Surface. Figure 4(b) Is Reproduced With Permission From Greathouse, J.A., 

R.J. O’Brien, G. Bemis, and R.T. Pabalan. Journal of Physical Chemistry B. Vol. 106. 

p. 1,646. 2002. Copyright 2002. American Chemical Society. 
3-51 

(a) (b) 

Figure 3.4-5. Equilibrium Snapshot (x,z Plane) of the Inner-Sphere Surface Complexes 

(a) [UO2(H2O)(OH)2{O}2] and (b) [UO2(H2O)2(CO3){O}2], Both Shown as Large Spheres. 

Nonsolvating Water Molecules and Other Quartz Atoms Are Shown as Sticks. Atomic 

Designations Are Si (Orange), O (Red), U (Blue), and H (White). 
3-52 

Figure 3.4-6. Equilibrium Trajectories (z-Direction) of Uranium Atoms in Inner-Sphere, 

Outer-Sphere, and Diffuse Uranyl Species. From Bottom to Top, the Three Uranyl 

Complexes Are [UO2(H2O)(OH)2{O}2], [UO2(H2O)5]

2+, and [UO2(H2O)3(OH)2]. Hydrogen 

Atoms at the Quartz Surface Have a z-Coordinate of Approximately 9.4 Å, so the Uranium 

z-Coordinates Shown Correspond to Average Distances of 1 Å, 4 Å, and 6 Å Above the 

Surface. Reproduced With Permission From Greathouse, J.A., R.J. O’Brien, G. Bemis, 

and R.T. Pabalan. Journal of Physical Chemistry B. Vol. 106. p. 1,646. 2002. 

Copyright 2002. American Chemical Society. 
3-53 

Table 3.2-1. Summary of Binary Ion-Exchange Experiments 

Experimental 

System 

Form of 

Zeolite Used 

Ionic 

Strength 

Number of 

Reference 

Solutions 

Number of 

Experimental 

Solutions 

K+/Cs+ K 0.05 N 6 18 

Ca2+/Sr2+ Ca 0.05 N 5 18 

K+/Sr2+ K 0.0005 N 1 17 

Na+/Cs+ Na 0.05 N 5 19 

K+/Na+ K 0.05 N 7 20 

K+/Ca2+ K 0.01 N 3 14 

Table 3.2-2. Summary of Ternary and Quaternary Ion-Exchange Experiments 

Experimental 

System 

Form of 

zeolite 

Used 

Ionic 

Strength 

Number of 

Reference 

Solutions 

Number of 

Experimental 

Solutions 

Na+/K+/Cs+ K 0.05 N 6 18 

Na+/K+/Cs+ Na 0.005 N 29 29 

Na+/K+/Ca2+ Na 0.05 N 29 29 

Na+/K+/Ca2+ Na 0.005 N 29 29 

Na+/K+/Sr2+ Na 0.05 N 29 29 

Na+/K+/Sr2+ Na 0.005 N 29 29 

Na+/K+/Ca2+/Sr2+ Na 0.05 N 39 39 
3-54 

Table 3.2-3. Model Parameters Derived From Regression of Ion-Exchange Data. Standard Errors in the 

Are in Parentheses. The Footnotes Give the Source of Experimental Data Used in Deriving the Mod

Exchange Reaction 

Total Solution 

Concentration 

(N, equiv./liter) 

Wilson Model Margu

Λ12

Λ21 ln K

A,B W

A 

Li+ + NaL 

⇔ Na+ + LiL 0.1* 0.439 (864) 2.277 (1967) 

−1.970 

(0.102) 

−0.353 

(0.121) 

0

(0

K+ + NaL 

⇔ Na+ + KL 

0.005† 0.225 (2174.) 4.450 (9675.) 3.337 (0.306) 

−2.179 

(0.383) 

−

(0

0.05† 0.719 (0.504) 3.449 (1.888) 3.571 (0.122) 

−1.844 

(0.346) 

−

(0

0.5† 2.628 (0.606) 1.042 (0.518) 3.195 (0.092) 

−0.860 

(0.454) 

−

(0

Cs+ + NaL 

⇔ Na+ + CsL 

0.02§ 

1.3051 

×10−6

(0.5859) 

6.5211 

(65) 

2.7386 

(8.9677) 

3.1204 

(0.4737) 

−2

(0

0.1* 1.2744 (1.3811) 30.4197 (564) 5.9805 (17.4) 

−3.3839 

(2.7445) 

−2

(0

1.0‡ 0.7157 (2951) 1.3985 (4129) 4.1248 (1.4932) 

−4.5831 

(2.0340) 

−1

(0

0.02§, 0.1*, 

1.0‡ 

1.837 

(2.028) 

2.713 

(11.428) 

4.388 

(2.488) 

−1.666 

(4.906) 

−

(1

Cs+ + KL 

⇔ K+ + CsL 

0.1* 8.975 (9.73) 0.807 (0.804) 

−0.178 

(0.677) 

−1.197 

(0.643) 

−

(0

0.1║ 1.614 (0.504) 1.637 (0.465) 1.474 (0.073) 

−1.108 

(0.193) 

−

(0

0.1*,║ 0.876 (0.738) 3.013 (1.339) 0.968 (0.144) 

−1.960 

(0.457) 

−

(0

Cs+ + RbL 

⇔Rb+ + CsL 0.1* 2.7079 (0.3987) 1.7319 (0.2456) 

−0.4303 

(0.0545) 

−1.5890 

(0.1253) 

−2

(0

NH4

+ + NaL 

⇔ Na+ + NH4L 

0.1¶ 1.8350 (1.9340) 0.7923 (1.3641) 1.5894 (0.1500) 

−0.1781 

(0.4859) 

−0

(0

0.1# 3.0661 (237) 0.3272 (77) 1.5476 (0.1139) 0.2536 (0.2173) 

−1

(0

0.1 ¶,# 2.504 (2.901) 0.473 (1.277) 1.564 (0.087) 0.103 (0.213) 

−

(0
3-55 

Table 3.2-3. Model Parameters Derived From Regression of Ion-Exchange Data. Standard Errors in the 

Are in Parentheses. The Footnotes Give the Source of Experimental Data Used in Deriving t

Parameters (continued).

Exchange Reaction 

Total Solution 

Concentration 

(N, equiv./liter) 

Wilson Model Margu

Λ12

Λ21 ln K

A,B W

A 

NH4

+ + KL 

⇔ K+ + NH4L 0.1¶ 4.732 (2312) 0.212 (489) 

−0.901 

(0.303) 

−0.312 

(0.735) 

−

(0

2NH4

+ + CaL 

⇔ Ca2+ + NH4L2

0.1¶ 3.472 (108) 0.290 (31.165) 5.826 (0.243) 0.298 (0.549) 

−

(0

0.05† 0.785 (0.221) 4.151 (0.518) 

−1.615 

(0.063) 

−2.340 

(0.086) 

−

(0

0.5† 1.821 (0.324) 3.420 (0.185) 

−1.863 

(0.075) 

−2.718 

(0.708) 

−

(0

Ca2+ + 2KL 

⇔ 2K+ + CaL2 0.05** 0.164 (54) 6.099 (328) 

−8.739 

(0.057) 

−2.364 

(0.201) 

−

(0

Sr2+ + 2NaL 

⇔ 2Na+ + SrL2

0.005** 6.814 (1.697) 1.664 (0.214) 

−1.911 

(0.161) 

−2.204 

(0.135) 

−

(0

0.05** 4.807 (0.712) 2.454 (0.187) 

−1.231 

(0.099) 

−2.682 

(0.090) 

−

(0

0.5** 4.108 (0.801) 3.373 (0.169) 

−1.112 

(0.125) 

−3.371 

(0.087) 

−

(0

Sr2+ + 2KL 

⇔ 2K+ + SrL2 0.05** 0.225 (52.1) 4.452 (231.6) 

−6.385 

(0.069) 

−1.477 

(0.131) 

0

(0

Sr2+ + CaL2 ⇔ Ca2+ + SrL2 1.0†† 0.820 (0.584) 5.612 (1.257) 0.342 (0.222) 

−3.728 

(0.296) 

0

(0

*Chelischev et al. (1973) †Pabalan (1994) ‡Ames (1964a) §Howery and Thomas (1965) ║Ames (1968) ¶Jama and Yucel (1990) #Townsend and Loizidou (1984) **Pabalan and Bertetti (1999) ††Ames 1964b 
3-56 

Table 3.4-1. Results for Simulations in Bulk Water* 

Uranyl Complex

Interatomic Distance (Å)

U-OL U-Ow

[UO2(H2O)5]

2+ – 2.51 

[UO2(H2O)4(OH)]+ 2.31 2.53 

[UO2(H2O)3(OH)2] 2.33 2.53 

[UO2(H2O)3(CO3)] 2.37 2.51 

[UO2(H2O)(CO3)2]

2–

 2.39 2.51 

*U–O and U–Si distances (Å) from peaks in the averaged RDFs for ligand (OL) and water (Ow)

Table 3.4-2. Results for Simulations Near the Singly Protonated Quartz (010) Surface*

Uranyl Complex

Interatomic Distance (Å)

U-OL U-Ow U-Si

[UO2(H2O)5]

2+ – 2.49 6.35 

[UO2(H2O)4(OH)]+ 2.31 2.53 8.13 

[UO2(H2O)3(OH)2] 2.33 2.55 8.06 

[UO2(H2O)3(CO3)] 2.37 2.51 8.25 

[UO2(H2O)(CO3)2]

2–

 2.37 2.51 8.03 

*U-O and U-Si distances (Å) from peaks in the averaged RDFs. Oxygen atoms from ligand (hydroxide or 

carbonate), and water are denoted by OL and Ow, respectively.

 

Table 3.4-3. Results for Simulations Near the Partially Deprotonated 

Quartz (010) Surface* 

Uranyl Complex†

Interatomic Distance (Å)

U-OL U-Ow U-Os U-Si

[UO2(H2O)4{O}]2+ – 2.50 2.50 3.98 

[UO2(H2O)2(OH){O}2]

+

 2.35 2.55 2.55 4.06 

[UO2(H2O) (OH)2{O}2] 2.33 2.53 2.53, 2.91 4.46 

[UO2(H2O)2(CO3){O}] 2.35 2.49 2.49 3.79, 4.55 

[UO2(H2O) (CO3)2]

2–

 2.38 2.55 2.91 4.49 

*U-O and U-Si distances (Å) from peaks in the averaged RDFs. Oxygen atoms from ligand (hydroxide or 

carbonate), water, and quartz surface are denoted by OL, Ow, and OS, respectively.

†Surface oxygen atoms in the uranium coordination shell are denoted by {O}. Charge contributions due to {O} 

were not included in determining the overall charge of the complex.
4-1 

4 IMPLEMENTING SORPTION MODELING APPROACHES IN 

PERFORMANCE ASSESSMENT 

4.1 Groundwater Chemistry Considerations 

Results of sorption experiments on silicate, oxide, and carbonate minerals suggest that 

groundwater chemistry plays a primary role in determining the magnitude of surface 

complexation-related sorption (i.e., excluding ion-exchange reactions). For a particular element, 

differences in sorption behavior due to differences in the surface characteristics of minerals are 

minimal. The specific surface area of the mineral does influence sorption (with larger surface 

areas generally exhibiting greater sorption), but as discussed in Section 2.2, the surface area 

effects are similar across many mineral types. 

The pH, carbonate content (or PCO2), and redox conditions of solutions are most important in 

driving sorption behavior for most elements. The sorption of many contaminants, including 

radionuclides, is particularly sensitive to variations in pH. For actinides, sensitivity to pH values 

in terms of sorption is related to their typically complicated hydrolysis behavior. The 

concentration of carbonate species in groundwater also has a pronounced effect on 

complexation and sorption of actinides, particularly at pH values above about 7 (Bertetti, et al., 

1998; Pabalan and Turner, 1997; Davis, 2001). The oxidation states of many metals and 

actinides are affected by local redox conditions in groundwater systems. For instance, 

technetium, neptunium, and uranium are less mobile under reducing conditions and more 

mobile when oxidized. Conversely, iron is more mobile when reduced and less mobile 

when oxidized. Work in the Center for Nuclear Waste Regulatory Analyses (CNWRA®) 

sorption program focused on oxidizing conditions because, as confirmed by field 

sampling (e.g., Bertetti, et al., 2004), those conditions predominated throughout the 

Yucca Mountain system. 

For performance assessment modeling, an appropriate range of groundwater chemistry 

conditions must be identified to reasonably model the expected range of sorption values for 

contaminants of interest. As part of the development of a performance assessment model for 

Yucca Mountain, Turner, et al. (1998) screened and culled the water chemistry dataset of 

Perfect, et al. (1995) to represent the groundwater chemistry at Yucca Mountain. Data later 

developed as part of the Nye County Early Warning Drilling Program and additional 

U.S. Department of Energy (DOE) characterization efforts were assembled and analyzed in 

subsequent reports (Bertetti, et al., 2004; McMurry and Bertetti, 2005) to produce a site-specific 

dataset pertinent to the region of interest for radionuclide transport in groundwater at 

Yucca Mountain. 

The important parameters pH and PCO2 were used from the site-specific dataset to provide a 

characterization of Yucca Mountain groundwater chemistry in the performance assessment 

model (Figure 4.1-1) (McMurry and Bertetti, 2005). The two parameters were statistically 

analyzed and compared to groundwater data collected from the unsaturated zone 

(Browning and Murphy, 2002) to develop correlation and distribution functions for use in 

modeling actinide sorption (McMurry and Bertetti, 2005). These distribution functions were then 

sampled within the performance assessment code to calculate sorption for the actinides 

americium, neptunium, plutonium, thorium, and uranium as described in Section 4.5. 

Although the pH and PCO2 data were primarily used to inform the development of the 

U.S. Nuclear Regulatory Commission (NRC) performance assessment code, they were also 
4-2 

useful in developing a spatial representation of the potential for radionuclide sorption in the 

Yucca Mountain region (Figure 4.1-2). Because the NRC performance assessment code did 

not explicitly represent spatial variations in groundwater chemistry, maps such as that 

exemplified in Figure 4.1-2 can provide confidence in the model representation of sorption along 

expected flow paths. 

4.1.1 References 

Bertetti, F.P., J. Prikryl, and B. Werling. “Development of Updated Total-System Performance 

Assessment Parameter Distributions for Radionuclide Transport in the Saturated Zone.” 

San Antonio, Texas: CNWRA. 2004. 

Bertetti, F.P., R.T. Pabalan, and M.G. Almendarez. “Studies of NeptuniumV

 Sorption on 

Quartz, Clinoptilolite, Montmorillonite, and α-alumina. Adsorption of Metals by Geomedia. 

E.A. Jenne, ed. New York City, New York: Academic Press, Inc. pp. 131–148. 1998. 

Browning, L. and W.M. Murphy. “Revised Analytical Compositions of Pore Water Collected 

from Yucca Mountain, Nevada, and Vicinity”. San Antonio, Texas: CNWRA. 2002.

Davis, J.A. NUREG/CR–6708, “Surface Complexation Modeling of Uranium(VI) Adsorption on 

Natural Mineral Assemblages. Washington, DC: NRC. 2001. 

McMurry, J. and F.P. Bertetti. “Selection of Sorption-Related Values for Unsaturated Zone and 

Saturated Zone Transport in Total-System Performance Assessment.” CNWRA Letter Report. 

San Antonio, Texas: CNWRA. June 2005.

Pabalan, R.T. and D.R. Turner. “Uranium(6+) Sorption on Montmorillonite: Experimental and 

Surface Complexation Modeling Study.” Aqueous Geochemistry. Vol. 2. pp 203–226. 1997. 

Perfect, D.L., C.C. Faunt, W.C. Steinkampf, and A.K. Turner. “Hydrochemical Data Base for 

the Death Valley Region, California and Nevada. U.S. Geological Survey Open-File 

Report 94-305. 1995. 

Turner, D.R., R.T. Pabalan, J.D. Prikryl, and F.P. Bertetti. “Radionuclide Sorption at Yucca 

Mountain, Nevada–A Demonstration of an Alternate Approach for Performance Assessment.” 

Symposium on the Scientific Basis for Nuclear Waste Management-XXII, Materials Research 

Society Fall Meeting Paper, Boston, Massachusetts, November 30–December 3, 1998. 

Warrendale, Pennsylvania: Materials Research Society. 1998. 

4.2 Empirical Models—Deterministic 

4.2.1 Evaluating Kd Values for Deterministic Performance Assessments—

An Example 

The simplest approach to implementing sorption in radionuclide transport models supporting 

performance assessment is to develop preferred Kd values from available studies and apply 

them as single values (i.e., deterministically). The values must be defined for given spatial 

domains in the model (e.g., strata and lateral position) and so are preferably developed with the 

specific sorbing media and geochemical conditions for that domain in mind. The single-value, 

constant Kd values are then used in the advection-dispersion expression governing transport 

within that domain. CNWRA staff developed this sort of dataset as part of their charter 
4-3 

program Technical Assistance in Evaluating Non-High-Level Waste Determinations for the DOE 

in South Carolina and Idaho. In this program, CNWRA is providing technical support to NRC 

staff reviews of performance assessments designed to demonstrate compliance with 

performance objectives for retaining residual waste in high-level waste tanks and for disposing 

of salt wastes in the near surface. (NRC has a consultative, rather than regulatory, role in these 

reviews). This program is not directly related to Yucca Mountain, but as a part of the CNWRA 

charter program, it provides insights into how different chemical environments affect 

radionuclide sorption in engineered systems. 

Prikryl and Pickett (2007) were tasked with recommending reasonable Kd values for various 

radioelements for the natural system at two general locations potentially subject to NRC waste 

determinations reviews: the Savannah River Site and Idaho National Laboratory. (We use the 

term “radioelement” here because the sorption coefficient is a chemical parameter that would, in 

general, apply to all isotopes of an element.) The report was mainly intended to be useful as a 

review tool for the NRC and CNWRA staffs in evaluating Kd single values or probability 

distributions used in DOE performance assessments supporting waste determinations. The 

output of the report was a set of single recommended Kd values for each important radioelement 

in (i) each relevant hydrostratigraphic unit at Savannah River Site and (ii) each relevant aquifer 

rock type at Idaho National Laboratory. For example, values were recommended for the 

Gordon Aquifer at Savannah River Site and for alluvium at Idaho National Laboratory. The 

report also often included ranges of values the authors considered reasonable, but did not 

recommend that those ranges explicitly be used to develop probability distributions. The 

radioelements covered in the report were carbon, nickel, selenium, strontium, niobium, 

technetium, tin, iodine, cesium, lead, uranium, neptunium, plutonium, americium, and curium. 

The authors surveyed project reports and the literature for site-specific experimental data and 

considered other sources when site-specific data were lacking or inadequate. To paraphrase 

Prikryl and Pickett (2007, Section 1.3), the analysis of sorption coefficients followed this 

procedure for each site: 

• The mineralogical and hydrogeochemical characteristics of subsurface units along 

transport pathways were described. 

• The radioelements of concern were designated. 

• For each radioelement the chemistry and important sorptive characteristics of the 

radioelement were described. 

• Site-specific measurements of Kd values for the various transport media and/or 

measurements of Kd values under analogous conditions were summarized. 

• Literature-based reference Kd estimates and recommendations for previous 

assessments at the site were reviewed. 

• Kd values from compendia of sediment and rock types comparable to subsurface units at 

the site were discussed (e.g., Sheppard and Thibault, 1990; Looney, et al., 1987). 

• Based on the data review, a recommended Kd value was designated. 

The report went on to discuss how Kd measurements could be evaluated and compared 

(e.g., batch versus column versus in situ) and discuss the general effects of environmental 
4-4 

factors (e.g., pH and clay content) on sorption behavior. The report also made the following 

point about conservatism (Prikryl and Pickett, 2007, p. 1–2): 

Because these lithologic and hydrogeochemical factors are not always well 

constrained for a given transport model, it is often necessary to make 

conservative selections of Kd values that are at the low end of reasonable 

ranges. In this way, the model is less likely to overpredict the favorable effects of 

sorption. Such an approach is useful in the case of spatial and temporal 

variation. This approach also addresses the uncertainties inherent in the Kd

values themselves, apart from environmental uncertainty and variation. 

This statement about conservatism, generally accepted by the scientific community at the time, 

applies only to transport and minimum time of arrival of the radionuclide of interest at a specified 

compliance boundary. The adoption of low Kd values may not necessarily be conservative with 

respect to dose when other factors such as the effects of daughter ingrowth are considered. 

Moreover, spatial and temporal variation in water chemistry and other factors influencing 

sorption may result in significant variations in the spatial and temporal concentrations of the 

radionuclide of interest. These variations may result in unexpected effects on dose that are not 

well described by the use of single Kd values. The full range of expected Kd values for each 

radionuclide, which affects the expected time of transport, and the concentrations of 

radionuclides at the receptor location should be considered before an assumption of 

conservatism is associated with use of low Kd values. 

4.2.2 References 

Looney, B.B., M.W. Grant, and C.M. King. “Estimation of Geochemical Parameters for 

Assessing Subsurface Transport at the Savannah River Plant.” Environmental Information 

Document DPST–85–904. Aiken, South Carolina: E.I. du Pont de Nemours and Company, 

Savannah River Laboratory. 1987. 

Prikryl, J.D. and D.A. Pickett. “Recommended Site-Specific Sorption Coefficients for Reviewing 

Non-High-Level Waste Determinations at the Savannah River Site and Idaho National 

Laboratory.” San Antonio, Texas: CNWRA. 2007. 

Sheppard, M.I. and D.H. Thibault. “Default Soil Solid/liquid Partition Coefficients, Kds, for Four 

Major Soil Types: A Compendium.” Health Physics. Vol. 59, No. 4. pp. 471–482. 1990. 

4.3 Using Site-Specific Groundwater Chemistry To Develop 

Probability Density Function Statistics 

After evaluating the different surface complexation model approaches, CNWRA staff focused on 

using a relatively simple DLM to model sorption for the range in water chemistries at 

Yucca Mountain (Turner, et al., 1998a; Turner and Pabalan, 1999). The initial effort focused on 

a regional water chemistry database Perfect, et al. (1995) developed. 

As described previously, this modeling approach used several assumptions, including 

the following 

• Sorption behavior as a function of pH and carbonate concentration is similar for 

aluminosilicate minerals when expressed in terms of KA,eff = Kd/Aeff, where Aeff is the 

effective surface area (BET surface area in the case of nonporous minerals, 10 percent 
4-5 

of BET in the case of sheet minerals such as montmorillonite, gibbsite, and kaolinite). 

Also implicit in this assumption is that aluminosilicate minerals will dominate sorption in 

the tuff units at Yucca Mountain. 

• The effect outlined previously has been demonstrated for U(VI) and Np(V) 

(Pabalan, et al., 1998; Bertetti, et al., 1998). It is assumed that it is also true for other 

actinides such as Am(III), Pu(V), and Th(IV). 

• The water chemistries of Perfect, et al. (1995) as screened in Turner, et al. (1998a) 

represent the likely range in water chemistry at Yucca Mountain. These data were 

subsequently modified as the Nye County Early Warning Drilling Program and DOE 

developed additional water chemistry data for the Fortymile Wash region (McMurry and 

Bertetti, 2005). 

4.3.1 Steps in Applying the Model 

The steps involved in this modeling exercise include the following 

• Identify sorption experiments that can be used to calibrate the diffuse 

layer model parameters. We used the sorption experiments of Righetto, et al. 

(1991, 1988) for Am(III), Th(IV), and Pu(V) sorption on γ-alumina and those of 

Turner, et al. (1998b) and Pabalan and Turner (1997)] for Np(V) and U(VI) sorption on 

montmorillonite. Diffuse layer model parameters were determined assuming Aeff = 

10 percent of BET measured area for both γ-alumina (BET = 130 m2

/g; Aeff = 13.0 m2/g) 

and montmorillonite (BET = 97 m2

/g; Aeff = 9.7 m2/g). 

• Determine the diffuse layer model parameters for these experiments. 

• The diffuse layer model parameters in Turner, et al. (1998b) and Pabalan and Turner 

(1997) were used for Np(V)- and U(VI)-montmorillonite. Diffuse layer model parameters 

for Am(III)-, Pu(V)-, and Th(IV)-γ-alumina were developed in Turner (1995) for γ-alumina 

(130 m2

/g). We reinterpreted the data using Aeff = 13.0 m2/g and the Nuclear Energy 

Agency thermodynamic database for americium, plutonium, neptunium, and uranium. 

• With the parameters developed, the diffuse layer model was applied to the 460 analyses 

in the culled Perfect, et al. (1995) database (Turner, et al., 1998a). Running these 

models allows generation of sorption coefficients for the observed ranges in water 

chemistry for the five radionuclide-mineral systems considered here. Normalized to 

effective surface area KA,eff of the minerals is used in the calibration experiments, and 

assuming that the pH- and PCO2-dependent sorption behavior is similar for 

aluminosilicates, these sorption coefficient distributions were then recast in terms of Kd

for each of the hydrostratigraphic units used in the TPA code. 

• The distributions for each radionuclide appeared to be lognormal, although the kurtosis 

of the distributions varies (Turner, et al., 1998a; Turner and Pabalan, 1999). The final 

step in using this information in TPA is to apply this distribution to each 

hydrostratigraphic unit and transform the KA,eff into Kd (in mL/g). Assuming that 

aluminosilicates will dominate sorption in these units, it follows that the 

chemical-dependent sorption behavior, and therefore these distributions, should hold for 

each unit. The key parameter for each transform to KA,eff will therefore be the effective 

surface area. 
4-6 

Note that for a given radionuclide, the method used here results in the same distribution for 

each hydrostratigraphic unit, because Kd is determined by multiplying KA,eff by a unit-specific 

constant (Aeff). It is also possible to use this information to develop correlation coefficients 

between each of the radionuclides. 

4.3.2 Correlations Among Radionuclides 

In addition to constraining probability distribution functions for a given radioelement, the 

application of mechanistic sorption models offers a way of explicitly developing a multivariate 

correlation among sorption parameter PDFs that reflects the underlying effects of geochemistry 

on sorption. For example, calculating a distribution for Np(V)- and U(VI)-montmorillonite sorption 

can provide a direct means of testing the bivariate correlation between Np(V) and U(VI) sorption 

that results from the effects of chemical variability. The relationship is not 1:1 due to differences 

in the aqueous speciation that control sorption of Np(V) and U(VI). 

Using this method, the correlation coefficient for log Kd calculated for Np(V) and U(VI) is about 

0.6, indicating a positive correlation in sorption behavior for these two actinides due to 

geochemical effects. The correlation is weak (r2

 = 0.4), however, due to the different speciation 

behavior of these two actinides, and is also likely influenced by a few outlier points. 

Nevertheless, correlation coefficients calculated in a similar manner for multiple pairings of 

radioelements can be included as input into the performance assessment sampling routine; in 

this way, the value selected for one radioelement sorption parameter is conditioned by its 

statistical (and therefore geochemical) relationship to the other radioelements. These types of 

correlations can also be used to examine the justification for using chemical analogues in 

performance assessment to supplement limited sorption data for some radionuclides. 

4.3.3 Caveats to the Groundwater Chemistry Modeling Approach 

Some caveats should be kept in mind when using this approach: 

• The models were calibrated based on a limited number of single mineral, end member, 

batch experiments. This is particularly true for Am(III), Th(IV), and Pu(V) where only a 

single set of experimental conditions has been used to calibrate and test the model. 

• A simplified diffuse layer model was used to generate the sorption coefficients. 

Competitive sorption reactions are not included in the model, although there is 

allowance for aqueous speciation of the radionuclide of interest with ligands 

(e.g., CO3

2−

, F−, Cl−, and SO4

2−

). 

• In the absence of detailed site-specific information, many of the diffuse layer model 

parameters were assumed. These include M/V, radionuclide concentration, and site 

density (Turner and Sassman, 1996). 

• In converting the KA,eff values to Kd, empirical measures of specific surface areas were 

assumed based on porosity, density, and pore aperture. These surface areas were 

expressed in terms of m2

/g and do not explicitly take into account the potential effects of 

conditions such as wetted surface area and dead-end pore space. The surface areas 

are not based on site-specific measured surface area and for this reason should be used 

with caution. 
4-7 

• Other than assuming that the minerals were some type of aluminosilicate, no specific 

mineral was assumed. Although mineral-specific experiments were used in calibrating 

the models and in developing the KA,eff distributions, the empirical surface area 

presumed no specific mineral type. 

• Many of the Kds calculated here were too high to be reliably measured in experiments 

[e.g., Kd for Am(III) up to 1010 mL/g]. 

• Given these caveats, retardation predicted using these parameters is best viewed as a 

“defense-in-depth” strategy to reduce uncertainty in parts of the transport codes. 

4.3.4 References (Section 4.3) 

Bertetti, F.P., R.T. Pabalan, and M.G. Almendarez. “Studies of NeptuniumV Sorption on 

Quartz, Clinoptilolite, Montmorillonite, and α-alumina. Adsorption of Metals by Geomedia. 

E.A. Jenne, ed. New York City, New York: Academic Press, Inc. pp. 131–148. 1998. 

McMurry, J. and F.P. Bertetti. “Selection of Sorption-Related Values for Unsaturated Zone and 

Saturated Zone Transport in Total-System Performance Assessment.” San Antonio, Texas: 

CNWRA. 2005. 

Pabalan, R.T., D.R. Turner, F.P. Bertetti, and J.D. Prikryl. “UraniumVI Sorption onto 

Selected Mineral Surfaces.” Adsorption of Metals by Geomedia. E.A. Jenne, ed. New York 

City, New York: Academic Press, Inc. pp. 99−130. 1998. 

Pabalan, R.T. and D.R. Turner. “Uranium(6+) Sorption on Montmorillonite: Experimental and 

Surface Complexation Modeling Study.” Aqueous Geochemistry. Vol. 2. pp. 203-226. 1997. 

Perfect, D.L., C.C. Faunt, W.C. Steinkampf, and A.K. Turner. “Hydrochemical Data Base for 

the Death Valley Region, California and Nevada. U.S. Geological Survey Open-File 

Report 94-305. 1995. 

Righetto, L., G. Bidoglio, B. Marcandalli, and I.R. Bellobono. “Surface Interactions of Actinides 

With Alumina Colloids.” Radiochimica Acta. Vol. 44/45. pp. 73–75. 1988. 

Righetto, L., G. Bidoglio, G. Azimonti, and I.R. Bellobono. “Competitive Actinide Interactions in 

Colloidal Humic Acid-Mineral Oxide Systems.” Environmental Science and Technology. 

Vol. 25. pp. 1,913–1,919. 1991. 

Turner, D.R. “A Uniform Approach to Surface Complexation Modeling of Radionuclide 

Sorption.” CNWRA 95–001. San Antonio, Texas: CNWRA. 1995. 

Turner, D.R. and S.A. Sassman. “Approaches to Sorption Modeling for High-Level Waste 

Performance Assessment.” Journal of Contaminant Hydrology. Vol. 21 pp. 311–332. 1996. 

Turner, D.R. and R.T. Pabalan. “Abstraction of Mechanistic Sorption Model Results for 

Performance Assessment Calculations at Yucca Mountain, Nevada. Waste Management. 

Vol. 19. pp. 375–388. 1999. 

Turner, D.R., R.T. Pabalan, J.D. Prikryl, and F.P. Bertetti. “Radionuclide Sorption at Yucca 

Mountain, Nevada–A Demonstration of an Alternate Approach for Performance Assessment.” 
4-8 

Symposium on the Scientific Basis for Nuclear Waste Management-XXII, Materials Research 

Society Fall Meeting Paper, Boston, Massachusetts, November 30–December 3, 1998. 

Warrendale, Pennsylvania: Materials Research Society. 1998a. 

Turner, D.R., R.T. Pabalan, and F.P. Bertetti. “Neptunium(V) Sorption on Montmorillonite: An 

Experimental and Surface Complexation Modeling Study.” Clays and Clay Minerals. Vol. 46. 

pp. 256−269. 1998b, 

4.4 Thermodynamic Model—Response Surface 

4.4.1 Description of a Response Surface Approach 

As described previously in Section 1.3, another method the CNWRA staff used to abstract 

detailed geochemical sorption models for the purposes of performance assessment was 

to develop sorption response surfaces using a simplified diffuse layer model approach 

(Figure 4.4-1) that correlated Kd values with variations in pH and PCO2 (Turner, et al., 2006, 

2002; McMurry and Bertetti, 2005). After calibrating the model and running it over a wide range 

in geochemical conditions, the simulated sorption curve for discrete values was parameterized 

for the purposes of interpolating geochemical conditions using different polynomial expressions 

(Figure 4.4-2). The sorption response surfaces were for the actinides americium, neptunium, 

plutonium, thorium, and uranium; surface complexation models were applied over a wide range 

of geochemical conditions to generate a set of effective surface area normalized distribution 

coefficients (KA,eff) (Turner, et al., 2006, 2002), which are in turn converted to Kd values using 

the specific surface area properties of the hydrostratigraphic layers. Probability distribution 

functions for the key geochemical parameters pH and PCO2 were developed based on 

site-specific hydrochemical data (McMurry and Bertetti, 2005; Bertetti, et al., 2004) 

In a stochastic performance assessment simulation, each realization samples specific 

correlated values for pH and PCO2 that, in turn, are used to select the appropriate KA,eff from the 

response surface lookup table. Although the approach does not explicitly incorporate 

geochemistry in the performance assessment transport calculations, it correlates the calculation 

of Kd values with two key geochemical parameters that affect sorption behavior, providing a 

stronger technical basis for the treatment of transport for these five significant actinides. In any 

particular realization of the code, the Kd values for the actinides are calculated separately, but 

the calculations for all five are consistent because each value is associated with the same 

specified pH and PCO2 parameter values (Figure 4.4-3). This approach was used in developing 

the sorption coefficients for TPA Version 5.1 (Durham, et al., 2007). 

4.4.2 References (Section 4.4) 

Bertetti, F.P., J. Prikryl, and B. Werling. “Development of Updated Total-System Performance 

Assessment Parameter Distributions for Radionuclide Transport in the Saturated Zone.” 

San Antonio, Texas: CNWRA. 2004. 

Durham, J., C. Grossman, and B. Leslie. “Total-System Performance Assessment (TPA) 

Version 5.1 Code Module Descriptions And User Guide.” San Antonio, Texas: CNWRA. 

July 2007. 

Hsi, C-K.D. and D. Langmuir. “Adsorption of Uranyl Onto Ferric Oxyhydroxides: Application of

the Surface Complexation Site-Binding Model.” Geochimica et Cosmochimica Acta. Vol. 49, 

No. 1. pp. 931–1,941. 1984 
4-9 

McMurry, J. and F.P. Bertetti. “Selection of Sorption-Related Values For Unsaturated Zone and 

Saturated Zone Transport In Total-System Performance Assessment.” CNWRA Letter Report. 

San Antonio, Texas: CNWRA. June 2005. 

Turner, D.R., F.P. Bertetti, and R.T. Pabalan. “The Role of Radionuclide Sorption in High-Level 

Waste Performance Assessment: Approaches for the Abstraction of Detailed Models. Soil 

Science Society of America Proceedings Volume: Soil Geochemical Processes of 

Radionuclides. Pittsburgh, Pennsylvania: Soil Science of America. pp. 211–252. 2002. 

Turner, D.R., R.T. Pabalan, and F.P. Bertetti. “Applying Surface Complexation Modeling to 

Radionuclide Sorption.” Surface Complexation Modeling. F. Lützenkirchen, ed. Amsterdam: 

Elsevier, Ltd. pp. 553–604. 2006. 

Turner, D.R., R.T. Pabalan and F.P. Bertetti. “Neptunium(V) Sorption on Montmorillonite: An 

Experimental and Surface Complexation Modeling Study.” Clays and Clay Minerals. Vol. 46. 

pp. 256−269. 1998. 

4.5 In-Package Radionuclide Transport in Total System 

Performance Assessment 

In reviewing the license application for the proposed repository at Yucca Mountain, and in 

prelicensing interactions, the CNWRA and NRC staffs recognized the potential risk significance 

of actinide sorption onto surfaces inside the waste package. This process was not included in 

the independent NRC performance assessment, but DOE did implement it in Total System 

Performance Assessment. The modeled in-package sorptive medium was the surface of iron 

oxyhydroxides resulting from the corrosion of steel components, such as the basket holding fuel 

assemblies and the surface of the stainless steel internal container. 

In Total-system Performance Assessment, DOE modeled actinide sorption on corrosion 

products using a surface complexation model to develop effective Kds appropriate for the 

modeled chemical conditions. The Kds were then used in a mixing cell model to simulate 

equilibrium delay of release of some actinides (uranium, neptunium, and thorium) from the 

waste package. In contrast, kinetic reversible sorption onto corrosion products was modeled for 

americium and plutonium, also based on the Kds derived from the surface complexation model. 

To develop the Kd distributions used in Total-system Performance Assessment, DOE performed 

multiple surface complexation model simulations intended to represent the range of (i) surface 

properties and (ii) potential water chemistries inside the package and used multiple regressions 

to produce functions that calculated actinide sorption as a function of key geochemical 

properties. In this way, DOE sought to incorporate into the Kd distributions uncertainty for 

appropriate key geochemical properties, such as pH and PCO2, and surface properties related to 

sorption site concentration, such as site density, surface area, and solid mass. 

There is not an abundance of experimental evidence regarding the sorption of risk-significant 

radionuclides such as plutonium onto steel corrosion products. DOE, therefore, compared 

its modeled Kd distributions to ranges of soil data compiled by the U.S. Environmental 

Protection Agency. 
4-10 

Figure 4.1-1. Plot of Water Chemistry Data From Wells (107 Samples) Located Within the 

Site-Scale Model area in the Yucca Mountain Vicinity (McMurry and Bertetti, 2005). The 

95 Percent Confidence Ellipse (Blue Line) Is Shown for the pH and CO2 Data. The Red 

Bar Indicates the Range of pH Values From an Analysis of Unsaturated Zone Waters 

(103 Samples) From Yucca Mountain (Browning and Murphy, 2002), While the Red 

Diamond Indicates the Median pH Value From the Same Study. 
4-11 

Figure 4.1-2. Map of Calculated Neptunium KA,eff (See Section 2.4.3) Sorption Values for 

Selected Wells Plotted Along With a Geostatistical Prediction Map of Neptunium KA,eff

Sorption Values for the Yucca Mountain Region 
4-12 

Figure 4.4-1. Developing a Response Surface Using the Simplified Diffuse-Layer 

Model Approach. (a) U(VI)-Goethtite Sorption Data of Hsi and Langmuir (1985) 

Used to Calibrate Model. (b) Sorption (Kd) Expressed as a Function of pH and M/V 

as a Surrogate for Site Concentration. (c) Sorption (Kd) Expressed as a Function 

of pH and Inorganic Carbon. Reproduced with Permission From. Turner, D.R., 

F.P. Bertetti, and R.T. Pabalan. “Role of Radionuclide Sorption in High-Level 

Waste Performance Assessment: Approaches for the Abstraction of Detailed 

Models.” Soil Science Society of America Special Publication No. 59. 2002. 

Copyright 2002. Soil Science Society of America, Inc. 

0

500000

1000000

1500000

2000000

2500000

0

1

2

3

4

5

4 3 6 5 8 7 9

Kd (m

L/g)

M/V (g/L)

pH

0

4000

8000

12000

16000

20000

0

100200300 400 500

4 3

6 5 8 7 9

Kd (m

L/g)

CT

(ppm)

pH

(b) Predicted Effects of pH, M/V

U(VI)T=10-5 M

CT=150 ppm

U(VI)T=10-5 M

M/V=0.01 g/L

(c) Predicted effects of pH, CT

pH

3456789

Log K

d (mL/g)

-2

0

2

4

6

8 (a) Model Calibration

CT = 0.0 M

CT = 0.001 M

CT = 0.01 M

Data from Hsi and Langmuir (1985)
4-13 

Figure 4.4-2. Developing Parametric Expressions to Simulate 

Np(V)-Montmorillonite Sorption Surfaces for a Range in PCO2 From 0 to 10−2 atm. 

Simplified Diffusive-Layer Model Calibrated Using Np(V)-Montmorillonite Sorption 

Data (Turner, et al., 1998). Reproduced with Permission From. Turner, D.R., 

F.P. Bertetti, and R.T. Pabalan. “Role of Radionuclide Sorption in High-Level 

Waste Performance Assessment: Approaches for the Abstraction of Detailed 

Models.” Soil Science Society of America Special Publication No. 59. 2002. 

Copyright 2002. Soil Science Society of America, Inc.

pH

5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5

Kaf (mL.

m-2)

10-1

100

101

102

10-2.0

pH

5 6 7 8 9 10

Kaf (mL.

m-2)

10-1

100

101

102

10-3.5

pH

5 6 7 8 9 10 11

Kaf (mL.

m-2)

10-1

100

101

102

10-5.0

pH

6 7 8 9 10 11 12

Kaf (mL.

m-2) 10-1

100

101

102

10-7.0

pH

5 6 7 8 9 10 11 12

Kaf (mL.

m-2)

10-1

100

101

102 no CO2
4-14 

Figure 4.4-3. Flow Diagram for Implementing a Sorption Response Surface in 

Performance Assessment. Reproduced with Permission from. Turner, D.R., 

F.P. Bertetti, and R.T. Pabalan. “Role of Radionuclide Sorption in High-Level 

Waste Performance Assessment: Approaches for the Abstraction of Detailed 

Models.” Soil Science Society of America Special Publication No. 59. 2002. 

Copyright 2002. Soil Science Society of America, Inc. 
5-1 

5 SUMMARY AND CONCLUSIONS 

5.1 Summary 

The potential release of radionuclides as dissolved constituents in groundwater is a primary 

concern in performance assessment of proposed high-level nuclear waste geologic repositories. 

Sorption onto mineral surfaces is an important mechanism for reducing radionuclide 

concentrations along groundwater flow paths and retarding radionuclide migration to a receptor 

location [such as the reasonably maximally exposed individual (RMEI)]. The Center for Nuclear 

Waste Regulatory Analyses (CNWRA®) has been engaged in studying radionuclide sorption 

processes for more than 20 years, including experimental analysis, process modeling, and 

abstracting detailed sorption models for performance assessment calculations. 

This knowledge capture report summaries some of the technical assistance work undertaken on 

behalf of the U.S. Nuclear Regulatory Commission (NRC). In addition, it provides as an 

attachment a listing of journal articles, reports, abstracts, and scientific notebooks the CNWRA 

staff produced since 1990 to document the results of this work. 

5.2 Key Uncertainties and Future Work 

5.2.1 Development of Sorption Coefficient Databases 

Sorption coefficient databases can range from simple tabulations, to probability distributions, to 

complex interrelational computerized datasets. Historically, the tendency has been to use these 

databases as a source of individual sorption coefficients for the contaminant of interest. It is 

important, however, that the users of these compilations understand the assumptions and 

adequacy of experimental data (including quality assurance) that form the basis for the values 

presented in the database. This understanding is necessary for the user to characterize the 

limitations, conservatisms, and uncertainties inherent in the dataset and make an informed 

decision with regard to selecting values that are appropriate to different systems. Special care 

should be taken in selecting sorption coefficients for contaminants that are sensitive to 

geochemical and physical conditions. Experimental and modeling work at CNWRA has shown 

different ways to address the uncertainties due to these types of natural variability in 

geochemical systems. However, these approaches are generally calibrated based on batch 

sorption experiments using pure end-member minerals. More realistic approaches should be 

investigated to examine the extent to which these relatively simple surface complexation models 

and ion-exchange models can be productively applied to natural materials and mineral mixtures 

likely to be encountered along the groundwater flow path. 

5.2.2 Steel Corrosion Product Sorption 

As discussed in Section 4.6.1, products of corrosion of in-package steel components may play a 

role in radionuclide release from nuclear waste containers. Important uncertainties in modeling 

transport through the container include. 

• Temporal evolution of corrosion product mineralogy 

• The nature and temporal evolution of physical characteristics of the corrosion products, 

including surface area, crystallinity, and the potential for preferential pathways through 

the corrosion products or pathways that bypass them 
5-2 

• Temporal evolution of aqueous chemical conditions in the waste package 

• Sorption coefficients on steel corrosion products 

• Sorption kinetics 

• Sorption competition 

The CNWRA staff initiated a study of plutonium sorption onto goethite as an independent 

confirmatory analysis of the effectiveness of steel corrosion products at delaying waste package 

release. That work continues now under the Integrated Spent Nuclear Fuel Regulatory 

Activities program because it has application in a wide variety of potential geologic 

repository settings. 

5.2.3 Radionuclide Systems 

CNWRA sorption work has focused on actinides in general, and uranium and neptunium in 

particular. Other radionuclides that can have an effect on performance include plutonium, 

americium and thorium and fission products such as technetium and iodine. Particularly for 

plutonium, there is a general lack of suitable experimental data from which to apply the 

methods outlined in this report. Additional work will be needed to provide sorption results over a 

suitably wide range in geochemical conditions to allow model calibration and testing through 

model predictions. 
APPENDIX
1 

LIST OF CENTER FOR NUCLEAR WASTE REGULATORY ANALYSES (CNWRA®) 

SORPTION-RELATED PRODUCTS 

Bertetti, F.P., J. Prikryl, and B. Werling. “Development of Updated Total-System Performance 

Assessment Parameter Distributions for Radionuclide Transport in the Saturated Zone.” 

San Antonio, Texas: CNWRA. 2004. 

Bertetti, P. and D.R. Turner. “Review of U.S. Department of Energy (DOE) Study Plan 

8.3.1.3.4.2—Biological Sorption and Transport.” San Antonio, Texas: CNWRA. 1993. 

Bertetti, F.P. and B. Werling. “Predecisional for Official Use Only: Sorption of Neptunium-237 

on Alluvium Collected from Fortymile Wash, Nye County, Nevada.” San Antonio, Texas: 

CNWRA. 2005. 

McMurry, J. and F.P. Bertetti. “Selection of Sorption-Related Values for Unsaturated Zone and 

Saturated Zone Transport in Total-System Performance Assessment.” San Antonio, Texas: 

CNWRA. 2007. 

McMurry, J. and F.P. Bertetti. “Draft: Selection of Sorption-Related Values for Unsaturated 

Zone and Saturated Zone Transport in Total-System Performance Assessment.” San Antonio, 

Texas: CNWRA. 2007 

Pabalan, R. and D.R. Turner. “Center for Nuclear Waste Regulatory Analyses Review of the 

Nuclear Waste Management Organization (NWMO) Report: Sorption in Highly Saline 

Solutions—State-of-Science Review.” San Antonio, Texas: CNWRA. 2009 

Pabalan, R.T. “Report on the Peer Review of the Sorption Modeling for High-Level 

Performance Assessment Research Project.” CNWRA 95-023. San Antonio, Texas: CNWRA. 

1995. 

Prikryl, J.D. and R.T. Pabalan. “Proposal 20-20026: Radionuclide Sorption on 

Surfactant-Modified Natural Zeolites.” San Antonio, Texas: CNWRA. 1996. 

Prikryl, J.D. and D.A. Pickett. “Recommended Site-Specific Sorption Coefficients for Reviewing 

Non-High-Level Waste Determination at the Savannah River Site and Idaho National 

Laboratory.” Rev. 1. San Antonio, Texas: CNWRA. 2007 

Turner, D.R. “Center for Nuclear Waste Regulatory Analyses Input to the Nuclear Energy 

Agency Phase II Sorption Modeling Project.” San Antonio, Texas: CNWRA. 2002. 

Turner, D.R. “Radionuclide Sorption in Fractures at Yucca Mountain, Nevada: A Preliminary 

Demonstration of Approach for Performance Assessment.” San Antonio, Texas: CNWRA. 

1998. 

Turner, D.R. “A Uniform Approach to Surface Complexation Modeling of Radionuclide 

Sorption.” CNWRA 95-001. San Antonio, Texas: CNWRA. 1995. 

Turner, D.R. “Mechanistic Approaches to Radionuclide Sorption Modeling.” CNWRA 93-019. 

San Antonio, Texas: CNWRA. 1993. 
2 

Turner, D.R. “Sorption Modeling for High-Level Waste Performance Assessment: A Literature 

Review.” CNWRA 91-011. San Antonio, Texas: CNWRA. 1991. 

Turner, D.R. “Effects of Variable Hydrologic Saturation on Sorption Modeling for High-Level 

Waste Performance Assessment: A Literature Review.” CNWRA 91-016. San Antonio, Texas: 

CNWRA. 1991. 

Turner, D.R., F.P. Bertetti, and R.T. Pabalan. “Applying Surface Complexation Modeling to 

Radionuclide Sorption.” San Antonio, Texas: CNWRA. 2005. 

Turner, D., B. Sagar, and W. Ott. “Trip Report: Attendance at the Workshop on Sorption: 

Conclusion of the Nuclear Energy Agency (NEA) Sorption Project II on Use of Thermodynamic 

Sorption Models (TSM) in Performance Assessment held at Paris, France, October 10–11, 

2005.” San Antonio, Texas: CNWRA. 2005 

Turner, D., G. Wittmeyer, and P. Bertetti. “Radionuclide Sorption in the Alluvium at 

Yucca Mountain, NV—A Preliminary Demonstration of an Approach for Performance 

Assessment—Letter Report.” San Antonio, Texas: CNWRA. 1998. 

Abstracts/Posters/Presentations 

Bertetti, F.P., R. Pabalan, D.R. Turner, and M.G. Almendarez. “Studies of Neptunium(V) 

Sorption on Montmorillonite, Clinoptilolite, Quartz and a-Alumina.” American Chemical Society 

Annual Meeting Abstract, New Orleans, Louisiana, March 24–28, 1995. Washington, DC: 

American Chemical Society. 1996. 

Bertetti, F.P., R.T. Pabalan, and D.R. Turner. “Neptunium (V) Sorption Behavior on 

Clinoptilolite, Quartz and Montmorillonite.” Materials Research Society Fall Meeting Abstract. 

Boston, Massachusetts, November 27–December 1, 1995. Warrendale, Pennsylvania: 

Materials Research Society. 1996. 

Bertetti, F.P., R.T. Pabalan, and M.G. Almendarez. “Studies of Neptunium(V) Sorption on 

Quartz, Clinoptilolite, Montmorillonite, and A-Alumina.” Proceedings of the American Chemical 

Society Symposium Abstract, New Orleans, Louisiana, March 26–29, 1996; and 211th American 

Chemical Society Symposium on Sorption of Metals on Earth Material: Factors, Rates, 

Mechanisms, and Model Applications, New Orleans, Louisiana, March 26–29, 1996. 

Washington, DC: American Chemical Society. 1996. 

Bertetti, F.P., R.T. Pabalan, D.R. Turner, and M.G. Almendarez. “Experimental Studies of 

Neptunyl Sorption on Quartz, Clinoptilolite, and Montmorillonite.” Annual Meeting of the 

Geological Society of America Abstract, New Orleans, Louisiana, November 6–9, 1995. 

Washington, DC: Geological Society of America. 1995. 

Bertetti, F.P., R.T. Pabalan, D.T. Turner, and M.G. Almendarez. “ Neptunium(V) Sorption 

Behavior on Clinoptilolite Quartz and Montmorillonite.” Materials Research Society Meeting 

Paper, Boston, Massachusetts, November 27–December 1, 1995. Warrendale, Pennsylvania: 

Materials Research Society. 1995. 
3 

Bertetti, P.F., R.T. Pabalan, D.R. Turner, and M. Almendarez. “Experimental Study of Uranium 

(+6) Sorption on Quartz Sand. Geological Society of America (GSA) Annual Meeting Abstract, 

Seattle, Washington, October 24–27, 1994. Washington, DC: Geological Society of America. 

1994. 

Greathouse, J. and R.T. Pabalan. “Molecular Simulation of Uranium(6+) Adsorption on Quartz 

Surfaces.” Proceedings of the 24th International Symposium on the Scientific Basis for Nuclear 

Waste Management Abstract, Sydney, Australia, August 27–31, 2000. Warrendale, 

Pennsylvania: Materials Research Society. 2000. 

Jain, A., J.D. Prikryl, R.T. Pabalan, and D.R. Turner. “Experimental and Modeling Studies of 

Uranyl Adsorption Onto Binary Mixture of Silicate Minerals.” Proceedings of the 217th American 

Chemical Society National Meeting Abstract, Anaheim, California, March 21–25, 1999. 

Washington, DC: American Chemical Society. 1999. 

Pabalan, R.T. “Radionuclide Sorption in High-Level Waste Performance Assessment: 

Abstraction of Results From Experiments and Surface-Complexation Models.” Workshop 

on Conceptual Model Development for Subsurface Reactive Transport Modeling of Inorganic 

Contaminates, Radionuclides, and Nutrients Presentation, Albuquerque, New Mexico, 

April 20–22, 2004. Washington, DC: NRC. 2004. 

Pabalan, R., F.P. Bertetti, J.D. Prikryl, and D.R. Turner. “Uranium(VI) Sorption Onto 

Selected Mineral Surfaces: Key Geochemical Parameters.” Chapter 3. E. Jenne, ed. 

American Chemical Society Annual Meeting Abstract/Presentation, New Orleans, Louisiana, 

March 24–29, 1996. San Diego, California: American Chemical Society. pp. 100–130. 1996. 

Pabalan, R.T. and M. Lupkowski. “Molecular Dynamics Simulation of Uranyl Sorption on 

Mineral Surfaces.” Spring Meeting, American Geophysical Union Abstract, Baltimore, Maryland, 

May 23–27, 1994. Washington, DC: American Geophysical Union. 1994. 

Pabalan, R.T. and M. Lupkowski. “Molecular Dynamics Simulation of Uranyl Sorption on 

Mineral Surfaces.” Geological Society of America Annual Meetings Article, Boston, 

Massachusetts, October 25–28, 1993. Washington, DC: Geological Society of America. 1993. 

Pabalan, R.T. and D.R. Turner. “Uranium (6+) Sorption on Montmorillonite: Effect pH, EU, and 

M/V.” Symposium on Clays, Surfaces, and the Environment 32nd Annual Meeting of the Clay 

Minerals Society Abstract, Baltimore, Maryland, June 3–8, 1995. Chantilly, Virginia: Clay 

Minerals Society. 1995. 

Pabalan, R.T., D.R. Turner, and F.P. Bertetti. “Radionuclide Sorption in High-Level 

Waste Performance Assessment: Abstraction of Results From Experiments and 

Surface-Complexation Models.” Workshop on Conceptual Model Development for Subsurface 

Reactive Transport Modeling of Inorganic Contaminates, Radionuclides, and Nutrients, 

Sponsored by the Working Group on Subsurface Reactive Solute Transport; Federal 

Interagency Collaboration on Multimedia Environmental Models, Abstract, Albuquerque, 

New Mexico, April 20–22, 2004. Washington, DC: NRC. 2004. 

Pabalan, R.T., M. Miller, and M. Lupkowski. “Molecular Dynamics Simulation of Uranyl 

Adsorption on Quartz Surfaces.” Proceedings of the 217th American Chemical Society National 

Meeting Abstract, Anaheim, California, March 21–25, 1999. Washington, DC: American 

Chemical Society. 1999. 
4 

Pabalan, R.T., D.R. Turner, F.P. Bertetti, and J.D. Prikryl. “Uranium(VI) Sorption Onto Selected 

Mineral Surfaces: Key Geochemical Parameter.” Proceedings of the 211th American Chemical 

Society Symposium on Sorption of Metals on Earth Material: Factors, Rates, Mechanisms, and 

Model Applications Article, New Orleans, Louisiana, March 26–29, 1996. Washington, DC: 

American Geochemical Society. 1996. 

Pabalan, R.T., F.P. Bertetti, and J.D. Prikryl. “Geochemical Controls on the Sorption of Uranium 

(6+) onto Mineral Surfaces.” Spring Meeting, American Geophysical Union Abstract, 

Washington, DC, May 29–June 2, 1995. Washington, DC: American Geophysical Union. 

1995. 

Pabalan, R.T., T.B. Dietrich, and B.W. Leslie. “The Effects of pH, EU, and Ionic Strength on the 

Sorption of Uranium (VI) on Clinopilolite.” Proceedings of the 4th International Conference on 

the Chemistry and Migration Behavior of the Actinides and Fission Products in the Geosphere, 

MIGRATION ‘93 Abstract, Charleston, South Carolina, December 12–17, 1993. 

Pabalan, R.T., J.D. Prikryl, P.M. Muller, and T.B. Dietrich. “Experimental Study of Uranium (6+) 

Sorption on the Zeolite Mineral Clinoptilolite.” Symposium V 1992 Materials Research Society 

Fall Meeting Article, Boston, Massachusetts, November 30–December 4, 1992. Warrendale, 

Pennsylvania: Materials Research Society. 1992. 

Pabalan, R.T., J.D. Prikryl, and P.M. Muller. “Experimental Study of U6+ Sorption on the Zeolite 

Mineral Clinoptilolite.” Proceedings of the Seventh International Symposium on Water-Rock 

Interaction, Abstract. Research Triangle Park, North Carolina: International Association of 

GeoChemistry. 1991. 

Painter, S., V. Cvetkovic, D. Pickett, and D. Turner. “Significance of Kinetics for Sorption on 

Inorganic Colloids: Modeling and Data Interpretation Issues.” American Geophysical Union Fall 

Meeting Abstract, San Francisco, California, December 10–14, 2001. Washington, DC: 

American Geophysical Union. 2001. 

Prikryl, J. and R. Pabalan. “Sorption of Uranium (VI) and Neptunium (V) by Surfactant 

Modified Natural Zeolites.” Proceedings of the Materials Research Society Symposium on 

the Scientific Basis for Nuclear Waste Management Abstract, Boston, Massachusetts, 

November 30–December 4, 1998. Warrendale, Pennsylvania: Materials Research Society. 

pp. 1,035–1,042. 1998. 

Prikryl, J.D., A. Jain, D.R. Turner, and R.T. Pabalan. “Uranium(VI) Sorption Behavior on Silicate 

Mineral Mixtures.” Proceedings of the Seventh International Conference on the Chemistry and 

Migration Behavior of Actinide and Fission Products in the Geosphere, Article, Lake Tahoe, 

Nevada, September 26–October 1, 1999. Munich, Germany: Oldenbourg Verlag. 1999. 

Prikryl, J.D., F.P. Bertetti, and R.T. Pabalan. “Effects of Surfactant Modification on the Sorption 

Behavior of Natural Zeolites for Strontium (2+) and Uranium (6+).” Proceedings of the Materials 

Research Society Symposium on the Scientific Basis for Nuclear Waste Management Abstract, 

Boston, Massachusetts, November 29–December 3, 1999. Warrendale, Pennsylvania: 

Materials Research Society. 1999. 
5 

Prikryl, J.D., P. Bertetti, and R.T. Pabalan. “Effects of Surfactant Modification on the Sorption 

Behavior of Natural Zeolites for Strontium (2+) and Uranium (6+).” R. Smith, and D. Shoesmith 

eds. Scientific Basis for Nuclear Waste Management XXIII 608, Article. Warrendale, 

Pennsylvania: Materials Research Society. pp. 281–286. 1999. 

Prikryl, J.D., R.T. Pabalan, D.R. Turner, and B.W. Leslie. “Uranium Sorption on Alumina: 

Effects of pH and Surface-Area/Solution—Volume Ratio.” Fourth International Conference on 

the Chemistry and Migration Behavior of Actinides and Fission Products in the Geosphere, 

Article, MIGRATION ‘93, Charleston, South Carolina, December 12–17, 1993. Munich, 

Germany: Oldenbourg Verlag. 1993. 

Sagar, B. “Thermodynamic Sorption Models in Safety Assessments: Regulatory View.” 

Nuclear Energy Agency (NEA) Sorption Project Workshop, Article, Paris, France, 

October 10–11, 2005. Paris, France: Nuclear Energy Agency. 2005. 

Turner, D.R. “The Role of Radionuclide Sorption in High-Level Waste Performance 

Assessment: Approaches for the Abstraction of Detailed Models.” Annual Meeting of the Soil 

Science of America Annual Meeting, Abstract. Salt Lake City, Utah: October 31–November 4, 

1999. 

Turner, D. “Approaches to Sorption Modeling for High-Level Waste Performance Assessment.” 

Fourth International Conference Chemistry and Migration Behavior of the Actinides and Fission 

Products in the Geosphere, Article, MIGRATION ‘93, Charleston, South Carolina, 

December 12–17, 1993. Munich, Germany: Oldenbourg Verlag. 1993. 

Turner, D. “Radionuclide Sorption Modeling Using the MINTEQA2 Speciation Code.” Sixteenth 

International Symposium on the Scientific Basis for Nuclear Waste Management, Abstract, 

Boston, Massachusetts, November 30–December 4, 1992. Pittsburgh, Pennsylvania: Material 

Research Society. 1992. 

Turner, D.R. and R.T. Pabalan. “Abstraction of Mechanistic Sorption Models for Performance 

Assessment Calculations at Yucca Mountain, NV.” Proceedings of the Waste Management ’99 

Conference Paper, Tucson, Arizona, February 28–March 4, 1999. Published on CD ROM. 

Tucson, Arizona: WM Symposia, Inc. 1999. 

Turner, D.R. and R.T. Pabalan. “Sorption Modeling for Performance Assessment at the Center 

for Nuclear Waste Regulatory Analyses.” Nuclear Energy Agency (NEA), Abstract, Cambridge, 

United Kingdom, May 5–8, 1997. Paris, France: Nuclear Energy Agency. 1997. 

Turner, D. and S.A. Sassman. “Approaches to Sorption Modeling for High-Level Waste 

Performance Assessment.” Fourth International Conference on the Chemistry and Migration 

Behavior of Actinides and Fission Products in the Geosphere, Article, Charleston, South 

Carolina, December 12–17, 1993. 

Turner, D.R., F.P. Bertetti, J. McMurry, and R.T. Pabalan. “Applying Thermodynamic 

Radionuclide Sorption Models to Performance Assessment.” Nuclear Energy Agency (NEA) 

Sorption Workshop Presentation, Paris, France, October 10–11, 2005. Paris, France: Nuclear 

Energy Agency. 2005. 
6 

Turner, D.R., R.T. Pabalan, and F.P. Bertetti. “Developing Thermodynamic Radionuclide 

Sorption Models: Experimental and Modeling Results.” Nuclear Energy Agency (NEA) Sorption 

Workshop Presentation, Paris, France, October 10–11, 2005. Paris, France: Nuclear Energy 

Agency. 2005. 

Turner, D.R., F.P. Bertetti, and R.T. Pabalan. “The Role of Radionuclide Sorption High-Level 

Waste Performance Assessment.” Soil Geochemical Processes of Radionuclides. Proceedings 

of the 2001 Soil Science Society of America. Madison, Wisconsin: Soil Science Society of 

America. 2001. 

Turner, D.R., R.T. Pabalan, J.D. Prikryl, and F.P. Bertetti. “Radionuclide Sorption at Yucca 

Mountain, Nevada—A Demonstration of an Alternate Approach for Performance Assessment.” 

Symposium on the Scientific Basis for Nuclear Waste Management XXII, Materials Research 

Society Fall Meeting Paper, Boston, Massachusetts, November 30–December 3, 1998. 

Warrendale, Pennsylvania: Materials Research Society. 1998. 

Turner, D.R., R.T. Pabalan, P. Muller, and F.P. Bertetti. “Uniform Surface Complexation 

Approaches to Radionuclide Sorption Modeling.” Proceedings of the Sixth Annual International 

Conference on High-Level Radioactive Waste Management Paper, Las Vegas, Nevada, May 

1995. La Grange Park, Illinois: American Nuclear Society. 1995. 

Turner, D.R., T. Griffin, and T.B. Dietrich. “Radionuclide Sorption Modeling Using the 

MINTEQA2 Speciation Code.” Symposium V 1992 Materials Research Society Fall Meeting 

Paper, Boston, Massachusetts, November 30–December 4, 1992. Warrendale, Pennsylvania: 

Materials Research Society. 1992. 

Zaidan, O.F., J.A. Greathouse, and R.T. Pabalan. “Computer Simulation of Uranyl Adsorption 

on Montmorillonite Clay.” 12th Annual V.M. Goldschmidt Conference Abstract, Davos, 

Switzerland, August 18–23, 2003. Oxford, United Kingdom: Cambridge Publications. 2003. 

Journal Articles/Book Chapters 

Bertetti, P.F., R.T. Pabalan, D.R. Turner, and M. Almendarez. “Experimental and Modeling 

Study of Uranium (6+) Sorption on Quartz.” Journal Applied Geochemistry. 1995. 

Cvetkovic, V. and S. Painter. “Final Journal Article: Field-Scale Transport Implications of 

Sorption Kinetics with Multiple Rates.” Journal of Waters Resources Research. 2008. 

Jarzemba, M.S. “An Improved Method for Modeling Sorption of Radionuclides During Liquid 

Phase Transport for Performance Assessments of High-Level Waste Repositories.” Nuclear 

Technology. 1995. 

Pabalan, R.T. and D.R. Turner. “Uranium (+6) Sorption on Montmorillonite: Experimental and 

Surface Complexation Modeling Study.” Aquatic Geochemistry. 1995. 

Painter, S., V. Cvetkovic, D. Pickett, and D.R. Turner. “Significance of Kinetics for Sorption on 

Inorganic Colloids: Modeling and Experiment Interpretation Issues.” Environmental Science 

and Technology. Vol. 36. pp. 5,369–5,375. 2002. 
7 

Prikryl, J.D., A. Jain, R.T. Pabalan, and D.R. Turner. “Uranium (VI) Sorption Behavior on Mixed 

Silicate Minerals.” Journal of Contaminant Hydrology. Vol. 47, Issues 2–4. pp. 241–253. 

1999. 

Turner, D.R., R.T. Pabalan, and F.P. Bertetti. “Neptunium(V) Sorption on Montmorillonite: 

Experimental and Surface Complexation Modeling Study.” Environmental Science and 

Technology. 1996. 

Wang, P., A. Anderko, and D.R. Turner. “Thermodynamic Modeling of the Adsorption of 

Radionuclides on Selected Minerals II: Anions.” Geochimica et Cosmochimica Acta. 2000. 

Wang, P., A. Anderko, and D.R. Turner. “Thermodynamic Modeling of the Adsorption of 

Radionuclides on Selected Minerals I: Cations.” Geochimica et Cosmochimica Acta. 2000. 

Zaidan, O.F., J.A. Greathouse, and R.T. Pabalan. “Monte Carlo and Molecular Dynamics 

Simulation of Uranyl Adsorption on Montmorillonite Clay.” Clays and Clay Minerals. Vol. 51. 

pp. 372–381. 2002. 

Scientific Notebooks 

Bertetti, P. “Neptunium Sorption Experiments (03/18/1999–03/27/2002).” Scientific Notebook 

No. 309. San Antonio, Texas: CNWRA. 2002. 

Browning, L. “Development of a New Sorption Database for the Geochemist’s Workbench 

(GWB), Release 3.0 (06/01/1999–04/06/2000).” Scientific Notebook No. 331. San Antonio, 

Texas: CNWRA. 2000. 

CNWRA. “Supplemental Material, Technical Activity Supporting Documentation: Experimental 

Data for Uranium Sorption on a-alumina Experiments B–8005, B–8006, and B–8007 (1993). 

San Antonio, Texas: CNWRA. 2004. 

Jain, A. and D.Turner. “Radionuclide Transport Sorption Experiments 

(08/11/1999–08/24/2000).” Scientific Notebook No. 365. San Antonio, Texas: CNWRA. 1999. 

Nugent, M. and D. Turner. “Sorption Experiments with Clinoptilolite and Montmorillonite 

(10/08/1999 through 08/28/2000).” Scientific Notebook No. 370. San Antonio, Texas: 

CNWRA. 1999. 

Pabalan, R., R. Ghubash, J.D. Prikryl, and P. Bertetti. “Geochemistry Project: Zeolite Sorption 

Experiments (GC-10) (06/08/1993–01/08/1997). Scientific Notebook No. 061. San Antonio, 

Texas: CNWRA. 1997. 

Pabalan, R.T. “Geochemistry Research Laboratory Notebook: Ion Exchange and Sorption 

Experiments (06/11/1991-03/21/1992).” Vol. GC–05, Geochemistry Research Laboratory 

Notebook: Ion Exchange and Sorption Experiments (06/11/1991–03/21/1992). Scientific 

Notebook No. 026. San Antonio, Texas: CNWRA. 1992. 

Pabalan, R.T., P. Muller, and T. Dietrich. “Vol. GC–07 Geochemistry Research Laboratory 

Notebook: Ion Exchange and Sorption Experiments (03/21/1992–06/09/1993).” Scientific 

Notebook No. 037-1. San Antonio, Texas: CNWRA. 1993. 
8 

Pabalan, R.T., P. Muller, T. Dietrich, and J. Prikryl. “Vol. GC–06 Geochemistry Research 

Laboratory Notebook: Ion Exchange and Sorption Experiments (03/21/1992–12/10/1996).” 

Scientific Notebook No. 038. San Antonio, Texas: CNWRA. 1996. 

Pickett, D. “Actinide Sorption Experiments (02/07/1995–02/23/2007).” Scientific Notebook 

No. 133. San Antonio, Texas: CNWRA. 2007. 

Prikryl, J. “Focused Research Proposal-Radionuclide Sorption on Surfactant Modified Natural 

Zeolites (11/04/1996–02/04/2000). Scientific Notebook No. 200. San Antonio, Texas: 

CNWRA. 1996. 

Sabido, L. “Modeling Np (V) Sorption to Calcite Based on Batched Sorption Experiments and 

Surface Complexation Modeling Performed by Zarvin, et al., 2004 (04/27/2007–02/14/2008).” 

Scientific Notebook No. 874E. San Antonio, Texas: CNWRA. 2008. 

Turner, D. “Sorption Modeling for High-Level Waste Performance Assessment 

(06/02/1992–01/13/1997).” Scientific Notebook No. 046. San Antonio, Texas: CNWRA. 1997. 

Turner, D. “Sorption Modeling for Performance Assessment (06/16/1992–09/09/1994).” 

Scientific Notebook No. 057. San Antonio, Texas: CNWRA. 1995. 

Turner, D.R. and P.M. Muller. “Sorption Modeling of Radionuclide Sorption Using Surface 

Complexation Modeling Techniques (09/09/1994–01/20/1996).” Scientific Notebook No. 123. 

San Antonio, Texas: CNWRA. 1996. 

Turner, D., J. Prikryl and A. Jain. “Uranium Sorption Experiments (04/21/1998–06/13/2000).” 

Scientific Notebook No. 266. San Antonio, Texas: CNWRA. 1998. 

Werling, B. “Cation and Anion Analyses of Solutions From Sorption Experiments 19 and 22 

CAL Series 1.” Scientific Notebook No. 830. San Antonio, Texas: CNWRA. 2006. 

Werling, B. “Neptunium/Calcite Sorption Experiments (05/29/2001–0/21/2002).” 

Scientific Notebook No. 463. San Antonio, Texas: CNWRA. 2001. 

Werling, B. and M. Roberts. “Np Sorption on Consolidated With Simulated Waters (With 

and Without Calcium) Over a pH Range—Identified as CAL (Consolidated Alluvium) Series 5 

and 6—(06/06/2005–08/30/2005).” Scientific Notebook No. 711. San Antonio, Texas: 

CNWRA. 2005. 

Werling, B. and S. Watson. “Cation and Anion Analyses of Solutions from Sorption Experiments 

19 and 22 CAL Series 1 (08/080/2006–11/06/2006). Scientific Notebook No. 812. San Antonio, 

Texas: CNWRA. 2006 

Werling, B., M. Roberts, R. McGinnis, and P. Bertetti. “Cation Analyses of Experimental 

Solutions from Sorption Experiments CAL Series 7 and 8 (08/30/2005–08/31/2006).” Scientific 

Notebook No. 733. San Antonio, Texas: CNWRA. 2006 

Werling, B., J. Brown, and P. Bertetti. “Neptunium-Calcite Sorption Experiments 

(06/25/2002–03/14/2003).” Scientific Notebook No. 463. San Antonio, Texas: CNWRA. 2002.