Application of 

Surface Complexation 

Modeling to Selected 

Radionuclides and 

Aquifer Sediments

Office of Nuclear Regulatory Research 

NUREG/CR-6959 
Application of 

Surface Complexation 

Modeling to Selected 

Radionuclides and 

Aquifer Sediments 

Manuscript Completed: January 2008 

Date Published: April 2008 

Prepared by 

J.A. Davis 

U.S. Geological Survey 

Menlo Park, Ca 94025 

M. Fuhrmann, NRC Project Manager 

NRC Job Code Y6462 

Office of Nuclear Regulatory Research

NUREG/CR-6959 
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iii

ABSTRACT

Several different surface complexation modeling approaches exist to describe the adsorption of solutes 

from water onto the surfaces of mineral phases. The Generalized Composite surface complexation 

modeling approach considers the surfaces of a mixed mineral assemblage, such as soils or sediments, to 

contain generic functional groups with averaged properties (see demonstration of this approach in 

USNRC, 2003). The objective of the study described in this report was to extend the Generalized 

Composite modeling approach to other complex geomaterials and other radionuclides. The modeling 

approach provides the methodology to estimate Kd values and potential retardation of sorbing 

radionuclides (Np, U, Ni) with complex aqueous chemistry, and as a function of aqueous chemical 

conditions in groundwaters. Laboratory batch experiments with natural sediments illustrate that the 

adsorption (and Kd values) are sensitive to aqueous chemical conditions, including pH and the dissolved 

carbonate concentration (which equilibrates with a given partial pressure of carbon dioxide in the gas 

phase). Complex geomaterials in this study included aquifer sediments from: 1) the Naturita (Colorado) 

UMTRA site, 2) the Forty-Mile Wash (Nye County, Nevada) aquifer, and 3) the USGS research site at 

Cape Cod (Massachusetts). 

Generalized Composite surface complexation models (GC-SCM) were developed for the following 

radionuclide/geomaterial pairs, as examples of the modeling approach: 1) Np(V)/Naturita aquifer 

sediments, 2) U(VI)/Forty-Mile Wash aquifer sediments, and 3) Ni/Cape Cod aquifer sediments. In each 

case the models were calibrated by fitting batch adsorption data as a function of chemical conditions. In 

all cases, the GC-SCM were developed without electrical double layer terms. 

In the case of Np(V)/Naturita aquifer sediments, the GC-SCM was able to accurately simulate Kd values 

for Np(V) adsorption on the sediments over a range of pH and dissolved carbonate and Np(V) 

concentrations using three Np(V) surface reactions and two surface sites for each reaction (strong- and 

weak-binding). Unlike U(VI), the sorption of Np(V) was found to be much less sensitive to the dissolved 

carbonate concentration. Part of the reason for this limited dependence on dissolved carbonate may be 

the Np(V) forms both aqueous and surface (ternary) carbonate complexes. The effect of humic acids on 

Np(V) sorption was also studied. Like carbonate, Np(V) sorption was not influenced (decreased) as much 

by aqueous Np(V)-humic aqueous complexation as might have been expected, indicating that ternary 

surface-Np(V)-humic complexes may form on mineral surfaces. 

In the case of the U(VI)/Forty-Mile Wash aquifer sediments, the GC-SCM produced good agreement with 

the experimental data for U(VI) adsorption as a function of pH, dissolved carbonate, and U(VI) 

concentration with only one U(VI) surface reaction with two surface sites that formed strong and weak 

complexes. The Kd values decreased significantly with increasing carbonate or U(VI) concentrations, but 

the GC-SCM was able to simulate the dependence of Kd on these variables. In the case of the Ni/Cape 

Cod aquifer sediments, Ni Kd values increased with increasing pH and with decreasing Ni concentration.

A very simple GC-SCM was calibrated to the experimental data, with one Ni surface species and only one

site type. Addition of more reactions or more sites did not improve the goodness-of-fit to the data collected. 

Aqueous complexation of Ni with sulfate ions in the groundwater was considered as a part of the model 

calculations. 

The challenge in applying the surface complexation concept in the environment is to simplify the SCM, 

such that predicted adsorption is still calculated with mass laws that are coupled with aqueous speciation, 

while lumping parameters that are difficult to characterize in the environment in with other parameters. 

In order to be applied by solute transport modelers and within PA applications, the complexity of the 
iv 

adsorption model needs to be balanced with the goal of using the simplest model possible that is 

consistent with observed data. This can be achieved with the semi-empirical, site-binding GC modeling 

approach used in this report and previously demonstrated for modeling U(VI) retardation at the Naturita 

UMTRA site (Curtis et al., 2006; USNRC, 2003). The GC-SCM is a compromise between the simple 

constant-Kd approach and more complex SCM that are difficult to apply to the environment at present. 

The GC modeling approach is preferable to completely empirical approaches, such as the constant-Kd 

model or adsorption isotherms, because the important linkage between surface and aqueous species (and 

associated thermodynamic data) is retained in the modeling through the coupling of mass action 

equations. This linkage also provides a framework for conducting uncertainty analyses that is based on 

process level parameters rather than on ranges of Kd values that result from lumping together multiple 

processes. Uncertainty in radionuclide retardation predictions may be reduced, quantified, and more 

completely understood in the future with the use of the GC-SCM modeling approach.
FOREWORD 

The U.S. Nuclear Regulatory Commission (NRC) uses environmental transport models, 

describing the mass and rate of the movement of contaminants, to evaluate the migration of 

radionuclides released to the environment from sites that are licensed by the NRC. This report 

discusses the development of improved methods to describe chemical processes that control 

radionuclide movement in ground water. 

A key process that constrains the movement of dissolved contaminants in a soil is adsorption. 

In this process, ions associate with solid materials through reactions at the solid surface, and as 

a result they move more slowly than water moving through the material. The reactions 

controlling adsorption can vary substantially for different contaminants and can be sensitive to 

parameters such as pH, ionic strength, and concentrations of other dissolved species. 

Adsorption is typically described by the distribution coefficient, Kd. This is the concentration of 

an ion adsorbed on an associated solid divided by its concentration in solution, at steady state. 

A variety of approaches are used to describe adsorption in models, all of which ultimately assign 

a Kd value. Simple, constant geochemical conditions can use an empirical Kd, determined 

through batch experiments with site-specific materials. For better definition of Kd, isotherms can 

be developed through batch experiments in which some parameter, such as contaminant 

concentration, is varied. Cases in which several adsorption controlling parameters may change 

with time or location require more sophisticated approaches. The use of surface complexation 

models (SCMs) allows the calculation of Kd under these complex conditions, mechanistically 

describing how Kd changes as conditions are altered. SCMs employ mass action laws and 

thermodynamics to describe speciation in solution and on the solid, as well as the reactions 

between them. The family of SCMs ranges from detailed calculations of surface charge and 

associated adsorption to the semi-empirical approach of the generalized composite SCM 

(GC-SCM). This latter approach is applicable to mixed mineral assemblages of natural 

materials and views the mineral surface sites as generic functional groups with averaged 

properties. This report discusses the GC-SCM. 

The work described in this report developed GC-SCMs for three cases of contaminant/sediment 

combinations—Np(V)/Naturita aquifer sediment, U(VI)/Forty Mile Wash aquifer sediment, and 

Ni/Cape Cod aquifer sediment. These contaminants provide very different chemistries to test 

the modeling approach. The models provided good agreement with experimental data and 

performed satisfactorily in describing adsorption as a function of changing chemical conditions. 

Using this type of model as an input to performance assessment models will provide more 

realistic and flexible evaluations of site-specific geochemical processes, leading to better 

estimates of contaminant behavior. 

________________________________ 

Christiana Lui, Director 

Division of Risk Analysis 

Office of Nuclear Regulatory Research 

U.S. Nuclear Regulatory Commission 

 v
vii

CONTENTS 

ABSTRACT.............................................................................................................iii 

FIGURES................................................................................................................vii 

TABLES...................................................................................................................xv 

1. APPLICATIONS OF SURFACE COMPLEXATION MODELING: SCIENTIFIC BACKGROUND 

by J. A. Davis 

 1.1 INTRODUCTION .........................................................................................1 

 1.2 SURFACE COMPLEXATION MODELING ...... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 

 1.3 PROJECT GOALS............................................................ . . . . . . . . . . . . . . . . . .4 

 1.4 OVERVIEW OF REPORT...............................................................................4 

2. NEPTUNIUM(V) SORPTION TO AN ALLUVIAL AQUIFER SEDIMENT: APPLICABILITY OF 

SURFACE COMPLEXATION MODELING 

by P. B. Moran, B. D. Honeyman, and J. A. Davis 

 2.1 BACKGROUND .........................................................................................5 

 2.1.1 Neptunium Aqueous Geochemistry...............................................................5 

 2.1.2 Neptunium(V) Sorption to Pure Mineral Phases .......................................10 

 2.1.3 Neptunium(V) Sorption to Complex Geomedia ........................................21 

 2.1.4 Influence of Humic Acids on Neptunium(V) Sorption.................................22 

 2.1.5 Surface Complexation Modeling of Natural Systems .................................28 

2.2 EFFECT OF SOLUTION COMPOSITION ON NEPTUNIUM(V) SORPTION TO AN 

ALLUVIAL AQUIFER SEDIMENT...............................................................28 

 2.2.1 Aquifer Sediment Sample ..................................................................28 

 2.2.2 Materials and Methods......................................................................29 

 2.2.2.1 Np(V) Time Dependent Sorption Experiments .............................32 

 2.2.2.2 Sorption Isotherm Experiments ...............................................32 

 2.2.2.3 Np(V) Sorption Edge Experiments ...........................................33 

 2.2.2.4 Surface Charge Characterization .............................................33 

 2.2.2.5 Near Infrared Spectroscopy ...................................................35 

 2.2.3 Results and Discussion ....................................................................35 

 2.2.3.1 Oxidation State Confirmation .................................................35 

 2.2.3.2 Liquid Scintillation Analysis ..................................................35 

 2.2.3.3 Np(V) Time Dependent Sorption .............................................37 

 2.2.3.4 Time Dependent Sorption in the Presence of Humic Acids ...............37 

 2.2.3.5 Np(V) Sorption Isotherm Results .............................................37 

 2.2.3.6 Np(V) Sorption Edge Results .................................................43 

 2.2.3.7 Effect of Groundwater Constituents on Np(V) Sorption ..................51 

 2.2.4 Summary of Np(V) Sorption Results ....................................................52 

2.3 SURFACE COMPLEXATION MODELING OF NEPTUNIUM(V) SORPTION IN 

NATURAL SYSTEMS ...............................................................................55 

 2.3.1 Introduction .................................................................................55 

 2.3.2 Generalized Composite Modeling Approach ...........................................55 

 2.3.3 Effect of Humic Acids Presence .........................................................77 

 2.3.4 Summary of Np(V) Surface Complexation Modeling .................................81 
 viii

3. DEVELOPMENT OF A SURFACE COMPLEXATION MODEL FOR URANIUM(VI) SORPTION 

ONTO FORTY-MILE WASH (NEVADA) AQUIFER SEDIMENTS 

by M. Kohler, and J. A. Davis 

3.1 MATERIALS AND METHODS........................................................... . . . . . . 83 

 3.1.1 Sediments........................................................................ . . . . . . 83

3.1.2 Artificial Groundwater Solutions ............................................. . . . . . . .83 

 3.1.3 Sodium Carbonate Extractions .........................................................83 

 3.1.4 Uranium(VI) Sorption Experiments ...................................................83 

3.2 RESULTS ..............................................................................................84 

 3.2.1 Uranium(VI) Sorption Kinetics onto Forty-Mile Wash Sediments ................84 

 3.2.2 Uranium(VI) Sorption onto Forty-Mile Wash Sediments at Variable pH 

and Alkalinity .............................................................................84 

3.3 URANIUM(VI) SURFACE COMPLEXATION MODELING FOR FORTY-MILE WASH 

SEDIMENTS .................................................................................................. . . . . . . 88 

3.3.1 Introduction...................................................................... . . . . . . ..88 

3.3.2 Modeling Calculations and Approach ........................................ . . . . . . 89 

4 DEVELOPMENT OF A SURFACE COMPLEXATION MODEL FOR NICKEL SORPTION 

ONTO CAPE COD (MASSACHUSETTS) AQUIFER SEDIMENTS 

by J. A. Davis and J. Joye 

4.1 MATERIALS AND METHODS.......................................................... . . . . . ...95 

 4.1.1 Preparation of an Aquifer Sediment Composite Sample .................. . . . . . . 95 

 4.1.2 Nickel Sorption Experiments ............................................................95

 4.2 RESULTS ..................................................................................... . . . . . ..95 

 4.3 SURFACE COMPLEXATION MODEL DEVELOPMENT FOR NICKEL 

ADSORPTION....... . . . . . . ......................................................................................... .97 

5. CONCLUDING REMARKS AND SUMMARY.................................................. . . . . . . ...99 

by J. A. Davis 

REFERENCES............................................................................................... . . . . . . .103 
 ix

FIGURES 

Figure 2.1 Np(V) species distribution as a function of pH in 0.1 M NaClO4 with a total Np(V) 

concentration of 2.0x10-6 M under CO2-free conditions..................................................... 6 

Figure 2.2 Np(V) species distribution as a function of pH in 0.1 M NaClO4 with a total Np(V) 

concentration of 2.0x10-6 M at atmospheric PCO2 ............................................................. 6 

Figure 2.3 Np(V) species distribution as a function of pH in 0.1 M NaClO4 with a total Np(V) 

concentration of 2.0x10-6 M at 2% PCO2............................................................................ 7 

Figure 2.4 Np(V) species distribution as a function of pH in 0.1 M NaClO4 with a total Np(V) 

concentration of 2.0x10-6 M at 8% PCO2............................................................................ 7 

Figure 2.5a Np(V) species distribution as a function of pH in 0.1 M NaClO4 with a total Np(V) 

concentration of 2.0x10-6 M, 10 mg L-1 HA at atmospheric PCO2 ..................................... 9 

Figure 2.5b Np(V) species distribution as a function of pH in 0.1 M NaClO4 with a total Np(V) 

concentration of 2.0x10-6 M, 100 mg L-1 HA at atmospheric PCO2 ................................. 10 

Figure 2.6 Np(V) species distribution as a function of PCO2 in 0.1 M NaClO4 with 2.0x10-6 M 

Np(V) and 100 mg L-1 HA at pH 7 and pH 8 ................................................................... 11 

Figure 2.7 LSC spectrum showing activity associated with 237Np α particle emission and activity 

associated with background radioactivity and residual 233Pa in a 2.0x10-6 M Np(V) 

sample ............................................................................................................................... 36 

Figure 2.8 Series of LSC spectra of 2.0x10-6 M Np(V) in the presence of 10-200 mg L-1 HA showing 

varying degrees of color quenching without a reduction in counts .................................. 36 

Figure 2.9 Time dependent sorption of 5.0x10-7 M Np(V) to 50 g L-1 unmodified NABS in AGW-3 

at pH 8.0 ± 0.1 and atmospheric PCO2 ............................................................................. 38 

Figure 2.10 Time dependent sorption of Np(V) to 50 g L-1 treated NABS in the presence of 10 mg L-1

HA (pH 8.0 ± 0.1) at atmospheric PCO2 with a total Np(V) concentration of 5.0x10-7 M 

and 0.1 M NaClO4. ........................................................................................................... 38 

Figure 2.11 Comparison of time dependent sorption of 5.0x10-7 M Np(V) with and without 10 mg L-1

HA at pH 8.0 ± 0.1 and atmospheric PCO2. . ................................................................... 38 

Figure 2.12a Np(V) sorption isotherms using a maximum range of Np(V) from 3.0x10-7 M to 5.0x10-6

M, 125 g L-1 unmodified NABS, in AGW-3 (pH 8) at atmospheric PCO2 or in AGW-6 

(pH 6.8) at 8% PCO2......................................................................................................... 39 

Figure 2.12b Kd values for Np(V) sorption as a function of aqueous Np(V) remaining in solution using 

a maximum range of Np(V) from 3.0x10-7 M to 5.0x10-6 M, 125 g L-1 unmodified NABS, 

 in AGW-3 (pH 8) at atmospheric PCO2 or in AGW-6 (pH 6.8) at 8% PCO2................... 39 
 x

Figure 2.13a Np(V) sorption isotherms using a maximum range of Np(V) from 2.0x10-7 M to 3.0x10-6

M, 125 g L-1 treated NABS, in AGW-3 (pH 8) at atmospheric PCO2 or in AGW-6 (pH 

6.8) at 8% PCO2 ................................................................................................................ 41 

Figure 2.13b Kd values for Np(V) sorption as a function of aqueous Np(V) remaining in solution using 

a maximum range of Np(V) from 2.0x10-7 M to 3.0x10-6 M, 125 g L-1 treated NABS, in 

AGW-3 (pH 8) at atmospheric PCO2 or in AGW-6 (pH 6.8) at 8% PCO2....................... 41 

Figure 2.14a Np(V) sorption isotherms using a maximum range of Np(V) from 3.0x10-7 M to 5.0x10-6

M, 125 g L-1 treated and unmodified NABS, in AGW-3, pH 8, at atmospheric PCO2..... 42 

Figure 2.14b Np(V) sorption isotherms using a maximum range of Np(V) from 2.0x10-7 M to 1.0x10-6

M, 125 g L-1 treated and unmodified NABS, in AGW-6, pH 6.8, at 8% PCO2................ 42 

Figure 2.14c Kd values for Np(V) sorption as a function of aqueous Np(V) remaining in solution using 

a maximum range of Np(V) from 3.0x10-7 M to 5.0x10-6 M, 125 g L-1 treated and 

unmodified NABS, in AGW-3 (pH 8) at atmospheric PCO2............................................ 42

Figure 2.14d Kd values for Np(V) sorption as a function of aqueous Np(V) remaining in solution using 

a maximum range of Np(V) from 2.0x10-7 M to 1.0x10-6 M, 125 g L-1 treated and 

unmodified NABS, in AGW-6 (pH 6.8) at 8% PCO2....................................................... 42 

Figure 2.15 Comparison of U(VI) and Np(V) sorption isotherms using Np(V) concentration range 

from 3.0x10-7 to 3.0x10-6 M on 125 g L-1 treated NABS and a U(VI) concentration range 

of 3.0x10-7 to 4.0x10-6 M on 50 g L-1 treated NABS in AGW-3 (pH 8) at atmospheric 

PCO2 ................................................................................................................................. 44 

Figure 2.16a Np(V) sorbed as a function of Np(V) remaining in solution using a maximum range of 

Np(V) from 8x10-7 M to 1x10-5 M, 125 g L-1 or 500 g L-1 unmodified NABS, in AGW-3 

(pH 8) at atmospheric PCO2 ............................................................................................. 44 

Figure 2.16b Np(V) sorption isotherms using a maximum range of Np(V) from 8x10-7 M to 1x10-5 M, 

125 g L-1 and 500 g L-1 unmodified NABS, in AGW-3 (pH 8) at atmospheric PCO2...... 44 

Figure 2.17a Np(V) sorption edges using 5.0x10-7 M on 50 g L-1 treated NABS in AGW-A at 

atmospheric PCO2 ............................................................................................................. 45 

Figure 2.17b Np(V) sorption edges using 5.0x10-7 M on 125 g L-1 treated NABS in AGW-A at 

atmospheric PCO2 ............................................................................................................. 45 

Figure 2.18a Salt titrations of 500 g L-1 treated NABS in 0.001 M NaClO4 within an inert atmosphere 

glovebox............................................................................................................................ 46 

Figure 2.18b Concentrations of Al(aq) and SiO2 in the solution phase of 500 g L-1 treated NABS in 0.1 

M NaClO4 at atmospheric PCO2 ....................................................................................... 46 

Figure 2.18c Concentrations of Ca2+ in the solution phase of 500 g L-1 treated NABS in 0.1 M NaClO4

at atmospheric PCO2 ......................................................................................................... 46 
 xi

Figure 2.19 Sorption to 1 g L-1 hematite as a function of pH using 5.0x10-7 M Np(V) in 0.1 M NaClO4

at atmospheric PCO2 ........................................................................................... ..........46 

Figure 2.20a Sorption edges using 5.0x10-7 M Np(V), 50 g L-1 and 125 g L-1 treated NABS in AGW-A 

at atmospheric PCO2 ......................................................................................................... 47 

Figure 2.20b Sorption edges using 5.0x10-7 M Np(V) , 50, 125 and 500 g L-1 treated NABS in AGW-A 

at atmospheric PCO2 ......................................................................................................... 47 

Figure 2.21a Np(V) sorption edges using 5.0x10-7 M and 5.0x10-6 M, 50 g L-1 treated NABS, in AGWA at atmospheric PCO2 ..................................................................................................... 48 

Figure 2.21b Np(V) sorption edges using 5.0x10-7 M and 5.0x10-6 M, 125 g L-1 treated NABS, in 

AGW-A at atmospheric PCO2 .......................................................................................... 48 

Figure 2.21c Np(V) sorption edges using 5.0x10-7 M and 5.0x10-6 M, 500 g L-1 treated NABS, in 0.1 

M NaClO4 at atmospheric PCO2 ....................................................................................... 48 

Figure 2.22 Comparison of U(VI) and Np(V) sorption edges using 5.0x10-7 M, 125 g L-1 treated 

NABS in AGW-A at atmospheric PCO2........................................................................... 49 

Figure 2.23a Sorption edges using 5.0x10-7 M Np(V), 50 or 125 g L-1 treated NABS, in 0.1 M NaClO4

using a N2 atmosphere (CO2-free) .................................................................................... 50 

Figure 2.23b Sorption edges using 5.0x10-7 M Np(V), 125 g L-1 treated NABS, in 0.1 M NaClO4 at 

atmospheric PCO2 or using a N2 atmosphere (CO2-free).................................................. 50 

Figure 2.24a Sorption edges using 5.0x10-7 M Np(V), 500 g L-1 treated NABS, in 0.1 M NaClO4 at 8% 

PCO2 ................................................................................................................................. 51 

Figure 2.24b Comparison of 5.0x10-7 M Np(V) sorption edges at atmospheric and 8% PCO2 using 500 

g L-1 treated NABS in 0.1 M NaClO4 ............................................................................... 51 

Figure 2.24c Comparison of 5.0x10-7 M Np(V) sorption edges at atmospheric PCO2 using 50 and 125 

g L-1 treated NABS in AGW-3 ......................................................................................... 51 

Figure 2.25a Np(V) sorption edges with 5x10-7 M Np(V), 125 g L-1 treated NABS, in AGW-A, 0.1 M 

NaClO4 or AGW-3 at atmospheric PCO2 ......................................................................... 52 

Figure 2.25b Np(V) sorption edges with 5x10-7 M Np(V), 125 g L-1 treated NABS, in AGW-A or 0.1 

M NaClO4 at atmospheric PCO2 ....................................................................................... 52 

Figure 2.26a Effect of 10 mg L-1 and 100 mg L-1 HA on Np(V) sorption edges using 5.0x10-7 M 

Np(V), 500 g L-1 treated NABS, in 0.1 M NaClO4 at atmospheric PCO2 ........................ 53 

Figure 2.26b Effect of 10 mg L-1 and 100 mg L-1 HA on Np(V) sorption edges using 5.0x10-6 M 

Np(V), 500 g L-1 treated NABS, in 0.1 M NaClO4 at atmospheric PCO2 ........................ 53 

Figure 2.27a Comparison of HA and Np(V) sorption edges using 100 mg L-1 HA, 5.0x10-7 M Np(V), 

500 g L-1 treated NABS in 0.1 M NaClO4 at atmospheric PCO2 ...................................... 54 

 
xii

Figure 2.27b Comparison of HA and Np(V) sorption edges using 100 mg L-1 HA, 5.0x10-6 M Np(V), 

500 g L-1 treated NABS in 0.1 M NaClO4 at atmospheric PCO2 ...................................... 54 

Figure 2.28a Model calibration with a single surface species (≡XOHNpO2

+

) and a single data set, 

5x10-7 M Np(V), 50 g L-1 treated NABS in AGW-A at atmospheric PCO2 ..................... 69 

Figure 2.28b Model calibration with a single surface species (≡XOHNpO2

+

) and a single data set, 

5x10-7 M Np(V), 125 g L-1 treated NABS in AGW-A at atmospheric PCO2 ................... 69 

Figure 2.29a Model calibration with a single surface species (≡XONpO2) and a single data set, 5x10-7

M Np(V), 50 g L-1 treated NABS in AGW-A at atmospheric PCO2 ................................ 69 

Figure 2.29b Model calibration with a single surface species (≡XONpO2) and a single data set, 5x10-7

M Np(V), 125 g L-1 treated NABS in AGW-A at atmospheric PCO2 .............................. 69 

Figure 2.30a Model calibration with two surface species (≡XOHNpO2

+

 and ≡XONpO2) and a single 

data set; 5x10-7 M Np(V), 50 g L-1 treated NABS in AGW-A at atmospheric PCO2 ....... 71 

Figure 2.30b Model calibration with two surface species (≡XOHNpO2

+

 and ≡XONpO2) and a single 

data set, 5x10-7 M Np(V), 125 g L-1 treated NABS in AGW-A at atmospheric PCO2 .... 71 

Figure 2.31a Model calibration including a neptunyl carbonato ternary surface complex 

(≡XONpO2(HCO3)2

2-) and a single data set, 5x10-7 M Np(V), 50 g L-1 treated NABS in 

AGW-A at atmospheric PCO2 .......................................................................................... 71 

Figure 2.31b Model calibration including a neptunyl carbonato ternary surface complex 

(≡XONpO2(HCO3)2

2-) and a single data set, 5x10-7 M Np(V), 125 g L-1 treated NABS in 

AGW-A at atmospheric PCO2 .......................................................................................... 71 

Figure 2.32a Model calibration with both AGW-A data sets, 5x10-7 M Np(V), 50 and 125 g L-1 treated 

NABS at atmospheric PCO2, with a 1-site type model..................................................... 72 

Figure 2.32b Model calibration with both AGW-A data sets, 5x10-7 M Np(V), 50 and 125 g L-1 treated 

NABS at atmospheric PCO2, 2-site type model with 99% weak and 1% strong sites...... 72 

Figure 2.32c Model calibration with both AGW-A data sets, 5x10-7 M Np(V), 50 and 125 g L-1 treated 

NABS at atmospheric PCO2 with incorporation of surface protolysis reactions. Surface 

species include ≡XOHNpO2

+

, ≡XONpO2 and ≡XONpO2(HCO3)2

2- ................................ 72 

Figure 2.33a Test of model performance for higher Np(V) concentration AGW-A data sets without 

additional parameter optimization .................................................................................... 73 

Figure 2.33b Test of model performance for higher Np(V) concentration AGW-A data sets with 

additional log K optimization ........................................................................................... 73 

Figure 2.34a Test of model performance to Np(V) sorption isotherm data sets without additional 

parameter optimization using the best fit model (Figure 2.32b)....................................... 75 
xiii

Figure 2.34b Test of model performance to Np(V) sorption isotherm data sets with additional log K 

optimization ...................................................................................................................... 75 

Figure 2.34c Np(V) surface species distribution for the model simulation presented in Figure 2.31b 

with the 5x10-7 M Np(V), 125 g L-1 treated NABS in AGW-A at atmospheric PCO2 data 

set...................................................................................................................................... 75 

Figure 2.34d Model calibration for the Np(V) sorption isotherms using ≡XONpO2 and 

≡XONpO2(HCO3)2

2- with a range of total Np(V) concentrations from 2x10-7 M to 3x10-6

M, 125 g L-1 treated NABS, in AGW-3 (pH 8) at atmospheric PCO2 or in AGW-6 (pH 

6.8) at 8% PCO2 ................................................................................................................ 75 

Figure 2.35a Test of model performance to Np(V) sorption isotherm data sets without additional 

parameter optimization ..................................................................................................... 77 

Figure 2.35b Test of model performance to Np(V) sorption isotherm data sets with additional log K 

optimization. ..................................................................................................................... 77 

Figure 2.35c Test of model performance to Np(V) sorption isotherm data sets with additional 

parameter optimization and exclusion of a ≡XOHNpO2

+

 surface species........................ 77 

Figure 2.36a Test of model performance for the 5x10-7 M Np(V) data sets using 50, 125 and 500 g L-1

treated NABS in 0.1 M NaClO4 with additional log K optimization................................ 78 

Figure 2.36b Test of model performance for the 5x10-7 M Np(V) data set using 500 g L-1 treated NABS 

in 0.1 M NaClO4 with additional log K optimization and an additional fit showing the 

effect of increasing the importance of the ternary neptunyl carbonate surface species.... 78 

Figure 2.37a Model simulation of 100 mg L-1 HA sorption to 500 g L-1 treated NABS at atmospheric 

PCO2 in 0.1 M NaClO4 without Np(V) present.. ............................................................. .80 

Figure 2.37b Model simulation for 100 mg L-1 HA sorption to 500 g L-1 treated NABS at atmospheric 

PCO2 in 0.1 M NaClO4 with 5x10-7 M Np(V) present..................................................... .80 

Figure 2.38 Model simulation of 5x10-7 M Np(V) to 500 g L-1 treated NABS in the presence of 100 

mg L-1 HA sorption at atmospheric PCO2 in 0.1 M NaClO4. ........................................... 81 

Figure 3.1. Kinetics of U(VI) adsorption on the composite sample of Forty-Mile Wash subsurface 

sediments (FMW-COMP) in artificial groundwater solution (FMW-2).....................85 

Figure 3.2. U(VI) sorption data on the composite sample of Forty Mile Wash subsurface sediments 

(FMW-COMP) in artificial groundwater solutions at low ionic strength. ..................85 

Figure 3.3. U(VI) sorption data on the composite sample of Forty Mile Wash subsurface sediments 

(FMW-COMP) in artificial groundwater solutions at low ionic strength as a function of 

dissolved U(VI) and PCO2. Data expressed as log Kd for U(VI) sorption versus log 

dissolved U(VI) concentration.....................................................................86 
xiv

Figure 3.4. U(VI) sorption data on the composite sample of Forty Mile Wash subsurface sediments 

(FMW-COMP) in artificial groundwater solutions at low ionic strength as a function of 

dissolved U(VI) and PCO2. Data expressed as percent U(VI) adsorbed versus log 

dissolved U(VI) concentration..................................................................86 

Figure 3.5. U(VI) sorption data on the composite sample of Forty Mile Wash subsurface sediments 

(FMW-COMP) in artificial groundwater solution at ionic strength of 0.1M..............87 

Figure 3.6. U(VI) sorption data on the composite sample of Forty Mile Wash subsurface sediments 

(FMW-COMP) in artificial groundwater solution at ionic strength of 0.1M as a function 

of dissolved U(VI) and PCO2. Data expressed as log Kd for U(VI) sorption versus log 

dissolved U(VI) concentration...................................................................87 

Figure 3.7. U(VI) sorption data on the composite sample of Forty Mile Wash subsurface sediments 

(FMW-COMP) in artificial groundwater solution at ionic strength of 0.1M as a function 

of dissolved U(VI) and PCO2. Data expressed as percent U(VI) adsorbed versus log 

dissolved U(VI) concentration....................................................................88 

Figure 3.8a. Comparison of U(VI) sorption data and surface complexation model calculations for the 

composite sample of Forty Mile Wash subsurface sediments (FMW-COMP) in artificial 

groundwater solutions at low ionic strength as a function of dissolved U(VI) and PCO2. 

Data expressed as log Kd for U(VI) sorption versus log dissolved U(VI) concentration 

and shown with large symbols....................................................................91 

Figure 3.8b. Comparison of U(VI) sorption data and surface complexation model calculations for the 

composite sample of Forty Mile Wash subsurface sediments (FMW-COMP) in artificial 

groundwater solutions at low ionic strength as a function of dissolved U(VI) and PCO2. 

Data expressed as log Kd for U(VI) sorption versus log dissolved U(VI) concentration 

and shown with large symbols....................................................................91 

Figure 3.9. Comparison of U(VI) sorption data and surface complexation model calculations for the 

composite sample of Forty Mile Wash subsurface sediments (FMW-COMP) in artificial 

groundwater solutions at an ionic strength of 0.1M as a function of dissolved U(VI) and 

PCO2. Data expressed as log Kd for U(VI) sorption versus log dissolved U(VI) 

concentration and shown with large symbols...................................................92 

Figure 3.10. Comparison of selected U(VI) sorption data and surface complexation model calculations 

for the composite sample of Forty Mile Wash subsurface sediments (FMW-COMP) in 

artificial groundwater solutions at low ionic strength. Data expressed as percent U(VI) 

adsorbed versus alkalinity and shown with large symbols....................................92 

Figure 4.1. Percent Ni adsorbed as a function of pH and total Ni concentration for suspensions with 

400 g/L of Cape Cod aquifer sand composite in artificial groundwater solution...........96 

Figure 4.2. Ni adsorption isotherms for Cape Cod aquifer sand composite in artificial groundwater 

solution at pH 5.4 and 6.1..........................................................................96 

Figure 4.3. Comparison of GC model simulation and experimental data for percent Ni adsorbed as a 

function of pH and total Ni concentration, for suspensions with 400 g/L of Cape Cod 

aquifer sand composite in artificial groundwater solution.....................................98 
xv

Figure 4.4. Comparison of GC model simulations and experimental data for Ni adsorption 

isotherms for Cape Cod aquifer sand composite in artificial groundwater solution 

at pH 5.4 and 6.1 ....................................................................................98 
xvi
xvii

TABLES 

Table 2.1 Equilibrium Constants for Select Np(V) Aqueous Phase Reactions .................................. 8 

Table 2.2 Humic Binding Model Parameters and Best Fit Values ..................................................... 9 

Table 2.3 Studies Of Np(V) Sorption To Pure Mineral Phases ........................................................ 12 

Table 2.4 Np(V) Sorption Spectroscopic Studies ............................................................................. 22 

Table 2.5 Studies Of Np(V) Sorption To Complex Geomedia......................................................... 23 

Table 2.6 Studies Of Np(V) Sorption In The Presence Of Humic Substances................................ 29 

Table 2.7 NABS Surface Area Distributions And Weight Percent For Various Grain 

 Size Fractions.................................................................................................................... 31 

Table 2.8 Composition Of Artificial Groundwater Solutions........................................................... 31 

Table 2.9 Np(V) Sorption Isotherm Experimental Variables ........................................................... 34 

Table 2.10 Np(V) Sorption Edge Experimental Variables ................................................................. 34 

Table 2.11 Humic Acid Sorption Experiment Variables ..................................................................... 34 

Table 2.12 Error Calculations Using The 95% Confidence Intervals From Regression Analysis Of 

Np(V) Isotherm Experimental Data.................................................................................. 41 

Table 2.13 Surface Complexation Modeling Studies Of Np(V) Sorption To Pure Mineral Phases.... 56

Table 2.14 Plausible Np(V) Surface Reactions Considered in Models ............................................... 67 

Table 2.15 Summary Of Adjustable Parameters And Goodness-Of-Fit: One-Site Models................ 68 

Table 2.16 Summary Of Adjustable Parameters And Goodness-Of-Fit: Two-Site Models............... 74 

Table 2.17 Summary Of Adjustable Parameters And Goodness-Of-Fit: Isotherm Data .................... 76 

 

Table 2.18 Summary Of Adjustable Parameters And Goodness-Of-Fit: NaClO4 Solution................ 79 

Table 2.19 Humic Acid Solution Reactions........................................................................................ 80 

Table 2.20 Humic Acid Surface Reactions......................................................................................... 81 

Table 3.1. U(VI) Surface Reactions Considered for the Forty-Mile Wash GC Model ................ 90 

Table 4.1. Ni Surface Reactions Considered for the Cape Cod GC Model .......................... .. 97 

Table 5.1. Characteristics of Surface Complexation Modeling Approaches for Environmental 

Adsorbents .........................................................................................101 
xviii
 

1

1 APPLICATIONS OF SURFACE COMPLEXATION MODELING: 

SCIENTIFIC BACKGROUND 

1.1 Introduction 

Public and private organizations in the United 

States and other nations are cleaning up 

hundreds of sites contaminated with uranium 

(U) and other contaminants that were released to 

the environment or that remain in temporary 

storage facilities (Crowley and Ahearne, 2002).

These wastes contain high levels of 

radionuclides with long half-lives, and in most 

cases, the wastes will be disposed of in 

engineered facilities constructed underground in 

specific types of geologic formations. The most 

likely pathway for radionuclides to reach the 

biosphere from these repositories is by transport 

of dissolved radionuclides in groundwater (or 

transport of colloidal particles with adsorbed 

radionuclides).

In addition to the nuclear waste disposal 

problem, numerous sites throughout the United 

States are contaminated by radionuclides, 

especially U and thorium (Th) and their byproducts, due to mining, milling and other 

industrial processes (Morrison and Cahn, 1991). 

The United States Nuclear Regulatory 

Commission (USNRC) is responsible for the 

regulatory oversight of site remediation and 

clean up, and possible release of sites for public 

or unrestricted use. Licensing decisions are 

made based on performance assessment (PA) 

calculations that utilize numerical models to 

estimate the increase in radioactivity exposure to 

the biosphere at selected locations. 

Contamination is often limited to the soils, 

subsurface sediments, and shallow aquifers at 

these sites. 

Prediction of the fate and transport of 

radionuclides is of paramount importance in 

evaluating remediation schemes and in 

quantifying the risk of contamination to human 

or ecosystem health. To make a calculation of 

radioactive exposure risk in PA, it is necessary 

to calculate the total amount of each 

radionuclide that will be present at selected 

locations as a function of time. A solute 

transport model is typically used to describe the 

physical processes of advection and dispersion 

that may transport radionuclides in groundwater 

from the source location to a site of ingestion. 

In addition to these hydrophysical processes, the 

transport of many radionuclides can be strongly 

influenced by reactive chemical processes, 

including aqueous complexation, sorption, 

precipitation and dissolution, and redox 

reactions. However, in practice, many reactive 

transport models ignore the chemical complexity 

of aqueous complexation and sorption processes 

and utilize the distribution coefficient (constant 

Kd) approach to describe the retardation of 

radionuclide contaminants (Bethke and Brady, 

2000; USEPA, 1999). Although sorption is only 

a part of the overall PA calculations, retardation 

of radionuclide transport in the far-field 

geosphere zone may be extremely important in 

reducing the risk of biosphere exposure of 

certain radionuclides to levels that are in 

compliance with regulations. 

The transport of actinide elements and other 

radionuclides in porous media can be strongly 

influenced by the competitive effects of the 

formation of mobile solution complexes and 

immobile adsorbed species. These elements can 

form many species in natural waters as a result 

of hydrolysis and complexation reactions. The 

distribution of aqueous species can be highly 

dependent on chemical conditions, especially pH 

and the concentrations of complexing ligands, 

such as carbonate ions (Curtis et al., 2006; Davis 

et al., 2004; Clark et al., 1995). In natural 

waters, important complexing ligands for U(VI) 

and Np(V) include hydroxide, carbonate and 

dissolved organic carbon; such ligands may 

compete with adsorption sites for complexation 

of UO2

2+ (and other actinyl ions) and decrease 

the extent of adsorption via the formation of 

nonadsorbing aqueous complexes. The 

influence of some ligand interactions can be 

very complex, as demonstrated by the observed 

increased adsorption of metals in the presence of 

certain ligands (Davis and Leckie, 1978). 

However, in some cases, the effects of 
 

2

complexing ligands can be modeled in a 

straightforward way by assuming that the 

aqueous complexes formed do not adsorb. For 

example, Kohler et al. (1996) showed that the 

adsorption of U(VI) on quartz in the presence of 

fluoride (F) ligands could be modeled as a 

competition between the quartz surface and 

aqueous F for complexation of U(VI). 

Application of the Kd concept to inorganic 

contaminants can be problematic because the 

parameter is so sensitive to aqueous chemical 

conditions. For example, the Kd for U(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%) to 1% (Davis et al., 2004; 

USNRC, 2003). This is an important variation 

to understand, because the pCO2 in aquifers 

commonly reaches values of 1-5%, 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 

radionuclides that form strong aqueous 

complexes with carbonate. 

The quality of thermodynamic data for 

radionuclide solubilities and aqueous speciation 

has been steadily increasing in recent years and 

the data are now available in critically reviewed 

compilations (Guillaumont et al., 2003; 

Langmuir, 1997; Silva et al., 1995; Grenthe et 

al., 1992). In contrast, the choice of Kd values in 

PA modeling is generally made based on expert 

judgement of available experimental data for 

individual radionuclides and various rock 

materials. The expert judgement may include an 

evaluation of laboratory measurements of Kd

values from batch or column experiments with 

site-specific materials in contact with solutions 

of varying chemical composition. In addition to 

measurements with site-specific materials, 

databases of Kd values have been assembled for 

the sorption of radionuclides on a variety of 

single mineral phases (Turner, 1995), natural 

materials, including soils and rock powders 

(USEPA, 1996; McKinley and Scholtis, 1993; 

Sheppard and Thibault, 1990; Looney et al., 

1987; Baes and Sharp, 1983; Isherwood, 1981), 

and engineered barrier materials pertinent to 

nuclear waste disposal (Berry, 1992; Krupka and 

Serne, 1996). 

One of the potentially large uncertainties in PA 

model calculations arises from the choice of Kd

values for individual radionuclides. Depending 

on the type and purpose of the PA modeling 

study, either a single Kd value may be chosen for 

each radionuclide or a probability distribution 

function (PDF) may be derived that 

encompasses a range of Kd values. The 

uncertainty in the choice of Kd values or in the 

PDF arises from several sources, e.g. 1) 

experimental error, 2) extrapolation or 

interpolation of values to chemical conditions or 

rock types other than those used in actual 

experimental measurements of Kd, and 3) the 

scaling of Kd values measured for rock powders 

to the values expected for intact rocks in the sitespecific, geologic setting. 

 

Constant Kd models for adsorption do not 

adequately account for spatial variability in the 

composition of adsorbing phases or for variable 

chemical conditions, such as pH, ionic strength, 

alkalinity, or concentrations of complexing 

ligands that may be encountered along a 

groundwater flow path (Curtis et al., 2006; 

Davis et al., 2004; Kohler et al., 2004; USNRC, 

2003; Kohler et al., 1996). For a variety of 

reasons, the range of Kd values that may need to 

be considered for each radionuclide/rock 

combination can be quite large. One can 

consider spatial and temporal variability in 

chemical conditions in PA modeling with Kd

values by separating the calculations into 

separate blocks of time or hydrologic units in 

space. Although the effects of changes in 

chemical conditions on the solubilities of 

radionuclides can usually be calculated in a 

straightforward manner, the effects of variable 

chemical conditions on adsorption and the 

choice of Kd values is more complex (Curtis et 

al., 2004; Davis et al., 2004; USEPA, 1999; 

Kohler et al., 1996). In order to be 

“conservative”, large ranges of Kd values may 

need to be estimated by expert judgement in 

order to account for possible changes in 

chemical conditions and for other sources of 
 

3

error. The uncertainties in these ranges are 

difficult to assess quantitatively without doing 

large numbers of experiments. 

1.2 Surface Complexation 

Modeling 

Although a number of variations in the modeling 

approach have been developed, there are four 

fundamental tenets in all surface complexation 

models (SCM) (NEA, 2005; Davis and Kent, 

1990): 

• Mineral surfaces are composed of 

specific chemical functional groups that react 

with dissolved solutes to form surface species 

(coordinative complexes or ion pairs), in a 

manner analogous to complexation reactions in 

homogeneous solution. 

• The equilibria of adsorption reactions 

can be described by mass action equations. If 

desired, correction factors to these equations 

may be applied to account for variable 

electrostatic energy, using electrical double layer 

theory. 

• The apparent binding constants 

determined for the mass action equations are 

empirical parameters related to thermodynamic 

constants by the rational activity coefficients of 

the surface species. 

• Electrical charge at the surface is 

determined by the chemical reactions of the 

mineral functional groups, including acid-base 

reactions and the formation of ion pairs and 

coordinative complexes. 

In contrast to models using constant Kd, SCM 

have the capability of describing changes in 

contaminant adsorption as chemical conditions 

and aqueous speciation vary, and SCM can be 

readily incorporated within solute transport 

models (Curtis et al., 2006; USNRC, 2003; Kent 

et al., 2000). Previous investigations have shown 

that the SCM concept can be applied to great 

advantage in natural systems by employing a 

simple Generalized Composite (GC) approach to 

describe metal or radionuclide adsorption (Davis 

et al., 2004; Curtis et al., 2004; Davis et al., 

2002). In the GC approach, the surface of the 

mineral assemblage is considered too complex 

to be quantified in terms of the contributions of 

individual phases to adsorption. Instead, it is 

assumed that adsorption can be described by 

SCM equilibria written with “generic” surface 

functional groups, with the stoichiometry and 

formation constants for each SCM mass law 

determined by fitting experimental data for the 

mineral assemblage (Davis et al., 2004). 

Fortunately, the uncertainty in Kd values that 

arises from variations in the chemical 

composition of groundwater or mineralogical 

composition of the adsorbent phases along a 

flow path can be reduced with the use of surface 

complexation modeling (SCM) to describe 

adsorption (Curtis et al., 2006; USNRC, 2003; 

Kent et al., 2000). In addition, certain large 

sources of uncertainty (e.g., scaling from Kd

values measured for rock powders to the 

surfaces of a fractured rock system) can be more 

easily addressed with SCM, because SCM is 

based on defining radionuclide adsorption per 

unit surface area rather than per unit mass. 

SCM could be of significant value to PA if it 

was used to determine the range of Kd values 

that need to be considered and provide a 

scientific basis for the range of values chosen 

(NEA, 2005). Uncertainties in SCM parameters 

can be less than the uncertainties in Kd values 

(when considered as robust over a range of 

chemical conditions), and SCM uncertainties 

may be more easily quantified. For example, a 

recent report on batch studies of U(VI) 

adsorption on montmorillonite presented Kd

values that ranged by more than 4 orders of 

magnitude over the pH range of 6.5 to 8.5 

(Pabalan and Turner, 1996). In a series of 

papers, Davis et al. (2004), Kohler et al. (2004), 

and Curtis et al. (2004) showed that the Kd

values for U(VI) in an alluvial aquifer in 

Colorado varied by more than one order of 

magnitude because of variable chemical 

conditions in the groundwater. In each of these 

cases the datasets could be described by an SCM 

with a small number of independent parameters 

that remained constant and had comparatively 

little uncertainty in their values. 
 

4

Using a range of 4 orders of magnitude in the 

uncertainty of Kd values (Pabalan and Turner, 

1996) in PA calculations could lead to increased 

costs for waste cleanup at an industrial site or to 

rejection of an alternative waste disposal 

scenario. If used properly, SCM for 

radionuclide adsorption has the potential to 

increase the confidence and scientific credibility 

of PA transport modeling, by reducing the 

uncertainty in quantifying retardation and 

providing a means of quantifying that 

uncertainty. In addition, SCM has the potential 

to lower the estimated remediation costs of sites 

contaminated with radionuclides (or the 

feasibility of a disposal scenario) by decreasing 

the uncertainty of Kd values (and the associated 

safety factor applied in PA modeling). 

The use of equilibrium geochemical models to 

calculate radionuclide solubilities and aqueous 

speciation is well established in the PA field. 

Surface complexation modeling is an extension 

of this thermodynamic modeling approach to 

include the reactions between dissolved species 

and the functional groups (ligands) present on 

mineral surfaces (NEA, 2005). The adsorption 

reactions are included as part of the network of 

chemical reactions that require equilibration, 

rather than as a condition-dependent partitioning 

coefficient, like Kd. Once the model is 

calibrated, it allows predictive calculations for a 

range of geochemical conditions without 

changing the values of the stability constants for 

radionuclide adsorption. The adsorption 

equations can be included efficiently in transport 

simulations in which there are chemical 

gradients in the subsurface environment rather 

than constant chemical conditions (Curtis et al., 

2006; Kent et al., 2000). 

1.3 Project Goals 

In a previous study (USNRC, 2003), the surface 

complexation modeling approach was 

demonstrated successfully for a field site 

contaminated with uranium. The objective of 

that project was to demonstrate that, despite the 

complexities inherent at field sites and in 

modeling sorption by natural mineral 

assemblages, the surface complexation concept 

could be utilized to great advantage to describe 

U(VI) sorption and retardation in a field setting. 

In this report we demonstrate that the surface 

complexation concept can also be used to 

describe the sorption of other radionuclides 

(Np(V), Ni) on complex mineral assemblages 

and U(VI) sorption on other sediment samples. 

Generalize Composite models are developed for 

simulating: 1) Np(V) sorption by the subsurface 

sediments collected from an alluvial aquifer at 

the UMTRA site near Naturita, Colorado, 2) 

U(VI) sorption by aquifer sediments from the 

Forty-Mile Wash aquifer, Nye County, Nevada, 

and 3) Ni sorption by aquifer sediments from 

Cape Cod, Massachusetts. 

1.4 Overview of Report 

Data were collected within this project to enable 

development of a GC modeling approach for 

description of radionuclide adsorption by 

subsurface materials collected from three 

different field sites. The studies were completed 

under conditions of variable aqueous chemistry, 

which caused significant variation in the 

speciation of U(VI) or Np(V). The study of 

Np(V) sorption by Naturita (Colorado) aquifer 

sediments and development of a GC model to 

describe its sorption is presented in Section 2. 

The study of U(VI) sorption and modeling with 

Forty-Mile Wash sediments is summarized in 

Section 3. Ni sorption and modeling with Cape 

Cod aquifer sediments is described in Section 4. 

Section 5 summarizes the conclusions of the 

project and discusses the significance of the 

project results to PA modeling. The project 

impacts the evaluation of appropriate methods to 

determine Kd values for radionuclides, the 

choice of models to describe radionculide 

sorption, and the extent of adsorption 

uncertainties on PA modeling.

 
 

5

2 NEPTUNIUM(V) SORPTION TO AN ALLUVIAL AQUIFER 

SEDIMENT: APPLICABILITY OF SURFACE COMPLEXATION 

MODELING

2.1 Background 

Neptunium-237 (237Np) is a component of spent 

nuclear fuel and other radioactive wastes. 237Np 

is considered a radionuclide of significant 

concern due to its long half-life (2.14 x106

years), biological toxicity, high chemical 

stability of the pentavalent oxidation state and 

low sorption affinity of the neptunyl (NpO2

+

) 

cation (Choppin, 1983; Dozol and Hagemann, 

1993; Silva and Nitsche, 1995). The fate of 

Np(V) in natural systems is largely controlled by 

the extent NpO2

+

 and its complexes sorb to solid 

surfaces (Morse and Choppin, 1991). A 

considerable body of research has focused on the 

sorption of Np(V) to pure mineral phases (e.g. 

Girvin et al., 1991; Tochiyama et al., 1996; 

Bertetti et al., 1996; Del Nero et al., 1998). 

Np(V) sorption is shown to strongly depend on 

surface characteristics and variations in chemical 

composition such as pH, carbonate 

concentration, ionic strength and competing 

ligands. The data and text of this section are 

mostly taken from Moran (2007), whose thesis 

work was funded by this project. 

2.1.1 Neptunium Aqueous Geochemistry 

Np can exist in the III, IV, V, VI and VII 

oxidation states in aqueous solutions with the 

pentavalent state being the most stable under 

oxidizing conditions found in many aquatic 

systems (Dozol and Hagemann, 1993). Similar 

to U(VI), Np(V) exists as a linear trans dioxo 

cation in aqueous solutions. However, the lower 

effective charge density of NpO2

+

 compared to 

UO2

2+ (+2.2 and +3.3, respectively) leads to the 

much lower complexation strength of Np(V) 

compared to U(VI) (Choppin and Unrein, 1976). 

The unusual stability of NpO2

+

 compared to 

other pentavalent actinides is due to its 

resistance to hydrolysis, lack of polynuclear 

complex formation, and the fact that oxidation 

state disproportionation occurs only in highly 

acidic solutions (Burney and Harbour, 1974). 

In a CO2-free atmosphere, the hydroxo 

complexes (NpO2OH° and NpO2(OH)2

-

) 

dominate at high pH (Maya, 1983; Lierse et al., 

1985; Neck et al., 1992). Figure 2.1 illustrates 

the occurrence of aqueous neptunyl hydroxo 

complexes at a total Np(V) concentration of 

2.0x10-6 M, in 0.1 M NaClO4 under CO2-free 

conditions. The thermodynamic constants 

applicable to this study, compiled within the 

NEA critical reviews of all available 

thermodynamic data for Np(V) complexation 

with inorganic ligands, are presented in Table 

2.1 (Lemire et al., 2001; Guillaumont et al., 

2003). It is important to note that the speciation 

diagrams presented here only take solution 

phase reactions into consideration and only 

show Np(V) aqueous complexes that contribute 

>1% to the total species distribution. Solubility 

calculations estimate that crystalline NpO2OH 

may precipitate at ~ pH > 8 under the CO2-free 

conditions presented in Figure 2.1. 

In the presence of carbonate ligands, aqueous 

carbonato complexes dominate Np(V) speciation 

at near-neutral pH (Clark et al., 1995). A wealth 

of spectroscopic (Neck et al., 1994; Runde and 

Kim, 1995; Clark et al., 1996) and solubility 

(Neck et al., 1994; Runde and Kim, 1995) data 

support the existence of the aqueous carbonato 

complexes, NpO2CO3

-

, NpO2(CO3)2

3- and 

NpO2(CO3)3

5-. A number of authors have found 

evidence for a mixed Np(V) hydroxo-carbonato 

complex, but the exact stoichiometry has yet to 

be determined. As Figure 2.2 demonstrates, 

under conditions similar to those encountered in 

the laboratory, 0.1 M NaClO4 and atmospheric 

partial pressure of CO2 (PCO2), the NpO2

+

carbonato complexes prevail over the hydroxo 

complexes, with a small contribution to Np(V) 

speciation by the mixed hydroxo carbonato 

complex in the alkaline pH range, assuming 

NpO2(CO3)2OH4−

 is the correct stoichiometry. 

Figures 2.3 and 2.4 show how the shift towards 

dominance of carbonate complexes is extended 

as the PCO2 increases from atmospheric to 2% 

PCO2 and 8% PCO2, respectively.
 

6

0

10

20

30

40

50

60

70

80

90

100

7.0 7.5 8.0 8.5 9.0 9.5 10.0 10.5 11.0 11.5 12.0

pH

Percent of Total Np(V)

CO2-Free Atmosphere

[Np(V)]T = 2.0x10-6 M

0.1 M NaClO4

NpO2

+

NpO2OH0

NpO2(OH)2

-

Figure 2.1. Np(V) species distribution as a function of pH in 0.1 M NaClO4 with a total 

Np(V) concentration of 2.0x10-6 M under CO2-free conditions. 

0

10

20

30

40

50

60

70

80

90

100

5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0

pH

Percent of Total Np(V)

Atmospheric PCO2 

[Np(V)]T = 2.0x10-6 M

0.1 M NaClO4

NpO2

+

NpO2(CO3)-

NpO2(CO3)2

3-

NpO2(CO3)3

5-

NpO2(CO3)2OH4-

Figure 2.2. Np(V) species distribution as a function of pH in 0.1 M NaClO4 with a total 

Np(V) concentration of 2.0x10-6 M at atmospheric PCO2. 
 

7

0

10

20

30

40

50

60

70

80

90

100

5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0

pH

Percent of Total Np(V)

2% PCO2 

[Np(V)]T = 

2.0x10-6 M

0.1 M NaClO4

NpO2

+

NpO2CO3

-

NpO2(CO3)2

3-

NpO2(CO3)3

5-

Figure 2.3.Np(V) species distribution as a function of pH in 0.1 M NaClO4 with a total 

Np(V) concentration of 2.0x10-6 M at 2% PCO2. 

0

10

20

30

40

50

60

70

80

90

100

5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0

pH

Percent of Total Np(V)

8% PCO2 

Atmosphere

[Np(V)]T = 

2.0x10-6 M

0.1 M NaClO4

NpO2

+

NpO2CO3

-

NpO2(CO3)2

3-

NpO2(CO3)3

5-

Figure 2.4. Np(V) species distribution as a function of pH in 0.1 M NaClO4 with a total 

Np(V) concentration of 2.0x10-6 M at 8% PCO2.
 

8

Table 2.1 Equilibrium Constants For Select Np(V) Aqueous Phase Reactions 

Reactions Log K (I = 0) 

 NpO2

+

 + H2O ↔ NpO2OH(aq)o + H+ -11.3

 NpO2

+

 + 2H2O ↔ NpO2(OH) 2

- + 2H+

 -23.60 

NpO2

+

 + CO3

2-

 ↔ NpO2CO3

-

 4.96 

NpO2

+

 + 2CO3

2-

 ↔ NpO2(CO3) 2

3- 6.53 

 NpO2

+

 + 3CO3

2-

 ↔ NpO2(CO3) 3

5- 5.47 

NpO2

+

 + 2CO3

2- + OH-

 ↔ NpO2(CO3) 2 OH4- 8.70

I = 0, ionic strength corrected to zero using specific ion interaction (SIT) equations 

Under CO2-free conditions, the stable Np(V) 

solid phases are Np2O5(s) and NpO2OH(s) 

(Lierse et al., 1985; Neck et al., 1992; Merli and 

Fuger, 1994; Efurd et al., 1998). Solid Np(V)-

carbonate compounds become the stable phases 

with the addition of CO2 ( Maya, 1983; Grenthe 

et al., 1986; Neck et al., 1994; Neck et al., 1995; 

Novak et al., 1997; Al Mahamid et al., 1998). 

The stable solid phases in carbonate containing 

systems are MNpO2CO3•xH2O(s) and 

M3NpO2(CO3)2(s) where M = Na+

 or K+. The 

relatively high solubility of these Np(V) solid 

phases is likely to facilitate the transport of Np 

from underground repositories into the 

environment (USNRC, 2004). 

In some instances, naturally occurring organic 

ligands, such as humic acid (HA) and fulvic 

acid, can compete with carbonate to form 

complexes with NpO2

+

 (Leiser and Mühlenweg, 

1988). Reported stability constant values vary by 

over two orders of magnitude. This substantial 

variability can be attributed to a number of 

factors: 1) the method used to distinguish free 

NpO2

+

 from complexed NpO2

+

; 2) the model 

used to determine the free HA concentration; 

and 3) variations in the Np(V) concentration 

(Moran, 2007). 

In this study, the discrete log K model developed 

by Westall et al. (1995) was used to approximate 

both the acid-base character of Suwannee River 

humic acid (HA) and Np(V) speciation in the 

presence of Suwannee River HA. In this model, 

five discrete ligands (HLi) are assumed to exist 

that can describe the macroscopic acid-base and 

complexation behavior of an HA sample. The 

discrete log K spectrum model was originally 

developed to model 1:1 interactions of Co(II) 

with Leonardite HA. The model has since been 

successfully used to determine U(VI):HA and 

U(VI):FA stability constants with 1:1 and 1:2 

uranyl-ligand complexes (Lenhart, 1997; 

Lenhart et al., 2000). Kantar et al. (2005) used a 

similar approach to obtain stability constants for 

U(VI):Fe(III):citrate complexation using 1:1:1 

and 1:1:2 ternary complexes. The model does 

not explicitly account for electrostatic effects, 

but rather incorporates electrostatics implicitly 

using a counterion binding constant. 

The model was applied in this study from the 

data and calibration of Moran (2007). Table 2.2 

contains the optimized values of each 

hypothetical HA ligand as well as the value for 

KNa, where one value was used for all 5 HLi

ligands. The model fits for Np(V) complexation 

with HA were conducted using only ligands HL1

– HL4 (Moran, 2007). The log β values at I = 0 

and the associated errors determined by Moran 

(2007) are: pH 7.0, log β = 4.28 ± 0.14; pH 8.0, 

log β = 4.49 ± 0.11; average log β = 4.39 ± 0.13. 

Using the average stability constant, distribution 

diagrams were developed to predict the 

environmental behavior of Np(V) in the 

presence of HA. At atmospheric PCO2, Np(V) 

speciation in the presence of 10 mg L-1 HA over 

a wide pH range (3-10) in 0.1 M NaClO4 show 

little importance of HA complexation in a 2x10-6

M Np(V) solution (Figure 2.5a). Figure 2.5b 

illustrates a greater importance of the Np(V)-HA 

complex as the HA concentration increases from 

10 to 100 mg L-1. Under these conditions, HA 

complexation plays a role in Np(V) speciation 

below pH 9. 
 

9

Table 2.2 Humic Binding Model Parameters and Best Fit Values* 

Reactions THLi (mmol L-1) THLi (mmol g-1) Log K (I = 0 M) 

HL1 = L1

-

 + H+ 0.298 0.608 -2 

HL2 = L2

-

 + H+ 0.807 1.65 -4 

HL3 = L3

-

 + H+ 0.713 1.46 -6 

HL4 = L4

-

 + H+ 0.283 0.578 -8 

HL5 = L5

-

 + H+ 0.332 0.678 -10 

Li

-

 + Na+ = NaLi 2.34 

 *Model fit to potentiometric data, THLi = Total HLi, Total HL = 4.97 mmol g-1 

0

10

20

30

40

50

60

70

80

90

100

5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0

pH

Percent of Total Np(V)

[Np(V)]T = 2.0x10-6 M

HA: 10 mg L-1

0.1 M NaClO4 

Atmospheric PCO2 

NpO2

+

NpO2HA0

NpO2(CO3)2

3-

NpO2(CO3)3

5-

NpO2(CO3)2OH4-

NpO2CO3

-

Figure 2.5a. Np(V) species distribution as a function of pH in 0.1 M NaClO4 with a total 

Np(V) concentration of 2.0x10-6 M, 10 mg L-1 HA at atmospheric PCO2. 
 

10

0

10

20

30

40

50

60

70

80

90

100

5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0

pH

Percent of Total Np(V)

[Np(V)]T = 2.0x10-6 M, HA: 100 mg L-1

0.1 M NaClO4, Atmospheric PCO2 

NpO2

+

NpO2HA0

NpO2(CO3)2

3-

NpO2(CO3)3

5-

NpO2(CO3)2OH4-

NpO2CO3

-

Figure 2.5b. Np(V) species distribution as a function of pH in 0.1 M NaClO4 with a total 

Np(V) concentration of 2.0x10-6 M, 100 mg L-1 HA at atmospheric PCO2. 

Figure 2.6 shows an estimate of the effect of 

PCO2 on 2x10-6 M Np(V) complexation with 

100 mg L-1 HA at pH 7 and pH 8 in 0.1 M 

NaClO4. At pH 7 and below, the Np(V)-HA 

complex primarily competes with the free NpO2

+

cation over the entire PCO2 range shown. 

Increasing the pH to 8 shows that the NpO2

+

-HA 

complex is only significant at low PCO2. The 

first NpO2

+

 carbonato complex becomes the 

primary Np(V) species as PCO2 increases to 

levels likely to be encountered in groundwater. 

The conditional stability constant (log β = 4.39) 

determined by Moran (2007) is consistent with 

log β values for Aldrich HA obtained by Siebert 

et al. (2001) at similar Np(V) concentrations. 

Siebert et al. (2001) used ultrafiltration 

experiments to obtain values between 3.7 and 

5.0 over a wide Np(V) concentration range (10-

14–10-4 M). In the more narrow Np(V) range of 

1x10-5 to 4x10-6 M, the log β values are between 

4.1 and 4.2 at pH 7 and 8 (Siebert et al., 2001). 

2.1.2 Neptunium(V) Sorption to Pure 

Mineral Phases 

The majority of recent Np sorption studies have 

focused on the interaction of Np(V) with pure 

mineral phases. The discussion below illustrates 

some key features of Np(V) sorption to 

representative minerals using specific studies 

from the literature. Table 2.3 contains an 

overview of the literature pertaining to Np(V) 

sorption to pure mineral phases. 

The pH dependence of Np(V) sorption to many 

different types of pure mineral phases is well 

documented. The seminal paper by Girvin et al. 

(1991) investigated Np(V) sorption to 

ferrihydrite at tracer Np(V) concentrations 

(4.5x10-13 M to 4.5x10-11 M) and PCO2 slightly 

below 10-3.5 atm. The Girvin et al. study showed 

that sorption increased rapidly from about zero 

at pH 4 to almost 100% by pH 8. At low Np(V) 

concentrations the total Np(V) concentration had 
 

11

little impact on sorption. In contrast, the 

ferrihydrite concentration (0.1, 0.4 and 1.0 g L1

) had a significant effect on sorption, resulting 

in a shift in the sorption edge to higher pH as the 

solid concentration was reduced. 

The Kohler et al. (1999) study of Np(V) sorption 

to goethite and hematite obtained results 

consistent with the Girvin et al. study. Kohler 

and coworkers also investigated the effect of 

PCO2 on Np(V) sorption. At 2% PCO2, sorption 

to goethite and hematite was significantly 

reduced above approximately pH 7.5 compared 

to samples obtained under atmospheric and CO2-

free conditions with the suggestion that weakly 

sorbing aqueous neptunyl carbonato complexes 

were responsible. Of interest, the experimental 

data collected under CO2-free conditions was 

well fit using a model without carbonate species 

while the data collected under atmospheric and 

2% PCO2 was well simulated using a model that 

included a ternary neptunyl carbonato surface 

complex. 

At Np(V) concentrations used for the Kohler et 

al. study (1.2x10-7 M to 1.3x10-6 M) the impact 

of reduced solid concentrations on sorption was 

greater than observed by Girvin and coworkers. 

For example, at 1.1x10-6 M Np(V) and 1 g L-1

hematite, sorption followed the same trend as 

observed by Girvin et al., while at 1.3x10-6 M 

Np(V) and 0.01 g L-1 hematite, Np(V) sorption 

was less than 10% over the entire pH range 

studied (pH 4 to 10). This flattening of the 

sorption edge was indicative of hematite surface 

site saturation. Calculations confirmed that the 

Np(V) concentration was in excess of the site 

concentration at 1.3x10-6 M Np(V) and 0.01 g L1

 hematite. 

Tochiyama et al. (1995) studied sorption of ~10-

13 M Np(V) to a number of naturally occurring 

and synthetic iron oxides at 20 g L-1. The study 

found that Np(V) Kd values varied by 2 to 4 

orders of magnitude at a given pH for the group 

of iron oxides investigated. Furthermore, Np(V) 

sorption affinity followed the trend: goethite (αFeOOH) > ferrihydrite (Fe2O3•H2O) > hematite 

(α-Fe2O3) ≈ magnetite (α-Fe3O4). This trend 

suggested that crystal structure was the primary 

variable controlling Np(V) sorption to iron 

oxides. Tochiyama et al. (1996) conducted 

Np(V) sorption experiments using naturally 

0

10

20

30

40

50

60

70

80

90

100

0.0% 0.4% 0.8% 1.2% 1.6% 2.0%

PCO2 (%)

Percent of Total Np(V)

[Np(V)]T = 2.0x10-6 M

HA: 100 mg L-1

0.1 M NaClO4 

Atmospheric PCO2

NpO2

+

NpO2CO3

-

NpO2HA

0 NpO2(CO3)2

3-

pH 7.0 pH 8.0

Figure 2.6. Np(V) species distribution as a function of PCO2 in 0.1 M NaClO4 with a total 

Np(V) concentration of 2.0x10-6 M, 100 mg L-1 HA at pH 7 and 8. 
12 

Table 2.3. Studies of Np(V) Sorption to Pure Mineral Phases (AGW = artificial groundwater, GW = 

Sorbent 

(Chemical Formula) 

Np(V) 

(237, 239Np) 

(mol L-1) 

pH 

range 

Solid 

Concentration

(g L-1) 

I 

(mol L-1)

PCO2

(Atm) 

Albite 

(NaAlSi2O8) 

<10-7 1.9, 7.5 

and 11.3 

20 GW 10-3.5 To

Al silicates 6x10-6 – 1x10-9 3-10 20 0.001 and 

0.1 

0 and 10-3.5 D

Alumina 

(γ-Al2O3) 

4x10-7- 

6x10-6

4-11 1 0.1 <10-3.5 

sealed vials 

Na

Sak

Alumina 1.0x10-14 4-10 0.2 0.1 0 RAlumina 1.02x10-6 10-13.6 12.5 1.0 10-3.5 Ya

Anorthite <10-7 1.9, 7.5 

and 11.3 

20 GW 10-3.5 To

Amorphous SiO2 1.0x10-14 4-10 1.2 0.1 0 R

Apatite (group) 

(Ca5(PO4)3(OH,F,Cl)) 

2.0x10-11 5-10 7.8 0.01-

AGW 

10-3.5 Be

Aragonite 

(CaCO3) 

1x10-4 – 1x10-13 7.8-8.2 3.4 ~0.05- 0.7 10-3.5 

K

M

Attapulgite 

((Mg,Al)2Si4O10 

(OH)·4(H2O)) 

2.0x10-11 5 and 8 7-12 0.01-

AGW 

10-3.5 Be

Bentonite 

(Al2O34SiO2H2O) 

6x10-7 2-8 10 0.01 10-3.5 
13 

Table 2.3 (continued). Studies of Np(V) Sorption to Pure Mineral Phases

Sorbent 

(Chemical Formula) 

Np(V) 

(237, 239Np) 

(mol L-1) 

pH range Solid 

Concentration

(g L-1) 

I 

(mol L-1) 

PCO2

(Atm) 

Bentonite <10-7 2.3, 8.7 

and 10.2 

20 GW 10-3.5 To

Biotite 

(K(Fe, Mg)3 

AlSi3O10(F,OH)2) 

2.0x10-11 5 and 8 7-12 0.01-AGW 10-3.5 

A

Biotite-natural 

and synthetic 

4x10-7- 

6x10-6

3-11 1.0 0.1 <10-3.5 

Sealed 

vials 

N

an

Boehmite 

(γ-AlOOH) 

4x10-7- 

6x10-6

3-11 1.0 0.1 <10-3.5 

Sealed 

vials 

N

an

Boehmite 2x10-7– 2x10-6 6.2 ~20 0.1 (KCl) 10-3.5 

T

Calcite 

(CaCO3) 

2.0x10-11 8-10 7.8 0.01-AGW 10-3.5 

A

Calcite 1x10-4 – 1x10-13 7.8-8.2 8.3 ~0.05- 0.7 10-3.5 

Ke

M

Calcite 8.84x10-6 – 

9.54x10--6

Atm 

PCO2= 

8.0-8.5 

N2 = 9.5 

250 AGWsvaried total 

dissolved 

solids 

10-3.5 and 

N2 (low O2

< 0.5 ppm)

Tic

Calcite 1.6x10-6 7.25-9 3.3-15 varied with 

pH (~0.6 to 

0.1) 

10-3.5 Be

Calcite not given 7.0-9.6 not given 0.1 10-3.5 Za
14 

Table 2.3 (continued). Studies of Np(V) Sorption to Pure Mineral Phases

Sorbent 

(Chemical Formula) 

Np(V) 

(237, 239Np) 

(mol L-1) 

pH range Solid 

Concentration

(g L-1) 

I 

(mol L-1)

PCO2

(Atm) 

R

Chlorite (group) 

(Generalized: 

(Fe,Mg, Al)6(Si, 

Al)4O10(OH)8) 

2.0x10-11 5 and 8 7-12 0.01-

AGW 

10-3.5 Beal

Chlorite (group) 8.84x10-6 – 

9.54x10--6

Atm 

PCO2= 

8.0-8.5 

N2 = 9.5 

250 AGWsvaried 

total 

dissolved 

solids 

10-3.5 and 

N2 (low O2

< 0.5 ppm) 

Tic

Clinoptilolite 

(Generalized:Na1.3

KI.2Ca1.55A16.2Si29.8

O72

•23H20) 

1x10-6 4 -11 4 and 8 0.01 and 

0.1 

0, capped 

and 10-3.5

Be

Clinoptilolite 

≈10-6 8.6 and 

9.0 

50 GW 10-3.5 T

Corundum 

(α-Al2O3) 

2.0x10-11 4-10 7.2 0.01-

AGW 

10-3.5 Beal

Corundumnatural and 

synthetic 

≈10-12 – 10-13 4-10 20 0.1 10-3.5 Toch

Diaspore 

(α-AlOOH) 

≈10-12 – 10-13 4-10 20 0.1 10-3.5 Toch

Epidote 

(Ca2(Al,Fe)3(SiO4)3

(OH))

8.84x10-6 – 

9.54x10--6

Atm 

PCO2= 

8.0-8.5 

N2 = 9.5 

250 AGWsvaried 

total 

dissolved 

solids 

10-3.5 and 

N2 (low O2

< 0.5 ppm) 

Tic
15 

Table 2.3 (continued). Studies of Np(V) Sorption to Pure Mineral Phases

Sorbent 

(Chemical Formula) 

Np(V) 

(237, 239Np) 

(mol L-1) 

pH range Solid 

Concentration

(g L-1) 

I 

(mol L-1)

PCO2

(Atm) 

R

Fe silicates 1x10-6 – 1x10-9 3-9 20 0.01 and 

0.1 

0 and 10-3.5 Del 

Ferrihydrite 

(Fe2O3•H2O) 

4.5x10-11-

4.5x10-13

4-9 0.1, 0.4 and 

1.0 

0.1 10-3.5 G

Fluorite 

(CaF2) 

2.0x10-11 4-9 7.4 0.01-

AGW 

10-3.5 Beal

Gibbsite-natural 

and synthetic (γAl(OH)3) 

≈10-12 – 10-13 4-10 20 0.1 10-3.5 Toch

Goethite-natural and synthetic (αFeOOH) 

4x10-7– 6x10-6 3-11 1.0 0.1 <10-3.5 

sealed vials 

Nak

Sako

Goethite 1.3x10-5 7.2 1 0.08 10-3.5 CoGoethite 8.84x10-6 – 

9.54x10--6

Atm 

PCO2= 

7.0-7.5 

N2 = 9.5 

250 AGWsvaried 

total 

dissolved 

solids 

10-3.5 and 

N2 (low O2

< 0.5 ppm) 

Tic

Goethite 2x10-7– 2x10-6 6.2 ~20 0.1 (KCl) 10-3.5 KunGoethite 4x10-7- 

6x10-6

4-11 1 0.1 10-3.5 Fu

Goethite-natural 

and synthetic 

≈10-12 3-9 20 0.1 10-3.5 Toch

Goethite 1.2x10-7 3-9 20 0.1 0,10-3.5 and 

10-2.7

Ko
16 

Table 2.3 (continued). Studies of Np(V) Sorption to Pure Mineral Phases

Sorbent 

(Chemical Formula) 

Np(V) 

(237, 239Np) 

(mol L-1) 

pH range Solid 

Concentration

(g L-1) 

I 

(mol L-1) 

PCO2

(Atm) 

Goethite 5.0x10-5 2-11 4 0.1 and 

~0.004 

GW 

0 and10-3.5 Sh

Gypsum 

CaSO4•2(H2O) 

8.84x10-6 – 

9.54x10--6

Atm 

PCO2= 

8.0-8.5 

N2 = 9.5 

250 AGWsvaried total 

dissolved 

solids 

10-3.5 and 

N2 (low O2

< 0.5 ppm)

Tic

Hausmanite 

(Mn3O4) 

1x10-6, 1x10-5

and 1x10-4

4-11 3.3 0.1 10-3.5 W

Hematitenatural and 

synthetic 

(α-Fe2O3) 

4x10-7- 

6x10-6

4-11 1 0.1 10-3.5 Nak

S

Hematite 8.84x10-6 – 

9.54x10--6

Atm 

PCO2= 

8.0-8.5 

N2 = 9.5 

250 AGWsvaried total 

dissolved 

solids 

10-3.5 and 

N2 (low O2

< 0.5 ppm)

Tic

Hematite-natural 

and synthetic 

≈10-12 3-9 20 0.1 10-3.5 To

Hematite 1.2x10-7- 

1.3x10-6

4-9.5 0.1 -5.75 0.005, 0.01, 

0.05 and 

0.1 

0, 

10-3.5 and 

10-2.7

K

Hematite 6x10-6 4-8 25 0.1 0 and 10-3.5 N

(200

Hematite 1.90x10-7 6, 7 and 

8.5 

0.2 ~0.004 GW 10-3.5 R

Hematite 

≈10-6 8.5 and 8.7 50 GW 10-3.5 T
17 

Table 2.3 (continued). Studies of Np(V) Sorption to Pure Mineral Phases

Sorbent 

(Chemical Formula) 

Np(V) 

(237, 239Np) 

(mol L-1) 

pH range Solid 

Concentration

(g L-1) 

I 

(mol L-1)

PCO2

(Atm) 

Hornblende 

(Representative: 

Ca2(Mg,Fe, Al)5 

(Al,Si)8O22(OH)2) 

<10-7 2.5, 8.5 

and 10.4 

20 GW 10-3.5 To

Hydrargilite 

(α-Al(OH)3) 

≈10-13 4-9 19 0.1 and 

0.01 

0 and10-4 M 

HCO3

-

De

Hydrargilite 

≈10-13 4-9 6 0.1 0 and 10-4 M 

HCO3

-

De

Illite 

(Generalized: 

(K,H)Al2(Si,Al)4

O10 (OH)2•xH2O) 

8.84x10-6 – 

9.54x10--6

Atm 

PCO2= 

8.0-8.5 

N2 = 9.5 

250 AGWsvaried 

total 

dissolved 

solids 

10-3.5 and N2

(low O2 < 

0.5 ppm) 

Tic

Illite 1x10-8- 1x10-4 6 ~0.1 0.01 10-3.5 NaIllite <10-7 2.5, 8.5 

and 10.4 

20 GW 10-3.5 To

Kaolinite 

(Al2Si2O5(OH)4) 

2.0x10-11 5 and 8 7-12 0.01-

AGW 

10-3.5 B

Al

Kaolinite 9.37x10-14- 

9.48x10-14

not given 0.61 ~ 0 and 

0.05 

10-3.5 

Ke

M

Kaolinite 8.84x10-6 – 

9.54x10--6

Atm 

PCO2= 

6.0-6.5 

N2 = 9.5 

250 AGWsvaried 

total 

dissolved 

solids 

10-3.5 and N2

(low O2 < 

0.5 ppm) 

Tic
18 

Table 2.3 (continued). Studies of Np(V) Sorption to Pure Mineral Phases

Sorbent 

(Chemical Formula) 

Np(V) 

(237, 239Np) 

(mol L-1) 

pH range Solid 

Concentration

(g L-1) 

I 

(mol L-1)

PCO2

(Atm) 

R

Kaolinite 7.0x10-6 6-11 5.0 0.1 0 N

Lepidocrocite 

(γ-FeOOH) 

4x10-7- 

6x10-6

4-11 1 0.1 <10-3.5 

sealed vials 

Nak

Sako

Mackinawite 

(FeS) 

2.7x10-2 – 

2.74x10-3

7-8 8.8 no added 

I 

0 M

Mackinawite 650-6500 

mg L-1

buffer was 

initially 

pH 4.5 

0.17 MBq g-1

– 333 MBq g-1

no added 

I 

0 Li

Magnetite 

(α-Fe3O4) 

2.0x10-11 5-10 7.3 0.01-

AGW 

10-3.5 Beal

Magnetitenatural and 

synthetic 

4x10-7- 

6x10-6

4-11 1 0.1 <10-3.5 

sealed vials 

Nak

Sako

Magnetite 4x10-7- 

6x10-6

4-11 1 0.1 10-3.5 Fu

Magnetitenatural and 

synthetic 

≈10-12 3-9 20 0.1 10-3.5 Toch

Magnetite 6x10-6 4-8 25 0.1 0 and 10-3.5 Na

(200

Manganese 

dioxide

9.42x10-14-

9.52x10-14

not given 0.028 ~ 0 and 

0.05 

10-3.5 

Ken

Mo

Manganite 

(MnOOH) 

1x10-6, 1x10-5

and 1x10-4

4-11 2.5 0.1 10-3.5 W

Microcline 

(Formula) 

<10-7 1.9, 8.4 

and 10.3 

20 GW 10-3.5 Tors
19 

Table 2.3 (continued). Studies of Np(V) Sorption to Pure Mineral Phases

Sorbent 

(Chemical Formula) 

Np(V) 

(237, 239Np) 

(mol L-1) 

pH range Solid 

Concentration

(g L-1) 

I 

(mol L-1) 

PCO2

(Atm) 

Montmorillonite 

(Generilized: 

(Na,Ca)(Al, Mg)6 

(Si4O10)3(OH)6•

nH2O)) 

2.0x10-11 5 and 8 7-12 0.01-AGW 10-3.5 

A

Montmorillonite 9.31x10-14-

9.61x10-14

not given 

0.18 

~ 0 and 

0.05 

10-3.5 K

M

Montmorillonite 1x10-6 2-10 4.0 0.1 0 BMontmorillonite 8.79x10-7 2-10 3.97 0.1 0-10-3.5 TMontmorillonite 1x10-8- 

1x10-4

6 1 0.01 10-3.5 N

Ta

Montmorillonite 1.90x10-7 6, 7 and 

8.5 

0.2 ~0.004 GW 10-3.5 R

Montmorillonite 5.0x10-5 2-11 4 0.1 and 

~0.004 GW 

0 and 10-3.5 Sh

Montmorillonite ≈10-6 8.5 and 8.7 50 GW 10-3.5 T

Muscovite 

(KAl2(AlSi3O10) 

(F,OH)2) 

8.84x10-6 – 

9.54x10--6

Atm 

PCO2= 

8.0-8.5 

N2 = 9.5 

250 AGW 

solutionsvaried total 

dissolved 

solids 

10-3.5 and 

N2 (low O2

< 0.5 ppm)

Tic

Pyrite (FeS2) 

2.0x10-11 3-9 7.8 0.01-AGW 10-3.5 

A
20 

Table 2.3 (continued). Studies of Np(V) Sorption to Pure Mineral Phases

Sorbent 

(Chemical Formula) 

Np(V) 

(237, 239Np) 

(mol L-1) 

pH range Solid 

Concentration

(g L-1) 

I 

(mol L-1)

PCO2

(Atm) 

R

Quartz 

(SiO2) 

2.0x10-11 3-10 7.6 0.01-

AGW 

10-3.5 Beal

Quartz ≈10-6 8.5 and 8.9 50 GW 10-3.5 TQuartz 8.84x10-6 – 

9.54x10--6

Atm 

PCO2= 

7.0-7.5 

N2 = 9.5 

250 AGWsvaried 

total 

dissolved 

solids 

10-3.5 and 

N2 (low O2

< 0.5 ppm) 

Tic

Quartz 1.0x10-7- 

1.0x10-6

5-11 40 and 80 0.1 <10-3.5 

sealed vials 

and 10-3.5

Be

Quartz 1.2x10-7 4-12 30 0.1 0 Ko

Serpentinite 2.0x10-11 5 and 9 7-12 0.01-

AGW 

10-3.5 Beal

Silica colloids 1.90x10-7 6, 7 and 

8.5 

0.2 ~0.004 

GW 

10-3.5 Ru

Silcate gel 

(Ca2+ or Mg2+) 

7.2x10-14 and 

2.8 x10-4

7 not given 

~ 4 

not given

~ 0.25 

10-3.5 Pa

Sodium-Smectite 

(Generilized: 

(Ca, Na, H) 

(Al, Mg, Fe, Zn)2 

(Si, Al)4O10 

(OH)2•xH2O)) 

6x10-7 2-8 1 0.01 10-3.5 K

(199
21

occurring and synthetic aluminum oxides under 

conditions similar to those used to study Np(V) 

sorption to iron oxides. Similar to Np(V) 

sorption to iron oxides, sorption to aluminum 

oxides followed the order: gibbsite (α-Al(OH)3) 

> diaspore (α-AlOOH) > corundum (α-Al2O3), 

with the highest Np(V) sorption occuring on the 

least crystalline aluminium oxides having the 

highest surface area. 

Del Nero et al. (2004) conducted Np(V) sorption 

experiments using poorly ordered allophane-like 

(Al-rich) and hisingerite-like (Fe-rich) silicates. 

This study found that increasing the 

concentration of aluminol sites had little impact 

on Np(V) sorption, suggesting that the 

predominant surface sites were the weakly 

acidic silanol groups. Np(V) sorption to Fe-rich 

silicates was substantially higher than sorption 

to Al-rich silicates but the wide Fe/Si molar ratio 

had an unexpectedly low impact on Np(V) 

sorption. One explanation given was that the 

high solubility of the Fe silicates led to the 

release of silicate anions, which in turn adsorbed 

to the ferrinol surface sites or formed silica gel 

coatings. 

Few spectroscopic studies have been conducted 

to elucidate the nature of Np(V) surface 

complexation (Table 2.4). Of the X-ray 

absorption spectroscopy (XAS) studies 

conducted, the majority have focused on Np(V) 

surface reactions with goethite and hematite 

likely due to the high affinity of Np(V) for iron 

oxides compared to other minerals (Combes et 

al., 1992; Sherman et al., 2000; Arai et al., 

2007). The Combes et al. (1992) study of Np(V) 

sorption to goethite gave evidence that Np(V) 

formed inner- sphere mononuclear surface 

complexes. Arai et al. (2007) showed that 

EXAFS results suggest bis-carbonato innersphere and tris-carbonato outer-sphere ternary 

neptunyl surface species co-exist at the 

hematite-water interface at pH 7-8.8. 

2.1.3 Neptunium(V) Sorption to 

Complex Geomedia 

Most of the research on Np(V) sorption to soils 

and sediments has been conducted with the 

intent of determining Kd values for input into 

transport models relevant to underground 

repositories. Kd values (e.g. ml g-1) are defined 

as the ratio of the Np sorbed per mass of solid to 

the concentration of Np remaining in solution, 

where small Kd values are indicative of weak 

sorption. The USEPA (1999) and the USNRC 

(1981) offer thorough reviews of early Np(V) 

sorption studies. The USEPA report contains 

look-up tables for minimum and maximum Kd

values for many radionuclides. However, lookup tables were not derived for Np(V) due to the 

lack of sufficient Np(V) sorption studies on 

complex geomedia. However, the database used 

by the USNRC for previous performance 

assessments (PA) has a Kd value of 10 ml g-1 for 

Np(V) sorption to clay, granite, basalt and tuff 

(McKinley and Scholtis, 1993). Table 2.5 

contains a summary of the available studies for 

Np(V) sorption to various sediments and soil 

types. Many of these studies focus on Np(V) 

sorption under fixed geochemical conditions 

(e.g. pH, PCO2, total Np and solid 

concentration). 

The Eckhardt (2000) review of research 

conducted by Los Alamos National Laboratory 

illustrated the variability in Np(V) sorption to 

different Yucca Mountain zeolitic tuff samples. 

Variations in Np(V) sorption was primarily 

attributed to variable concentrations of clay and 

iron oxide surface sites in the different tuff 

samples. However, experimental Np(V) 

sorption data was fairly well simulated when 

only clinoptilolite surface sites, a primary 

component of the tuffs, were considered, 

evidence for the surface site dependence of 

Np(V) sorption. 

The Mincher et al. (2003) study of Np sorption 

to soil from the Snake River Plain in Idaho 

showed that sorption was well described using a 

Freundlich isotherm, suggesting the possible 

presence of multiple site types. The soil had an 

estimated distribution by weight of the following 

minerals: 50-75% quartz, 10-25% plagioclase 

and K-feldspar, 2-5% olivine and pyroxene, 10-

20% clay as illite-smectite and kaolinite, and 2-

5% calcite. The authors hypothesized that 

sorption was initially controlled by 

complexation with clay surface sites rather than 

iron oxides and ultimately controlled by 
22

Table 2.4 Np(V) Sorption Spectroscopic Studies 

Sorbent Significant Findings / Comments Reference 

Goethite EXAFS results show that Np(V) sorbs as a 

mononuclear species, likely neptunyl, but 

carbonato or hydrolysis complexes may be present; 

sorbs as an inner sphere complex 

Combes et 

al. (1992) 

Goethite Preliminary XAS observations show that Np(V) is 

reduced to Np(IV) at low pH under inert 

atmosphere 

Sherman et 

al. (2000) 

Mackinawite EXAFS and XANES results show that interaction 

with the sulfide surface reduces Np(V) to Np(IV) 

with Np(IV) coordinating directly to sulfide surface 

atoms 

Moyes et al. 

(2002) 

Mackinawite EXAFS and XANES results show that direct 

covalent interaction of Np(V) with the sulfide 

surface and reduction to Np(IV) ; coordination at 

the surface is the same over 1 log unit of Np(V) 

concentration change 

Livens et 

al. (2004) 

Hausmanite 

and 

Manganite 

XANES results show that solution Np(V) is 

reduced to Np(IV) by interaction with manganite 

but not hausmanite. Introduction into the X-ray 

beam caused Np reduction in the presence of 

hausmanite. EXAFS results show that Np 

precipitates at both mineral surfaces. 

Wilk et al. 

(2005) 

Hematite EXAFS results show that bis-carbonato innersphere and tris-carbonato outer-sphere ternary 

surface species co-exist at the hematite-water 

interface at pH 7.0-8.8 

Arai et al. 

(2007) 

formation of Np oxide phases. The Kd value of 

49 ± 12 ml g-1 in artificial groundwater was 

consistent with the value obtained by Grossman 

et al. (2001) for sorption to basalt interbeded 

sediments. Grossman et al. showed that 

Freundlich isotherms fit the experimental data 

for Np(V) sorption better than Langmiur 

isotherms. Of interest, Mincher et al. (2003) 

used a very short contact period of 3 hours while 

the Grossman et al. study was conducted over 56 

days. Grossman et al. showed that sorption 

proceeded quickly initially, followed by a slow 

increase in sorption over the entire study period. 

2.1.4 Influence of Humic Acids on 

Neptunium(V) Sorption 

Humic acids (HA) can reduce, increase or have 

no effect on metal and radionuclide sorption to 

mineral surfaces depending on the nature of the 

system. For example, the Niitsu et al. (1997) 

study of Np(V) sorption to kaolinite showed a 

noticeable increase in Np(V) sorption below pH 

8 in the presence of HA and a marked decrease 

in Np(V) sorption from pH 8-11 compared to the 

system without HA. The increase in sorption 

was associated with a concomitant increase in 

HA sorption. Increased Np(V) affinity for the 

kaolinite was likely a result of the formation of 

ternary Np(V) complexes and the increasingly 

negative surface charge due to HA sorption. At 

pH values greater than 8, the desorption of HA 

and subsequent formation of a Np-HA aqueous 

complex was considered the cause for the 

reduction in Np(V) sorption. Righetto et al. 

(1991) observed a similar trend for Np(V) 

sorption to γ-alumina and amorphous silica. 

Righetto and co-workers also showed
23 

Table 2.5. Studies of Np(V) Sorption to Complex Geomedia (GW = groundwater, AGW = artificial 

ASW = artificial groundwater, DW = deionized water, all collected at atmospheric PCO

Sorbent Kd

(mL g-1) 

Solution Phase pH Refer

Soils : 

3.90 0.015 M NaNO33.51 0.030 M NaNO33.28 0.3 M NaNO33.28 0.75 M NaNO33.19 3.00 M NaNO32.37 0.002 M Ca(NO3)20.93 0.02 M Ca(NO3)20.78 0.05 M Ca(NO3)20.62 0.10 M Ca(NO3)2

Sandy Washington 

soil (WS) 

0.36 0.20 M Ca(NO3)2

7.4 and 4.1 Routson et al. 

0.66 0.015 M NaNO30.57 0.030 M NaNO30.51 0.3 M NaNO30.45 0.75 M NaNO30.43 3.00 M NaNO30.25 0.02 M Ca(NO3)2

South Carolina soil 

(SCS) 

0.16 0.20 M Ca(NO3)2

7.8 and 6.2 Routson et al. 

Silt loam soil-clay 

fraction 

320 5 mM Ca(NO3)2 6.5 Dahlman et 

Sharpsburg silt clay 35 5.83 

loam 95 

Deionized water 

6.85 

Nishita et 

Malbis sandy loam 3 4.08 

18 

Deionized water 

5.57 

Nishita et 
24 

Table 2.5 (continued). Studies of Np(V) Sorption to Complex Geomedia 

Sorbent Kd

(mL g-1) 

Solution Phase pH Refer

Lyman sandy loam 32 Deionized water 4.42 Nishita et 

Holtsville silty clay 41 7.29 

117 

Deionized water 

8.28 

Nishita et 

Aiken loam 26 5.56 

108 

Deionized water

6.57 

Nishita et 

Yolo silt loam 52 6.13 

81 

Deionized water 

6.83 

Nishita et 

Egbert muck 786 6.24 

929 

Deionized water

7.25 

Nishita et 

180 8.3 (low carb) 126 8.3 (med carb) 

68 8.3 (high carb) 

Oxidized clayey 

sand 

90 

GW 

9.0 

Bidoglio e

58.5 ± 1.3 (7 days) 

68.1 ± 4.7 (14 days)

80.8 ± 6.3 (28 days) 

INEEL sedimentary 

interbed soil, 

representative 

sample: 

7DS01701 94.9 ± 2.9 (56 days) 

AGW ~8 Grossman 

195 ± 6.3 (7 days) 

227 ± 13 (14 days) 257 ± 16 (28 days) 

INEEL sedimentary 

interbed soil, 

representative 

sample: 

L1S-lNEEL-10 286 ± 24 (56 days) 

AGW ~8 Grossman 

Idaho plains soil 49 ± 12 AGW 7.55 

(quartz/feldspar/ 

clays/Fe and Al 

oxides/ calcite) 

63 ± 12 Deionized water Not given 

Mincher e
25 

Table 2.5 (continued). Studies of Np(V) Sorption to Complex Geomedia 

Sorbent Kd

(mL g-1) 

Solution Phase pH Refer

Calcareous soil: 

Untreated soil 80-760 7.7 

Soil with CaCO3

removed 

25-50 7.8 

Soil with organic 

matter removed 

16-200 7.8 

Soil with CaCO3 and 

organic matter 

removed 

27-61 

0.01 CaCl2

7.8 

Weijuan & 

Sediments: 

Hanford: Touchet 2.17 (7 days) 

bed sand 3.62 (77 days) 

Hanford : Silty loam 2.67 (7 days) 

13.5 (77 days) 

Hanford : Very 14.2 (7 days) 

coarse sand 19.9 (77 days) 

GW 8.46 Kaplan a

(1995) and K

(19

Hanford: Trench 8 13.5 ± 3 (5 days) 

Sediment (TBS-1) 29.1 ± 3.6 (44 days) 

GW 8.3 Serne et 

Saturated aquifer 

sediment- Forty Mile 

Wash, Nevada 

10-35 GW or AGW ~ 7.0-9.0 Bertetti et 

Aquifer sediments from Gorleben, Germany, different depths: 

S1 1.9 ± 0.5 (aerobic) 6.0 

21 ± 6 (anerobic) 6.0 

S2 1.4 ± 0.4 (aerobic) 7.1 

1290 ± 330 (anerobic)

GW from same 

depth as sediment 

6.8 

Leiser & M

(19
26 

Table 2.5 (continued). Studies of Np(V) Sorption to Complex Geomedia 

Sorbent Kd

(mL g-1) 

Solution Phase pH Refe

Aquifer sediments from Gorleben, Germany, different depths: 

S3 2.6 ± 0.6 (aerobic) 7.9 

168 ± 42 (anerobic) 7.3 

S4 2.7 ± 0.6 (aerobic) 7.0 

1060 ± 260 (anerobic) 7.3 

S5 3.7 ± 0.8 (aerobic) 7.0 

1600 ± 500 (anerobic)

GW from same depth 

as sediment 

6.9 

Leiser & 

(1

Marine sediments: 

Clay-rich & organicrich marine 

sediments 

Kinetic data only Deionized water and 

ASW in equilbrium 

with calcite 

7.8-8.2 Keeney-K

Mors

Silt and clay 

fractions of marine 

particulate material 

See sorption data in 

article 

Deionized water & 0.7 

M NaCl with or without 

2 mM HCO3-, 0.42 M 

NaCl & Ca2+ or Mg2+

6.8-11 McCubbin 

(1

Silt and clay 

fractions of marine 

particulate material 

See sorption data in 

article 

Partial seawater 8.0 McCubbin 

(1

Rock samples: 

10 1.9 60 8.5 

Stripa granite 

70-120 

GW 

10.1 

Torsten

(1

10 2.2 

60-180 8.8 

Finnsjön granite 

110-210 

GW 

10.1 

Torsten

(1
27 

Table 2.5 (continued). Studies of Np(V) Sorption to Complex Geomedia 

Sorbent Kd

(mL g-1) 

Solution Phase pH Refe

Westerly granite 10 2.0 

70-90 

GW 

7.5 

Torsten

(1

3000 (1.1x10-6 m s-1 Granite ) 

900 (3.2x10-6 m s-1) 

GW 8.9 Kum

Vanderg

Yucca Mountain 0-2.4 (batch) 

Tuff 0.01-2.8 (column) 

GW and AGW 8.3-9.5 Triay et 

19

Yucca Mountain Tuffs: 

Zeolitic 0.3 7.0 

1.5 8.5 

Devitrified 0.007 7.0 

~0.04 8.5 

Vitric 0.2 7.0 

0.3 8.5 

Vitric with 10% clay ~8 

GW 

7.0 

Eckhar

(summa

Alamo

Mountai
28

that increasing the HA concentration (1 to 50

mg L-1) accentuated the increase in Np(V) 

sorption to both minerals. Furthermore, the 

decrease in sorption at pH values >8 was greater 

at higher HA concentrations presumably due to 

an increase in the aqueous Np(V)-HA 

complexation. Table 2.6 summarizes the Np(V) 

sorption studies conducted with HA present. 

2.1.5 Surface Complexation Modeling of 

Natural Systems 

The primary geochemical mechanism that will 

limit the migration of 237Np away from an 

underground repository is the extent to which 

Np sorbs to solid surfaces (Eckhardt, 2000). 

Traditionally, quantitative descriptions of 

adsorption are accomplished using empirical 

adsorption models as suggested by the literature 

pertaining to Np(V) sorption to complex 

geomedia (see Table 2.5). Distribution 

coefficients, Kd, have often been used to model 

sorption due to the experimental simplicity 

(batch and column studies) and the ease of 

incorporation into transport models. However, 

factors such as solute composition, pH, PCO2

and solid characteristics may significantly 

impact Kd values (USEPA, 1999). Langmuir 

and Freundlich isotherm constants will generally 

describe sorption over a wider concentration 

range than do single Kd values, but the constants 

are also system dependent (Koretsky, 2000). 

Therefore, Kd values and isotherms derived from 

a specific set of conditions may not be 

applicable to natural systems with geochemical 

conditions that vary both spatially and 

temporally. 

Generally, the sorption behavior of Np has been 

incorporated into transport models using a 

constant distribution coefficient (Kd) or a range 

of Kd values (USEPA, 1999). However, Kd

values can vary due to temporal and spatial 

variations in chemical conditions resulting in a 

high degree of error being incorporated into 

model calculations (Bethke and Brady, 2000). 

To address this inadequacy, surface 

complexation models (SCMs), which describe 

metal and radionuclide sorption over a range of 

geochemical conditions, have been incorporated 

into transport model simulations (Kent et al., 

2000; Curtis et al., 2006). The component 

additivity (CA) and generalized composite (GC) 

approaches are two types of SCMs capable of 

describing metal and radionuclide sorption to 

heterogeneous sediments and soils (Kent et al., 

2000; Waite et al., 2000; Sanpawanitchakit, 

2002; Davis et al., 2004). 

The primary objective of the research described 

in this section was to test the appropriateness of 

using SCMs to describe Np(V) sorption to 

heterogeneous geomedia under field relevent 

conditions. To date, few studies have focused 

on Np(V) sorption to sediments and soils over a 

range of geochemical conditions, limiting the 

available experimental data (e.g. Mincher et al., 

2003; Sakamoto et al., 2000; Weijuan & Zuyi, 

2003). As a result, the use of SCMs to describe 

Np(V) sorption has been restricted to pure 

mineral phases (Del Nero et al., 1997; Turner et 

al., 1998; Kohler et al., 1999; Del Nero et al., 

2004). This study expands the available 

experimental data for Np(V) sorption to well 

characterized sediments under environmentally 

relevant conditions. Experimental variables 

include: Np(V) concentration (5x10-8 – 1x10-5

M), solid concentration (50, 125 and 500 g L-1) 

partial pressure of CO2 (PCO2 = 0, 10-3.5 and 10-

1.1 atmospheres (atm)), competing inorganic 

groundwater solutes (e.g. Ca2+, Mg2+, CO3

2-) and 

HA (10-200 mg L-1). The effect of carbonate 

minerals was elucidated by conducting 

experiments with unmodified sediment and 

sediment treated to remove the carbonate 

minerals. The effect of variable pH (3-10) was 

studied using sediment treated to remove 

carbonate minerals. A non-electrostatic GCSCM was assessed to determine its ability to 

simulate and predict the Np(V) sorption on an 

alluvial aquifer sediment. 

2.2 Effect of Solution Composition 

on Neptunium(V) Sorption to an 

Alluvial Aquifer Sediment 

2.2.1 Aquifer Sediment Sample 

This section contains experimental results for 

Np(V) sorption to the well characterized 

Naturita aquifer background sediment (NABS) 
29 

Table 2.6 Studies of Np(V) Sorption in the Presence of Humic Substances 

Sorbent Organic Material 

(Origin and mg L-1) 

Ionic Strength

(mol L-1) 

pH Reference

γ-alumina and 

amorphous silica 

Boom clay extracted HA0 to 50 mg L-1

0.1 NaClO4 4-10 Righetto 

et al. 

(1991) 

Yucca Mountain 

tuff, Al oxide 

and Fe oxide 

Nordic aquifer FA0.1 to 0.4 mg L-10.1 KCl 6.2 Kung and 

Triay 

(1994) 

Kaolinite Aldrich HA 0.1 NaClO4 6-11 Niitsu et 

al. (1997) 

Sandy sediment Gorleben groundwater 

DOC = 29.9 mg L-1

UnspecifiedGorleben 

groundwater 

7.6 Artinger 

et al. 

(2000) 

Sandy soil and 

granite media 

Aldrich HA, HS from the 

Chalk River Laboratories 

(CRL-Fulvics) and from 

the Underground 

Research Laboratory 

(URL-Fulvics) of Atomic 

Energy of Canada 

Limited- 20 mg L-1

0.01 M 

NaClO4

5.5 

and 

9.5 

Sakamoto 

et al. 

(2000) 

Weak loess 

aquifer medium 

Unnamed HS10% (w/w) Unspecified 

groundwater 

Not 

given 

Chunli et 

al. (2001) 

Calcareous soiltreated to 

remove organic 

matter and/or 

CaCO3

Field site at the China 

Institute for Radiation 

Protection (CIRP) FA- 20 

mg L-1

0.01 CaCl2 7.8 Weijuan 

and Zuyi 

(2003) 

under variable geochemical conditions. 

Uncontaminated sediment was obtained from the 

Uranium Mill Tailings Remedial Action 

(UMTRA) site situated along the San Miguel 

River about 3 km downstream from the town of 

Naturita in Montrose County, Colorado. 

Sediment from the saturated zone was collected 

on July 16-17, 1998 from a gravel pit located up 

gradient of the uranium (U) contamination 

(USNRC, 2003). The sediment size fraction that 

passed through both a 64 mm and a 3 mm sieve 

was used for all experiments. This fraction 

made up roughly 15% of the total sediment 

collected from the field and approximately 30% 

of the < 64 mm fraction (Davis et al., 2004). The 

alluvial aquifer sediment (NABS) was utilized in 

its unmodified and carbonate-mineral removed 

forms. The unmodified sediment was largely 

composed of quartz, feldspars and calcite with 

lesser amounts of magnetite and clay minerals 

(Davis et al., 2004). Treated sediment 

underwent carbonate destruction using 1M 

sodium acetate solution, adjusted to pH 5 with 

acetic acid. Both the unmodified and treated 

sediment grains exhibited illite/smectite clay 

coatings with occluded ferrihydrite and goethite 

(Davis et al., 2004). Both sediment types were 

previously utilized to study U(VI) sorption and 

have been extensively characterized 

(Sanpawanitchakit, 2002; USNRC, 2003; Davis 

et al., 2004; Kohler et al., 2004). 

2.2.2 Materials and Methods 

The neptunium-237 (237Np) stock solution, in 

secular equilibrium with its daughter 
30 

protactinium-233 (233Pa), was prepared in 4 M 

nitric acid by Isotope Products Laboratories. 

With the exception of 233Pa, the 237Np solution 

was free of radioimpurities. Uranium(VI) 

samples were prepared from 238U as uranyl 

nitrate, UO2(NO3)2, spiked with 233U (t1⁄2 = 

1.59x105

 years), both purchased from Anderson 

Laboratories. All reagents were analytical grade 

or better unless noted. Standard reference grade 

Suwannee River humic acid (HA) was 

purchased from the International Humic 

Substances Society (IHSS) and used without 

further purification. Water was obtained from a 

Barnstead ultra-low carbon water system 

(EASYPure® UV water). Labware was soaked 

for approximately 24 hours in 1% w/v sodium 

hydroxide (NaOH), rinsed at least 3 times with 

UV-treated water and soaked for another 24 

hours in 10% v/v hydrochloric acid (HCl) prior 

to a final thorough rinse with UV-treated water. 

Labware that contacted organic matter was 

scrubbed in a dilute solution of Alconox LiquiNox® detergent and rinsed with water prior to 

the NaOH/HCl cleaning procedure. 

The stock 237Np (6 μCi mL-1) and 233U (4 μCi 

mL-1) solutions were stored in lead shielded 

boxes. Samples containing 237Np and 233U were 

handled in a fume hood under negative pressure 

or in an inert atmosphere glovebox except 

during sample analysis. 

NABS was treated to remove carbonate 

minerals, primarily present as calcite, following 

procedures similar to Sanpawanitchakit (2002). 

Briefly, 500 g of sediment was added to 5 L of 1 

M sodium acetate (CH3COONa) adjusted to pH 

5 with acetic acid (CH3COOH). The slurry was 

placed on a shaker table for 24 hours in a large 

HPDE container that was left open to the 

atmosphere to allow CO2 transfer. Suspended 

particles were allowed to settle before the 

supernatant was decanted and the procedure was 

repeated. The wet sediment was rinsed 

numerous times with UV-treated water 

following the settling/decanting procedure 

outlined above until conductivity measurements 

were constant. The treated sediment was dried 

at 60oC for approximately 48 hours. 

The specific surface area (Asp) for treated NABS 

was 4.47 m2

 g-1 as determined by BET 

(Brunauer, Emmett, and Teller) analysis while 

Asp was 5.15 m2

 g-1 for unmodified NABS 

(Davis et al., 2004). Surface area measurements 

were also conducted for various grain size 

fractions of treated and unmodified NABS 

(Table 2.7). For all fractions, treated sediment 

had Asp values lower than unmodified NABS 

suggesting that dissolution of carbonate minerals 

broke up the cemented fragments. An overview 

of NABS characterization is found in 

Sanpawanitchakit (2002), USNRC (2003), and 

Davis et al., (2004). 

Artificial groundwater (AGW) solutions with 

major cation concentrations similar to 

concentrations encountered in Naturita 

groundwater were used for all sorption 

experiments with the exception of experiments 

conducted using sodium perchlorate (NaClO4) 

solutions. AGW solution compositions varied 

slightly depending on the experimental PCO2. 

The composition, ionic strength and pH of each 

AGW are given in Table 2.8. 

A Packard 2500TR liquid scintillation counting 

(LSC) instrument was used to analyze all 237Np 

and 233U containing samples. The LSC data 

analysis is discussed in detail in Section 2.2.3.2, 

Liquid Scintillation Analysis. A Thermo Orion 

RossTM semi-micro combination electrode 

calibrated with NIST certified pH buffers 

(4.000, 7.000 and 10.000) was used for all pH 

measurements. Total organic carbon (TOC) was 

analyzed using a Shimadzu 5000A TOC 

Analyzer. To confirm PCO2 values, alkalinity 

titrations were conducted following Gran 

function plot methodology using a HACH 

digital titrator with 1.600 N or 0.1600 N sulfuric 

acid cartridges. All experiments were conducted 

at room temperature. 

Batch sorption samples were centrifuged at 5000 

g for 60 minutes and a portion of the solution 

phase of each sample was removed and analyzed 

by LSC. Gelman Acrodisc® syringe filters (0.45 

μm) were used to ensure that separation by 

centrifugation was acceptable. Samples that 

were filtered gave consistent results with 

centrifuged samples. 
31

Table 2.7 NABS Surface Area Distributions and Weight Percent 

 for Various Grain Size Fractions* 

Unmodified Treated 

Size Fractions Surface Area Weight Surface Area Weight 

 (μm) (m2

/g) (%) (m2/g) (%) 

< 63 13.1 3.7 11.01 4.7 

63-125 7.19 6.4 5.96 8.3 

125-250 5.1 16.6 4.05 21.3 

250-500 3.95 23.1 3.11 25.4 

500-1000 5.7 15.9 4.75 15.2 

1000-3000 6.31 34.2 5.20 25.1 

 NABS 5.15 100.0 4.47 100.0 

*From USNRC, 2003; Kohler et al., 2004; and Sanpawanitchakit, 2002 

Table 2.8 Composition of Artificial Groundwater Solutions 

AGW-3 AGW-A AGW-6 

Initial PCO2 Atmospheric Atmospheric 8% 

pH 7.9 5.6 6.8 

Ionic Strength 0.02 0.02 0.03 

CaSO4 2.33x10-3 0 3.27x10-3

MgSO4 1.52x10-3 1.52x10-3 1.52x10-3

Na2SO4 9.38x10-4 3.27x10-3 0 

CaCl2 2.38x10-3 4.71x10-4 0 

KCl 6.40x10-5 6.40x10-5 6.40x10-5

NaHCO3 5.38x10-4 0 2.41x10-3

CaCO3 0 0 3.33x10-3

HCl 0 0 1.57x10-3 

Ionic strengths and component concentrations are in mol L-1
32 

The percent Np(V) sorbed (%NpSorbed) was 

calculated using a mass balance approach as 

follows: 

%NpSorbed =100% * 1− CPMFinal

CPMInitial

⎛

⎝ ⎜ 

⎞ 

⎠ ⎟

where CPMInitial and CPMFinal represent the 

counts per minute, for solution phase samples, 

before and after contact with NABS, 

respectively. CPM and decays per minute 

(DPM) were considered equal due to the 100% 

efficiency for LSC α particle counting 

(Horrocks, 1976). In order to confirm that 

NpSorbed determined by the above approach was 

actually sorbed to the sediment, mass balance 

experiments were occasionally conducted by 

adding 1 M HCl, removing an aliquot of the 

solution phase and adjusting to pH ~ 4 prior to 

LSC analysis. 

LSC counting times of 60 minutes gave a 

maximum 2σ error of ± 1% for the α counting 

activity. The use of a range of humic acid (HA) 

concentrations resulted in varying degrees of 

color quenching during LSC analysis, resulting 

in quantifiable shifts in the LSC α spectrum. To 

account for color quenching, the LSC CPM data 

for each HA containing sample was manually 

analyzed (see Section 2.4.2). 

2.2.2.1 Time Dependent Np(V) Sorption 

Experiments 

The length of time needed for quasi-equilibrium 

conditions to be met was studied using batch 

sorption methodology with 5.0x10-7 M Np(V) 

and 50 g L-1 unmodified NABS in AGW-3 

under atmospheric PCO2. Into each 50 ml 

Nalgene® Oak Ridge polycarbonate centrifuge 

tube, 19.5 ml ± 0.2 ml AGW-3 was added to 

1.000 g ± 0.0025 g unmodified NABS. The 

tubes were shaken in the dark while open to 

atmospheric PCO2 for a 72-hour preequilibration period. The purpose of this step 

was to allow ions dissolved from the NABS 

surface (e.g. Ca2+, Mn2+and HSiO4

-

) during 

initial contact with AGW to approach steady 

state concentrations (USNRC, 2003). A 500 μl 

aliquot of 2.0x10-5 M Np(V) was added to each 

vial, resulting in a slight dilution (< 3%) of the 

AGW-3 and giving a final Np(V) concentration 

of 5.0x10-7 M. Modification of pH, using 

unmodified NABS, was difficult under fixed 

PCO2 due to the buffering effect of the 

carbonate minerals. Therefore, the pH of each 

sample was allowed to reach its final value of 

pH 8.0 ± 0.1 without outside manipulation. 

Vials were removed in triplicate at specified 

time intervals, centrifuged, and the supernatant 

was analyzed by LSC. 

The influence of Suwannee River HA on the 

time Np(V) takes to reach steady state-like 

concentrations in the presence of NABS was 

also investigated using batch sorption 

experiments. Two batch experiments were 

conducted: 1) the first studied the influence of 

Np(V) pre-equilibration with HA prior to 

conducting sorption experiments; 2) the second 

experiment explored the effect of contact time 

on Np(V) sorption to treated NABS in the 

presence of HA. 

The stock Np(V)-HA solution, consisting of 

5.0x10-5 M Np(V) and 1000 mg L-1 HA, was 

prepared in an autoclaved 20 ml Nalgene® Oak 

Ridge polycarbonate centrifuge tube. The 

sample pH was 8.0 ±0.1 and the ionic strength 

was 0.02 M NaClO4. The stock solution was 

stored at 4oC in the dark except for brief periods 

for sample removal. To each sample vial, 1.000 

g ± 0.0025 g treated NABS was added and preequilibrated for 72 hours with 19.8 ml of 0.1 M 

NaClO4 at pH 8.0 ±0.1. Throughout the 

experiment, samples were kept on a shaker table 

in the dark at atmospheric PCO2. At set time 

intervals, 200 μL ± 2 μL of the stock preequilibrated Np(V)-HA solution was added to 

each vial in a group to be further equilibrated 

with NABS over separate time intervals. Each 

sample contained 5.0x10-7 M Np(V) and 10 mg 

L-1 HA. Individual vials were removed after 

specific time intervals, centrifuged and analyzed 

by LSC. 

2.2.2.2 Sorption Isotherm Experiments 

The effect of PCO2 and variable Np(V) 

concentration on sorption to treated and 
33

unmodified NABS was investigated using batch 

sorption isotherm experiments with a solid 

concentration of 125 and 500 g L-1 at 

atmospheric PCO2 (0.025% in Golden, CO) and 

125 g L-1 at 8% PCO2. Sample pH was allowed 

to reach equilibrium with the specific PCO2. 

The equilibrium pH at atmospheric PCO2 was 

8.0 ± 0.1 and 6.8 ± 0.1 at 8% PCO2. The 8% 

PCO2 conditions were carried out using 10% v/v 

certified primary grade CO2 gas within a soft 

sided glovebox that was effectively an open 

system maintained at ambient pressure. To each 

polycarbonate tube, 19.8 ml ± 0.2 ml of AGW-3, 

AGW-6 or 0.1 M NaClO4 was added to 2.5000 g 

± 0.0050 g or 10.000 ± 0.0050 g treated or 

unmodified NABS. Tubes were shaken in the 

dark while open to the applicable PCO2

atmosphere for the 72-hour pre-equilibration 

period. The Np(V) concentration range was 

varied between 2.0x10-7 M to 5.0x10-6 M for 

samples containing 125 g L-1 and between 

8.0x10-7 M and 1.0x10-5 M for the 500 g L-1

samples. Following addition of the acidic 

Np(V) stock solution, the pH was adjusted and 

the total volume was brought up to 20 ml ± 0.2 

ml with UV-treated water, NaOH and/or HCl. 

The samples were equilibrated on a shaker table 

in the dark for an additional 24 hours ± 1 hour 

before centrifugation or filtration followed by 

LSC analysis. Immediately following removal 

of a sample for LSC analysis, the final pH was 

measured and in some cases the total alkalinity 

was determined. Most samples were run in 

triplicate with some exceptions, including the 

entire 500 g L-1 data set which was run in 

duplicate. Table 2.9 outlines the experimental 

variables that were examined. For comparison 

purposes, a U(VI) isotherm experiment was 

conducted at atmospheric PCO2, using 50 g L-1

and U(VI) concentrations between 1.0x10-7 M 

and 1.0x10-5 M. 

2.2.2.3 Np(V) Sorption Edge Experiments 

The protocols for determining the sorption of 

Np(V) as a function of pH were similar to the 

sorption isotherm methodology described in the 

previous section (2.2.2.2). The effects of pH, 

PCO2, Np(V) concentration, solid-solution ratio, 

competing ions and HA on Np(V) sorption to 

NABS were investigated using batch sorption 

experiments. Only treated NABS was used for 

sorption edge experiments due to the buffering 

effect of the carbonate minerals in unmodified 

NABS. Solid-solution ratios were held constant 

at 50, 125 and 500 g L-1 treated NABS for each 

experimental data set. Most sorption edges were 

conducted over the pH range of 4 to 9 with some 

exceptions. Sorption edge experiments were 

conducted at Np(V) concentrations of 5.0x10-7

M and 5.0x10-6 M. For comparison purposes, 

several sorption edges were obtained using 

U(VI) = 5.0x10-7 M and solid-solution ratios of 

50 and 125 g L-1 treated NABS. Sorption edges 

with HA only (10 and 100 mg L-1) were 

conducted at atmospheric PCO2, using 500 g L-1

treated NABS in 0.1 M NaClO4. Table 2.10 

outlines the experimental variables that were 

investigated without HA. Table 2.11 

summarizes the experimental variables 

investigated with HA present. 

2.2.2.4 Surface Charge Characterization 

A salt titration technique based on the work by 

Yates and Healy (1975) was used to determine 

the approximate point of zero salt effect (pHpzse) 

and obtain a reasonable estimate of the point of 

zero charge (pHpzc) for treated NABS in 

NaClO4. The ions of the indifferent electrolyte, 

NaClO4, were assumed to sorb to the NABS 

surface non-specifically. Briefly, a series of 500 

g L-1 samples were prepared in triplicate 

following the same approach described above. 

The experiment was conducted in an inert (N2) 

atmosphere glovebox to exclude carbonate 

species. Each sample was adjusted to 

approximately pH 5-9 using dropwise addition 

of dilute HCl or NaOH solutions and preequilibrated on a shaker table in the dark for 72 

hours. The initial pH of each sample was 

recorded prior to addition of enough GFS 

BiorefinedTM NaClO4 salt to bring the NaClO4

concentration up to 0.01 and 0.1 M . The pH of 

an individual sample was recorded following the 

first salt addition (0.01 M). The pH was 

recorded for an additional 10 minutes prior to 

the second salt addition and the final pH 

measurement. The samples were run in 

duplicate. Variations in initial pH and the 

change in pH due to salt addition are presented 
34 

Table 2.9 Np(V) Sorption Isotherm Experimental Variables 

PCO2 NABS Solid Concentration Solution 

(%) Type (g L-1) Phase 

0.025 Unmodified 125 AGW-3 

0.025 Unmodified 500 AGW-3 

0.025 Treated 125 AGW-3 

0.025 Treated 500 AGW-3 

0.025 Unmodified 125 0.1 M NaClO4 

0.025 Treated 125 0.1 M NaClO4

8.0 Unmodified 125 AGW-6 

8.0 Treated 125 AGW-6 

Table 2.10 Np(V) Sorption Edge Experimental Variables 

PCO2 Total Np(V) Solid Solution 

(%) (mol L-1) (g L-1) Phase 

0.0 5.0x10-7 50 0.1 M NaClO4

0.0 5.0x10-7 125 0.1 M NaClO4

0.025 5.0x10-7 50 0.1 M NaClO4

0.025 5.0x10-7 125 0.1 M NaClO4

0.025 5.0x10-7 500 0.1 M NaClO4

0.025 5.0x10-6 500 0.1 M NaClO4

0.025 5.0x10-7 50 AGW-A 

0.025 5.0x10-7 125 AGW-A 

0.025 5.0x10-6 50 AGW-A 

0.025 5.0x10-6 125 AGW-A 

0.025 5.0x10-7 50 AGW-3 

0.025 5.0x10-7 125 AGW-3 

8.0 5.0x10-7 500 0.1 M NaClO4

Table 2.11 Humic Acid Sorption Experiment Variables* 

Humic Acid NABS Total Np(V) 

(mg L-1) Type (mol L-1) 

Isotherm Experiments 

0-200 Untreated 2.0x10-6 

0-200 Treated 2.0x10-6

Edge Experiments

10 Treated No Np(V) 

10 Treated 5.0x10-7 

10 Treated 5.0x10-6 

100 Treated No Np(V) 

100 Treated 5.0x10-7 

100 Treated 5.0x10-6

*All humic acid experiments were conducted using 500 g L-1 in 0.1 M NaClO4 at atmospheric 

PCO2. 
35 

as average values with the data range displayed 

as error bars. 

2.2.2.5 Near Infrared Spectroscopy 

Samples were analyzed using a Beckman 

Coulter DU® 800 UV/Vis spectrophotometer 

with a wavelength interval of 0.1 nm and a 

scanning speed of 120 nm min-1. The scanning 

region was 600 nm to 1100 nm for oxidation 

state determination and 840 nm to 1100 nm for 

evaluation of Np(V) complexation with 

Suwannee River HA. Each sample cell was 

made of optical glass with a 10 mm path length 

and a septum sealable opening. Np(V) 

concentrations were 2x10-4 M or greater unless 

otherwise noted. 

2.2.3 Results and Discussion 

2.2.3.1 Oxidation State Confirmation 

Near infrared (NIR) spectroscopic analysis 

showed that a dilute 237Np stock solution (2x10-4

M in 20 mM nitric acid) consists of the 

pentavalent oxidation state (Moran, 2007). A 

characteristic band at 980 nm and a small band 

at 617 nm confirmed the presence of the 

uncomplexed neptunyl (NpO2

+

) species. The 

solution concentration was confirmed using the 

molar extinct coefficient of 395 M-1 cm-1 and by 

LSC (Hagan and Cleveland, 1966). The 

spectrum lacked any evidence of the 

characteristic absorption bands for Np(IV) at 

723 and 960 nm (Burney and Harbour, 1974; 

Katz et al., 1986). 

2.2.3.2 Liquid Scintillation Analysis 

LSC is commonly used to analyze α activity 

associated with 237Np containing samples 

(Turner et al., 1998; Kohler et al., 1999; Runde 

et al., 2002). It is important to distinguish the α

particle counts associated with 237Np from any β

radiation activity. The Packard 2500TR LSC 

instrument used to analyze 237Np lacks the α/β

discriminator that is available on some LSC 

instruments. Under the conditions investigated, 

the presence of varying amounts of the β particle 

emitting daughter product, 233Pa, must be taken 

into account. To address this requirement the 

steps outlined below were followed. 

In the case of the Np(V)-NABS system, where 

HA is not present, counts associated with 

background radiation and residual 233Pa are low. 

Figure 2.7 is a representative spectrum of 

2.0x10-6 M Np(V) in a 0.1 M NaClO4 solution 

that had 233Pa removed by passing the solution 

through a cation exchange column. The activity 

contribution of 233Pa in the region of the 

spectrum less than 75 kilo electron volts (KeV) 

is present as a result of its in-growth (t1/2 = 23 

days) and due to a small quantity of 233Pa 

remaining in the stock solutions. In the binary 

system, the activity contribution of 233Pa and 

background radiation is easily removed by 

subtracting the total counts associated with the 

“β region” of the spectra from the total α activity 

counts associated with 237Np in the 75 to 300 

KeV region. For all experiments without HA 

present the distinction between the two regions 

is set at 75 KeV. As shown in Figure 2.7, this 

demarcation is located in the “trough” in the 

spectrum where minimal counts are detected. 

The error associated with the LSC analysis 

remains constant from sample to sample as long 

as the number of counts that fall within the 

“trough” of each spectrum remain constant. 

Error up to 2% beyond the LSC error (2σ =1%) 

was associated with the manual α/β

discrimination. This approach was applied to a 

binary Np(V)-hematite system (fixed Np(V) 

concentration, fixed hematite concentration and 

variable pH), which produced sorption edge data 

consistent with Kohler et al. (1999) (Data not 

shown). 

The data manipulation becomes slightly more 

complex when analyzing samples that contain 

HA due to color quenching in LSC samples. A 

quantifiable shift in the 237Np region of the 

spectra toward lower KeV results as the HA 

concentration increases. This phenomena is 

illustrated in Figure 2.8, which shows a series of 

Np(V)-HA samples (10-200 mg L-1 HA). To 

locate the region in the spectra where minimum 

counts are observed, the LSC raw data (total 

counts at every 0.5 KeV from 0-300 KeV) is 

plotted as total counts as a function of KeV. 

Therefore, the α counts can be quantified even 
36 

0

200

400

600

800

1000

1200

1400

0 50 100 150 200 250 300

KeV

Total Activity (Counts)

[Np(V)]T = 

2.0x10-6 M

pH 8.0

0.1 M NaClO4

Aqueous Sample:

4 ml

LSC Cocktail: 

10 ml

 α Activity 

Associated 

with 237Np

(75.5-300 

KeV)

β Activity 

Associated with 233Pa and 

Miscellaneous 

Background 

Radiation

(0-75 KeV) 

Figure 2.7. LSC spectrum showing activity associated with 237Np α particle emission and 

activity associated with background radioactivity residual 233Pa in a 2.0x10-6 M Np(V) 

sample. 

0

500

1000

1500

2000

2500

3000

0 50 100 150 200 250 300

KeV

Total Activity (Counts)

[Np(V)]T = 

2.0x10-6 M

Atmospheric 

PCO2

pH 7.0

0.1 M NaClO4

HAT = 10 to 

200 mg L-1

Increasing Humic Acid Concentration

Figure 2.8. Series of LSC spectra of 2.0x10-6 M Np(V) in the presence of 10-200 mg L-1 HA 

showing varying degrees of color quenching without a reduction in counts. 

though they have shifted. Following this 

approach gives a relative standard deviation of 

2% for the sample series containing constant 

Np(V) and variable HA shown in Figure 2.8. In 

the case of Np-HA samples, the error associated 

with manual α/β discrimination and color 

quench is limited by determining CPMInitial for 

Np-HA samples without sediment present but 

with CPM and degrees of spectral shift 

equivalent to the samples containing NABS. 

This comparison is essential due to the 

additional error associated with the increasing 

number of counts within the spectral “trough” as 

HA concentration increases. For every Np(V)-

HA dataset, several samples containing Np(V) 

without HA were processed in the same manner 

to make certain the data manipulation was 

consistent between sample sets. 
37

2.2.3.3 Np(V) Time Dependent Sorption 

The time dependent sorption experiment without 

added HA shows that Np(V) sorption to 

unmodified NABS is characterized by an initial 

rapid increase in sorption followed by a gradual 

slowing in rate after approximately 24 hours 

(Figure 2.9). Allowing the samples to 

equilibrate for an additional 3 days leads to an 

increase in sorption of approximately 10%. This 

type of sorption vs. time curve is characteristic 

of metal-ion sorption to complex geomedia. For 

practical reasons, a 24 hour (± 1) contact period 

was selected for Np(V) sorption experiments 

using unmodified and treated NABS without 

added HA. The adherence to the 24 hr 

equilibration time helps to reduce the error 

associated with the slow increase in sorption 

with time. All samples were run in triplicate and 

a relative standard deviation of 6% or less was 

obtained. A preliminary time dependent 

sorption experiment using Np(V) and treated 

NABS also approached steady state-like 

conditions within 24 hours (data not shown). 

Sanpawanitchakit (2002) conducted experiments 

to determine the length of time for U(VI) to 

reach quasi-equilibrium conditions in contact 

with treated NABS. Similar to Np(V) sorption, 

U(VI) sorption to treated NABS reached steady 

state-like conditions within 24 hours via fast 

initial sorption followed by slower uptake. 

Davis et al. (2004) determined the time period 

for U(VI) sorption to unmodified NABS to 

occur in approximately 96 hr. 

2.2.3.4 Time Dependent Sorption in the 

Presence of Humic Acids 

The influence of pre-equilibrating Np(V) with 

HA prior to sorption to treated NABS was 

investigated using batch sorption experiments. 

At first glance, it appears that the addition of HA 

reduces the initial rapid increase regardless of 

how long Np(V) is in contact with HA prior to 

sorption to treated NABS (Figure 2.10). 

However, the sorption experiments with HA 

present were conducted over a much wider time 

period (24 hours to 600 hours) than the Np(V)-

only time dependent experiments. Comparing 

the time dependent sorption results shows that 

sorption with and without HA present follows 

the same trend with respect to time. This is best 

illustrated in Figure 2.11 which compares the 90 

hour Np(V)-HA pre-equilibration data from 

Figure 2.10 and the Np(V)-only data from 

Figure 2.9. As a consequence of these results, 

all Np(V)-HA stock solutions were preequilibrated for 90 hours prior to sorption 

experiments. Clearly, extending the sorption 

time leads to a relatively constant slow increase 

in sorption regardless of the presence of HA. 

For example, focusing again on the 90 hour preequilibration results, sorption increases from 

41% at 24 hours to 77% near 600 hr. This point 

is further illustrated by the >90% sorption 

observed for isotherm experiments in which 

Np(V) was equilibrated with 500 g L-1

unmodified and treated NABS in the presence of 

0-200 mg L-1 HA at pH 8.0 in 0.1 M NaClO4 for 

600 hr (results not shown). For consistency all 

sorption experiments with or without HA were 

conducted over a 24 hr (± 1) equilibration 

period. 

2.2.3.5 Np(V) Sorption Isotherm Results 

The effect of PCO2 on Np(V) sorption to treated 

and unmodified NABS was investigated using 

isotherm experiments. An average relative 

standard deviation of 6% was obtained for all 

the isotherm experimental data at a given total 

Np(V) concentration. Figure 2.12a shows the 

sorption behavior of Np(V) under atmospheric 

and 8% PCO2 using a solid-solution ratio of 125 

g L-1 unmodified NABS, while varying the 

Np(V) concentration in artificial groundwater 

(AGW-3 and AGW-6). The pH was allowed to 

reach an equilibrium value specific to the 

particular PCO2. In Figure 2.12a Np(V) sorbed 

is presented as Г (mol g-1) as a function of 

NpSoln, the concentration of Np(V) remaining in 

solution (mol L-1). Figure 2.12b presents the 

same data in the form of Kd (mL g-1) as a 

function of NpSoln in order to easily compare the 

results from this study to the majority of the 

experimental data in the literature for Np(V) 

sorption to complex geomedia. These 

experiments show that Np(V) sorption is very 

similar at atmospheric and 8% PCO2 with only a 

slight decrease in sorption at high PCO2. The 
38 

0

10

20

30

40

50

60

70

80

90

100

0 12 24 36 48 60 72 84 96 108 120

Time (Hours)

Percent Np(V) Sorbed

[Np]T = 5.0x10-7 M, pH = 8.0 ± 0.1

Atmospheric PCO2

Unmodified NABS

AGW-3 (I = 0.02 M)

50 g L-1 

Figure 2.9. Time dependent sorption of 5.0x10-7 M Np(V) to 50 g L-1 unmodified NABS in AGW-3 

at pH 8.0 and atmospheric PCO2. 

0

10

20

30

40

50

60

70

80

90

100

0 60 120 180 240 300 360 420 480 540 600

Equilibration with NABS (Hours)

Percent Np(V) Sorbed

Time in flask = 2 hrs

Time in flask = 24 hrs

Time in flask = 90 hrs

Tme in flask = 117 hrs

Time in flask = 167 hrs

Time in flask = 236 hrs

Time in flask = 766 hrs

[Np]T = 5.0x10-7 M HA = 10 mg L-1 

pH 8.0 ± 0.1 0.1 M NaClO4 

Atmospheric PCO2 50 g L-1 Treated NABS

Figure 2.10. Time dependent sorption of Np(V) to 50 g L-1 treated NABS in the presence of 10 mg 

L-1 HA (pH 8.0) at atmospheric PCO2 with a total Np(V) concentration of 5.0x10-7 M and 0.1 M 

NaClO4. Np(V) pre-equilibrated with HA for 2, 24, 90, 117, 167, 236 and 766 hours. Sorption times 

were approximately 24, 72, 120, 196, 285 and 600 hours. 

0

10

20

30

40

50

60

70

80

90

100

0 24 48 72 96 120 144 168 192

Equilibration with NABS (Hours)

Percent Np(V) Sorbed

Np(V) Only

Np(V)-HA: 90 Hour

Pre-equilibration

Np(V) Only: [Np]T = 5.0x10-7 M

AGW-3, Unmodified NABS 

Np(V)-HA: [Np]T = 5.0x10-7 M and HA = 10 mg L-1

0.1 M NaClO4, Treated NABS

50 g L-1

pH 8.0 ± 0.1 

Atmospheric PCO2

Figure 2.11. Comparison of time dependent sorption of 5.0x10-7 M Np(V) with and without 10 mg 

L-1 HA at pH 8.0 and atmospheric PCO2. The Np(V) only experiments were conducted with 50 g L1

 unmodified NABS in AGW-3. The Np(V)-HA samples shown were pre-equilibrated for 90 hours 

prior to sorption with 50 g L-1 treated NABS in 0.1 M NaClO4. 
39

-9.3

-9.1

-8.8

-8.6

-8.3

-8.1

-7.8

-7.0 -6.8 -6.5 -6.3 -6.0 -5.8 -5.5 -5.3

Log Npsoln (mol L-1)

Log Np(V) Sorbed (mol g-1)

8% PCO2

Slope = 0.81

R2

 = 0.98

Atmospheric PCO2

Slope = 0.83

R2 = 0.99

125 g L-1Unmodified 

NABS

 [Np]T = 3.0x10-7 to 

5.0x10-6 M

Atmospheric PCO2: 

AGW-3, pH 8.0 ± 0.1

8% PCO2: AGW-6, 

pH 6.8 ± 0.1 

Figure 2.12a. Np(V) sorption isotherms using a maximum range of Np(V) from 3.0x10-7 M 

to 5.0x10-6 M, 125 g L-1 unmodified NABS, in AGW-3 (pH 8) at atmospheric PCO2 or in 

AGW-6 (pH 6.8) at 8% PCO2. 

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

-7.0 -6.8 -6.5 -6.3 -6.0 -5.8 -5.5 -5.3

Log Npsoln (mol L-1)

Log Kd (mL g-1)

8% PCO2

Atmospheric PCO2

125 g L-1 Unmodified NABS

 [Np]T = 3.0x10-7 to 5.0x10-6 M

Atmospheric PCO2: AGW-3, pH 8.0 ± 0.1

8% PCO2: AGW-6, pH 6.8 ± 0.1 

Figure 2.12b. Kd values for Np(V) sorption as a function of aqueous Np(V) remaining in 

solution using a maximum range of Np(V) from 3.0x10-7 M to 5.0x10-6 M, 125 g L-1

unmodified NABS, in AGW-3 (pH 8) at atmospheric PCO2 or in AGW-6 (pH 6.8) at 8% 

PCO2.
40 

error bars, which represent the 95% confidence 

intervals cannot be used as presented to 

conclusively say that the differences in the data 

are statistically significant. Instead, trendlines 

derived from the upper and lower limits of the 

95% confidence intervals do not overlap, and the 

y-intercept values are significantly different, 

suggesting that the lower Np(V) uptake at 8% 

PCO2 is significant. The errors associated with 

linear regression analysis of all Np(V) isotherm 

experimental data are presented in Table 2.12. 

Similarities in sorption at atmospheric and 8% 

PCO2 can be explained by looking at Np(V) 

aqueous speciation at the equilibrium pH values 

for each system (pH 8.0 at atmospheric and pH 

6.8 at 8% PCO2). Keep in mind that the 

equilibrium pH values for the two systems differ 

due to the presence of carbonate minerals. The 

calculated Np(V) aqueous speciation under 

atmospheric PCO2 at pH 8.0 in AGW-3 shows 

that NpO2

+

 accounts for approximately 75% of 

the Np(V) while NpO2CO3

-

 makes up 

approximately 25% of the species distribution. 

At pH 6.8, under an 8% PCO2 atmosphere, 

aqueous speciation is indistinguishable from the 

speciation at pH 8.0 under atmospheric PCO2. 

Figures 2.13a and 2.13b show experimental data 

for Np(V) sorption to treated NABS under the 

same conditions described for Np(V) sorption to 

the unmodified NABS shown in Figures 2.12a 

and 2.12b. The systems using treated NABS 

were maintained at pH 8.0 under atmospheric 

PCO2 and pH 6.8 under 8% PCO2 for 

comparison to the systems using unmodified 

NABS. Np(V) sorption to treated NABS 

follows the same trend observed for unmodified 

NABS, showing what appears to be a minor 

reduction in sorption with an increase in PCO2. 

However, error analysis of the 95% confidence 

interval error bars shows that the data are not 

significantly different. 

All the isotherms under atmospheric and 8% 

PCO2 exhibit Freundlich-like behavior, with 

slopes less than 1, over the Np(V) concentration 

ranges studied. The trendlines exhibited a 

minimum slope of 0.81± 0.01 and a maximum 

slope of 0.91 ± 0.02 (see Table 2.12). The 

nonlinear behavior indicates that as Np(V) 

concentration increases, the sorption affinity 

decreases, suggesting multiple site types. 

Np(V) sorption is characterized by a narrow 

range of Kd values, approximately 3-10 mL g-1, 

over the large PCO2 range. Recently, Davis et 

al. (2004) showed that as PCO2 increases from 

atmospheric to 6.8%, the Kd values associated 

with U(VI) sorption to unmodified NABS cover 

a comparably large range. For example, near 

atmospheric PCO2, with approximately 1x10-6 M 

U(VI) in solution, the Kd for U(VI) sorption is 

approximately 13 mL g-1 and decreases to 1 mL 

g-1 at 6.8% PCO2. The Kd values for 5.0x10-7 M 

U(VI) sorption to 125 g L-1 treated NABS varies 

between approximately 5 and 150 mL g-1 under 

the conditions investigated in this study. The Kd

values discussed above are low compared to 

values for Np(V) and U(VI) sorption to pure 

mineral oxides. For example, Tochiyama et al. 

(1991) obtained a Kd of approximately 120 mL 

g-1 for Np(V) sorption to α-Al2O3 at pH 8 using 

20 g L-1 solid and 5.5x10-13 M Np(V) at 

atmospheric PCO2. Bertetti et al. (1996) 

obtained a Kd = 100 mL g-1 for 1x10-6 M Np(V) 

sorption to 4.0 g L-1 montmorillonite at pH 8 

without CO2 present and at atmospheric PCO2. 

The high affinity of Np(V) and U(VI) for iron 

oxides is illustrated by the large Kd values 

obtained for sorption to hematite. At pH 8 and 

atmospheric PCO2, the Kd value for 1.1x10-6 M 

Np(V) sorption to 1 g L-1 hematite is 6x103

 mL 

g-1 and U(VI) has a Kd of 2x104

 at pH 8 using 

1x10-6 M U(VI) and 0.9 g L-1 (this study; Kohler 

et al., 1999; Lenhart and Honeyman, 1999). 

Np(V) sorption to both unmodified and treated 

NABS under atmospheric PCO2 and 8% PCO2

suggests that carbonate mineral surface sites are 

only minimally important in Np(V) sorption to 

unmodified NABS. The isotherms presented in 

Figures 2.14a and 2.14b illustrate the effect of 

carbonate mineral removal at fixed PCO2. The 

comparable results for Np(V) sorption to treated 

and unmodified NABS are further illustrated in 

Figures 2.14c and 2.14d which contain the same 

experimental data presented in terms of Kd as a 

function of NpSoln. The slight decrease in the 

sorption affinity of Np(V) for treated NABS is 

most evident under the 8% PCO2 conditions. 

Under high PCO2 conditions a significant 
41 

Table 2.12 Error Calculations Using The 95% Confidence Intervals From Regression 

Analysis Of Np(V) Isotherm Experimental Data 

PCO2

NABS 

Type

SolidSolution Slope

Slope 

Error

Y-axis 

Intercept

Intercept 

Error

Atmospheric Unmodified 125 0.83 0.01 3.27 0.08

Atmospheric Unmodified 500 0.91 0.02 2.90 0.11

Atmospheric Treated 125 0.83 0.05 3.32 0.32

8% Unmodified 125 0.81 0.01 3.51 0.03

8% Treated 125 0.9 0.02 3.06 0.12

-9.3

-9.1

-8.8

-8.6

-8.3

-8.1

-7.8

-7.0 -6.8 -6.5 -6.3 -6.0 -5.8 -5.5

Log Npsoln (mol L-1)

Log Np(V) Sorbed (mol g-1)

8% PCO2

Slope= 0.90

 R2 = 0.97

Atmospheric PCO2

Slope= 0.83

R2 = 0.98

125 g L-1 Treated NABS

 [Np]T = 2.0x10-7 to 3.0x10-6 M

Atmospheric PCO2: AGW-3, 

pH 8.0 ± 0.1

8% PCO2: AGW-6, pH 6.8 ± 0.1 

Figure 2.13a. Np(V) sorption isotherms using a maximum range of Np(V) from 2.0x10-7 M 

to 3.0x10-6 M, 125 g L-1 treated NABS, in AGW-3 (pH 8.0) at atmospheric PCO2 or in 

AGW-6 (pH 6.8) at 8% PCO2. 

0.5

0.6

0.7

0.8

0.9

1.0

-7.0 -6.8 -6.5 -6.3 -6.0 -5.8 -5.5

Log NpSoln (mol L-1)

Log Kd (mL g-1)

125 g L-1 Treated NABS

 [Np]T = 2.0x10-7 to 3.0x10-6 M

Atmospheric PCO2: AGW-3, pH 8.0 ± 0.1

8% PCO2: AGW-6, pH 6.8 ± 0.1 

8% PCO2

Atmospheric PCO2

Figure 2.13b. Kd values for Np(V) sorption as a function of aqueous Np(V) remaining in 

solution using a maximum range of Np(V) from 2.0x10-7 M to 3.0x10-6 M, 125 g L-1 treated 

NABS, in AGW-3 (pH 8.0) at atmospheric PCO2 or in AGW-6 (pH 6.8) at 8% PCO2. 
42 

-9.3

-9.1

-8.8

-8.6

-8.3

-8.1

-7.8

-7.0 -6.8 -6.5 -6.3 -6.0 -5.8 -5.5 -5.3

Log Npsoln (mol L-1)

Log Np(V) Sorbed (mol g-1)

Atmospheric PCO2: 125 g L-1 

[Np]T = 3.0x10-7 to 5.0x10-6 M

AGW-3

 pH 8.0 ± 0.1

Treated NABS

Slope = 0.83

R2

 = 0.98

Unmodified NABS

Slope = 0.83

R2

 = 0.99

Figure 2.14a. Np(V) sorption isotherms 

using a maximum range of Np(V) from 

3.0x10-7 M to 5.0x10-6 M, 125 g L-1 treated 

and unmodified NABS, in AGW-3, pH 8, 

at atmospheric PCO2. 

-9.4

-9.2

-8.9

-8.7

-8.4

-7.0 -6.8 -6.5 -6.3 -6.0

Log NpSoln (mol L-1)

Log Np(V) Sorbed (mol g-1)

8% PCO2: 125 g L-1, AGW-6

[Np]T = 2.0x10-7 to 1.0x10-6 M

 pH 6.8 ± 0.1

Treated NABS

Slope = 0.90

R2

 = 0.97

Unmodified NABS

Slope = 0.81

R2

 = 0.98

Figure 2.14b Np(V) sorption isotherms 

using a maximum range of Np(V) from 

2.0x10-7 M to 1.0x10-6 M, 125 g L-1 treated 

and unmodified NABS, in AGW-6, pH 

6.8, at 8% PCO2. 

0.5

0.6

0.7

0.8

0.9

1.0

-7.0 -6.8 -6.5 -6.3 -6.0 -5.8 -5.5 -5.3

Log NpSoln (mol L-1)

Log Kd (mL g-1)

 Treated NABS

 Unmodified NABS

Atmospheric PCO2: 125 g L-1, AGW-3

[Np]T = 3.0x10-7 to 5.0x10-6 M

 pH 8.0 ± 0.1

Figure 2.14c. Log Kd values for Np(V) 

sorption as a function of aqueous Np(V) 

remaining in solution using a maximum 

range of Np(V) from 3.0x10-7 M to 5.0x10-

6

 M, 125 g L-1 treated and unmodified 

NABS, in AGW-3 (pH 8) at atmospheric 

PCO2. 

0.5

0.6

0.7

0.8

0.9

1.0

-7.0 -6.8 -6.5 -6.3 -6.0

Log NpSoln (mol L-1)

Log Kd (mL g-1)

8% PCO2

125 g L-1 

[Np]T = 2.0x10-7 to 1.0x10-6 M

AGW-6

 pH 6.8 ± 0.1

Treated 

NABS

Unmodified 

NABS

Figure 2.14d. Log Kd values for Np(V) 

sorption as a function of aqueous Np(V) 

remaining in solution using a maximum 

range of Np(V) from 2.0x10-7 M to 1.0x10-

6

 M, 125 g L-1 treated and unmodified 

NABS, in AGW-6 (pH 6.8) at 8% PCO2. 

decrease in sorption to treated NABS is 

observed (see Table 2.12). 

The BET (Brunauer, Emmett, and Teller) 

specific surface area (Asp) of treated NABS was 

4.47 m2

 g-1 while the Asp for unmodified NABS 

was 5.15 m2

 g-1 (Sanpawanitchakit, 2002; Davis 

et al., 2004). In addition, all grain size fractions 

derived from unmodified NABS had higher Asp

values than treated NABS (Table 2.7). The 

higher Asp values associated with treated NABS 

suggest that carbonate mineral dissolution 

resulting from NABS treatment reduces the 

quantity of larger cemented fragments. A more 
43

detailed overview of NABS characterization is 

found in Sanpawanitchakit (2002), USNRC 

(2003), Kohler et al. (2004), and Davis et al., 

(2004). 

It follows that the higher sorption affinity for 

unmodified NABS is in part a consequence of 

the higher surface area. The role surface area 

plays in Np(V) sorption can be examined using a 

distribution coefficient corrected to account for 

surface area, Ka, as follows: 

 Ka = Kd

Asp

 [2.1] 

When surface area is taken into account, 

sorption to treated and unmodified sediment is 

equivalent under atmospheric PCO2. At 8% 

PCO2, the Ka values for treated and unmodified 

NABS are not as closely matched, but generally 

fall within the error associated with the data 

points. The minimal importance of carbonate 

minerals in Np(V) sorption to NABS is in 

contrast with the work of Keeney-Kennicutt and 

Morse (1984), which showed that at 

approximately pH 8 Np(V) had a higher affinity 

for calcite than iron oxides on a surface area 

corrected basis. However, recent work by 

Zavarin and Bruton (2004) suggests that Np(V) 

sorption affinity for calcite is considerably lower 

than suggested by the Keeney-Kennicutt and 

Morse study. For example, comparing the 

sorption of Np(V) to 1 g L-1 calcite obtained by 

Zavarin and Bruton (2004) and sorption to 

hematite obtained by Kohler et al. (1999) with 

similar surface areas (15.7 and 14.4 m2

 g-1, 

respectively) shows that Np(V) has a much 

higher affinity for hematite between pH 7 and 9 

at atmospheric PCO2. For example, the Kd for 

6x10-6 M Np(V) sorption to calcite at pH 8 is 

approximately 250 mL g-1 and 6x103

 mL g-1 for 

1.1x10-6 M Np(V) sorption to hematite. 

Sorption of Np(V) to 125 g L-1 and U(VI) to 50 

g L-1 treated NABS at atmospheric PCO2 at pH 

8.0 show very similar behavior (Figure 2.15). 

This can be attributed to the similarity in the 

percent sorbed of Np(V) and U(VI) at pH 8.0. 

Of interest, under atmospheric PCO2, pH 8 is the 

only pH between 6 and 10 at which Np(V) and 

U(VI) have approximately the same percent 

sorbed. 

Increasing the solid-solution ratio provides 

additional reactive surface sites for Np(V) 

complexation. This results in a substantial 

increase in sorption over the conditions studied. 

For example, Np(V) sorption increases from an 

average of 44% over the Np(V) concentration 

range studied using 125 g L-1 unmodified NABS 

to an average of 69% when the solid-solution 

ratio is increased to 500 g L-1 unmodified NABS 

at pH 8.0 ± 0.1 in AGW-3 under atmospheric 

PCO2 (Figure 2.16a). Presenting the above 

experimental data in the form of a linearized 

isotherm plot removes much, but not all, of the 

dependence of sorption on the solid-solution 

ratio (Figure 2.16b). Error analysis of the 

regression lines shows that the slope and yintercept values of the 125 and 500 g L-1 data 

sets are significantly different (see Table 2.12). 

In principle, presenting the data in the form of 

isotherm plots should eliminate the dependence 

on the solid-solution ratio resulting in 

overlapping isotherms within the error range. 

The lower sorption at 500 g L-1 suggests a 

particle concentration effect may be taking 

place. Close packing may cause particle-particle 

interactions that either physically block available 

surface sites or create electrostatic interactions 

that result in lower attraction of Np(V) for 

NABS surface sites (USEPA, 1999). Also, 

variability in solution composition due to 

mineral dissolution may increase the 

concentration of competitive cations and neutral 

or anionic species that can complex Np(V) as 

the solid-solution ratio increases. 

2.2.3.6 Np(V) Sorption Edge Results 

Sorption edge experiments were conducted 

between approximately pH 4 and 10, with most 

experiments covering pH 5-9. Several 

experiments contained data above pH 10. 

Preliminary experiments showed that samples 

initially adjusted to pH values below 

approximately pH 3 resulted in highly variable 

final pH values, as high as pH 7 in some cases. 

This is likely a result of sediment weathering 

which exposed carbonate minerals that 

otherwise were inaccessible without grinding the 

sediment (Miller and Gardiner, 1998). It follows 

that pH adjustments were conducted so as to 
44 

-9.3

-9.1

-8.8

-8.6

-8.3

-8.1

-7.8

-7.0 -6.8 -6.5 -6.3 -6.0 -5.8 -5.5 -5.3

Log Usoln or Npsoln (mol L-1)

Log Np(V) Sorbed (mol g-1)

Atmospheric PCO2: Treated NABS

[Np(V)]T = 3.0x10-7 to 3.0x10-6 M

[U(VI)]T = 3.0x10-7 to 4.0x10-6 M

AGW-3, pH 8.0 ± 0.1

Np(V): 125 g L-1

Slope = 0.83

R2 = 0.98

U(VI): 50 g L-1

Slope = 0.85

R2 = 0.99

Figure 2.15. Comparison of U(VI) and Np(V) sorption isotherms (Np(V) concentration 

range: 3.0x10-7 to 3.0x10-6 M) on 125 g L-1 treated NABS and U(VI) concentration range of 

3.0x10-7 to 4.0x10-6 M on 50 g L-1 treated NABS in AGW-3 (pH 8.0) at atmospheric PCO2. 

0

10

20

30

40

50

60

70

80

90

100

-7.0 -6.8 -6.5 -6.3 -6.0 -5.8 -5.5 -5.3

Log NpSoln (mol L-1)

Percent Np(V) Sorbed

Unmodified NABS

 [Np]T = 3.0x10-7 to 1.0x10-5 M

Atmospheric PCO2, AGW-3, pH 8.0 ± 0.1

125 g L-1 500 g L-1

Figure 2.16a. Np(V) sorbed as a function of Np(V) remaining in solution using a maximum 

range of Np(V) from 8x10-7 M to 1x10-5 M, 125 g L-1 or 500 g L-1 unmodified NABS, in 

AGW-3 (pH 8) at atmospheric PCO2. 

-9.1

-8.9

-8.6

-8.4

-8.1

-7.9

-7.0 -6.8 -6.5 -6.3 -6.0 -5.8 -5.5 -5.3

Log Npsoln (mol L-1)

Log Np(V) Sorbed (mol g-1)

125 g L-1

Slope= 0.83

R2

 = 0.99

500 g L-1

Slope= 0.91

R2

 = 0.99

Unmodified NABS

 [Np]T = 8.0x10-7 to 

1.0x10-5 M

Atmospheric PCO2, AGW-3,

pH 8.0 ± 0.1 

Figure 2.16b. Np(V) sorption isotherms using a maximum range of Np(V) from 8x10-7 M 

to 1x10-5 M, 125 g L-1 and 500 g L-1 unmodified NABS, in AGW-3 (pH 8.0 ± 0.1) at 

atmospheric PCO2. 
45 

0

10

20

30

40

50

60

70

80

90

100

4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5

pH

Percent Np(V) Sorbed

50 g L-1 Treated NABS

Atmospheric PCO2

AGW-A

 [Np]T = 5.0x10-7 M 

Figure 2.17a. Sorption edges using 

5.0x10-7 M Np(V)on 50 g L-1 treated 

NABS in AGW-A at atmospheric PCO2. 

0

10

20

30

40

50

60

70

80

90

100

4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5

pH

Percent Np(V) Sorbed

125 g L-1 Treated NABS

Atmospheric PCO2

AGW-A

 [Np]T = 5.0x10-7 M 

Figure 2.17b. Sorption edges using 

5.0x10-7 M Np(V) on 125 g L-1 treated 

NABS in AGW-A at atmospheric PCO2. 

obtain the desired pH while maintaining the 

sediment in the carbonate-removed form. 

In AGW, Np(V) sorption to treated NABS 

shows weak pH dependence over the entire pH 

range studied. This is illustrated in Figure 2.17a, 

which shows the sorption of 5.0x10-7 M Np(V) 

to 50 g L-1 treated NABS in AGW-A at 

atmospheric PCO2. The lowest sorption (17%) 

is seen at approximately pH 5, which is the most 

acidic pH value for this data set. Low sorption 

in the acidic pH range is in part due to the large 

number of protons (e.g. [H+

] = 10-5 M at pH 5) 

competing with NpO2

+

 for NABS surface sites. 

Similar weak pH dependence is observed for the 

system using 5.0x10-7 M Np(V) and 125 g L-1

treated NABS at atmospheric PCO2 in AGW-A 

(Figure 2.17b). The maximum sorption of 34% 

occurs over a wide pH range, specifically 

between pH 7.4 and 8.9. In addition, the surface 

charge of treated NABS plays a part in the 

extent to which Np(V) sorbs. 

Unlike unmodified NABS, the pHpzse can be 

used to approximate the pHpzc for treated NABS 

because the removal of carbonate minerals 

reduces the excess potential determining ions 

other than protons. The change in pH (ΔpH) due 

to salt addition as a function of pH gives an 

approximate pHpzc = 7 (Figure 2.18a). Below 

the pHpzc the surface has a net positive charge 

and the addition of NaClO4 salt results in an 

increase in pH, above the pHpzc addition of 

NaClO4 causes a decrease in pH. Salt titrations 

for monomineralic oxide systems in an 

indifferent electrolyte generally lead to titration 

crossover points (ΔpH=0) that coincide. 

However, the lack of a crossover point for batch 

titrations of treated NABS indicates the presence 

of variable concentrations of cationic and 

anionic species due to mineral dissolution. For 

example, analysis of the acidified NaClO4

solution phase from 500 g L-1 treated NABS 

using inductively coupled plasma-atomic 

emission spectrometry (ICP-AES) shows that 

the concentrations of constituents such as Ca2+, 

Mg2+, Al(aq) and SiO2, vary dramatically over the 

pH range studied. This is illustrated in Figure 

2.18b, which shows the sum of the 

concentrations of the dissolved SiO2 and 

aqueous phase Al3+ species in the supernatant as 

a function of pH. The lowest concentrations of 

these species (close to the concentrations found 

in the blank samples) are found in the 

circumneutral pH range, consistent with the 

solubility of aluminum oxyhydroxides and 

amorphous silica. The high variability of the 

SiO2 concentration in solution at pH 6.2 and 8.3 

may be a source of variability in percent Npsorbed

in batch sorption experiments using NABS. 

Figure 2.18c shows the Ca2+ solution 

concentration due to mineral dissolution. The 

estimated Ca2+ concentration between 

approximately pH 6.5 and 8.5 is 5x10-4 M. 

Above pH ~8.5 the Ca2+ concentration drops to 

levels close to concentrations measured in 

samples without sediment present. 
46

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 pH

Change in pH upon Salt Addition

M 0.01

M 0.1

N2 Atmosphere

72 Hour Pre-equilbriation of 0.001 M NaClO4

500 g L-1 Treated NABS

pH=6.78

pH=7.15

 

Figure 2.18a. Salt titrations of 500 g L-1

treated NABS in 0.001 M NaClO4 within 

an inert atmosphere glovebox. 

-4.5

-4.0

-3.5

-3.0

-2.5

-2.0

-1.5

-1.0

3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0

pH

log ΣSi02 or ΣAl(aq)(mol L-1)

500 g L-1

Atmospheric PCO2

0.1 M NaClO4

72 Hour Pre-equilibration

ΣAl(aq)

ΣSiO2

Figure 2.18b. Concentrations of Al(aq)

and SiO2 in the solution phase of 500 g L-1

treated NABS in 0.1 M NaClO4 at 

atmospheric PCO2. 

0

0.2

0.4

0.6

0.8

1

1.2

1.4

3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0

pH

Ca2+ (mmol L-1)

500 g L-1

Atmospheric PCO2

0.1 M NaClO4

72 Hour Pre-equilibration

5.0x10-4 M Ca2+ 

from sediment 

Figure 2.18c Concentrations of Ca2+ in 

the solution phase of 500 g L-1 treated 

NABS in 0.1 M NaClO4 at atmospheric 

PCO2. 

0

10

20

30

40

50

60

70

80

90

100

4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0

pH

Percent Np(V) Sorbed

[Np(V)]T = 1.1x10-6 M

Hematite = 1 g L-1

BET Surface Area = 43.8 m2 g-1

0.1 M NaClO4

Atmospheric PCO2

Figure 2.19. Np(V) sorption to 1 g L-1

hematite as a function of pH using 

5.0x10-7 M Np(V) in 0.1 M NaClO4 at 

atmospheric PCO2. 

Np(V) sorption to monomineralic oxides 

exhibits much stronger pH dependence than seen 

for Np(V) sorption to treated NABS. For 

example, sorption of 1.1x10-6 M Np(V) to 1 g L1

 hematite in 0.1 M NaClO4 at atmospheric 

PCO2 increases by 68% between pH 5.6 and 7.8 

(Figure 2.19). As shown in Figure 2.19, less 

than 5% of the total Np(V) sorbs to hematite 

below approximately pH 5.5. The negligible 

uptake of NpO2

+

 on hematite at low pH is to be 

expected given that hematite has a reported 

pHpzc of pH 8.5 or higher ( Kohler et al., 1999; 

Lenhart and Honeyman, 1999). In contrast, all 

Np(V) sorption edge experiments using treated 

NABS exhibit comparably high uptake in the 

acidic pH region. The contrast between Np(V) 

sorption behavior using treated NABS and 

hematite is a reminder of the heterogeneous 

nature of NABS and its clay surface coatings 

(Davis et al., 2004). 

Figure 2.20a illustrates the impact of increasing 

the solid concentration from 50 g L-1 to 125 g L-1

in AGW-A while holding the Np(V) 

concentration steady at 5.0x10-7 M. In 0.1 M 

NaClO4, an additional data set was collected 

using 500 g L-1 with all other conditions the 

same as the AGW-A system (Figure 2.20b). An 

increase in Np(V) sorption is observed 

regardless of the solution composition. For 

example, between pH 7 and 8, an increase in 

sorption of approximately 15% was observed 

with a 2.5 fold increase in solid concentration in 

both AGW-A and 0.1 M NaClO4. The increase 
47 

0

10

20

30

40

50

60

70

80

90

100

4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5

pH

Percent Np(V) Sorbed

 50 g L-1

 125 g L-1 

[Np(V)]T =5.0x10-7 M

Atmospheric PCO2

AGW-A

Figure 2.20a. Sorption edges using 

5.0x10-7 M Np(V), 50 g L-1 and 125 g L-1

treated NABS in AGW-A at atmospheric 

PCO2. 

0

10

20

30

40

50

60

70

80

90

100

5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0 pH

Percent Np(V) Sorbed

 125 g L-1

 50 g L-1

 500 g L-1

[Np(V)]T =5.0x10-7 M

Atmospheric PCO2

0.1 M NaClO4

Figure 2.20b. Sorption edges using 

5.0x10-7 M Np(V) , 50, 125 and 500 g L-1

treated NABS in 0.1 M NaClO4 at 

atmospheric PCO2. 

was extended to slightly more than 20% when 

the solid concentration was increased from 125 

to 500 g L-1 in 0.1 M NaClO4. The following 

surface reaction (Equation 2.2) and associated 

mass action expression give insight into the 

variables that play a role in the nonlinear 

sorption, 

≡ SOH + NpO2

+ ↔≡ SONpO2 + H+

 

 K = [ ] ≡ SONpO2 H+

[ ]

[ ] ≡ SOH NpO2

+

[ ] [2.2] 

where ≡SOH is a generic symbol for an 

amphoteric hydroxyl group. Rearranging the 

expression in terms of Npsorb gives, 

≡ SONpO2 = K[ ] ≡ SOH NpO2

+

[ ]

H+

[ ] [2.3] 

and the fractional Npsorb can be written as shown 

in equation 2.4. 

Fraction Np Sorb = [ ] ≡ SONpO 2

[ ] ≡ SONpO 2 + NpO 2

+

[ ] 

[2.4] 

Rewriting the expression in terms of [NpO2

+

], 

[≡SOH], [H+

] and the mass action expression, K, 

gives the following equation, 

Fraction Np Sorb =

NpO 2

+

[ ]K [ ] ≡ SOH

H+

NpO 2

+

[ ] K [ ] ≡ SOH

H+ + 1

⎧ ⎨ ⎩ ⎫ ⎬ ⎭ 

[2.5] 

which reduces to an expression that is 

independent of [NpO2

+

]. 

Fraction NpSorb =

K [≡ SOH]

H+

K [ ] ≡ SOH

H+ +1

 [2.6] 

Finally, Equation 2.7 shows more clearly that 

Npsorb will depend on the reactive surface site 

concentration as well as the mass action 

expression and protons released. 

 

Fraction NpSorb = [≡ SOH]

[ ] ≡ SOH +

H+

[ ]

K

 [2.7] 

Figures 2.21a, 2.21b and 2.21c show that 

fractional Np(V) sorption to NABS is largely 

independent of Np(V) concentration. Sorption 

edges using 500 g L-1 in 0.1 M NaClO4 are 

illustrated in Figure 2.21c while Figures 2.21a 
48 

0

10

20

30

40

50

60

70

80

90

100

4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5

pH

Percent Np(V) Sorbed

50 g L-1 Treated NABS

Atmospheric PCO2

AGW-A

 [Np]T = 5.0x10-7 M 

 [Np]T = 5.0x10-6 M 

Figure 2.21a. Sorption edges with 5.0x10-7

M and 5.0x10-6 M Np(V), 50 g L-1 treated 

NABS, in AGW-A at atmospheric PCO2. 

0

10

20

30

40

50

60

70

80

90

100

3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5

pH

Percent Np(V) Sorbed

125 g L-1 Treated NABS

Atmospheric PCO2

AGW-A

 [Np]T = 5.0x10-7 M 

 [Np]T = 5.0x10-6 M 

Figure 2.21b. Sorption edges with 5.0x10-7

M and 5.0x10-6 M Np(V), 125 g L-1 treated 

NABS, in AGW-A at atmospheric PCO2. 

0

10

20

30

40

50

60

70

80

90

100

3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5

pH

Percent Np(V) Sorbed

500 g L-1

Atmospheric PCO2

0.1 M NaClO4

 [Np(V)]T = 5.0x10-7 M

[Np(V)]T = 5.0x10-6 M 

Figure 2.21c. Sorption edges with 5.0x10-7

M and 5.0x10-6 M Np(V), 500 g L-1 treated 

NABS, in 0.1 M NaClO4 at atmospheric 

PCO2. 

and 2.21b show Np(V) sorption in AGW-A 

using 50 and 125 g L-1, respectively. The 

diagrams show that increasing the Np(V) 

concentration from 5.0x10-7 M to 5.0x10-6 M, 

while holding the solid-solution ratio constant, 

results in very similar percent Np(V) sorption. 

Isotherm results also suggest that percent Npsorb

is fairly constant over the Np(V) concentration 

range investigated for the edge experiments. 

However, a general trend towards slightly lower 

sorption at high Np(V) concentrations is 

observed when the entire Np(V) concentration 

range is considered at fixed pH (see Section 

2.2.3.5). Agreement between the experimental 

data at 5.0x10-7 M and 5.0x10-6 M is evidence of 

excess NABS surface sites at the lowest Np(V) 

concentration. This can be illustrated further by 

considering the percent Np(V) sorbed at both 

Np(V) concentrations, shown in Figure 2.21a 

with 50 g L-1 treated NABS. For example, at pH 

7.7 with 5.0x10-7 M Np(V), 30% or 3.0x10-9 mol 

of Np(V) are sorbed per gramof sediment. 

Increasing the Np(V) concentration to 5.0x10-6

M, 28% or 2.8x10-8 mol Np(V) are sorbed, very 

nearly a 10-fold increase. Therefore, the weak 

dependence of sorption on pH is not due to 

surface site saturation, which is characterized by 

‘flattening’ of sorption edges (Dzombak and 

Morel, 1990). 

The sorption of 5.0x10-7 M Np(V) and U(VI) to 

125 g L-1 treated NABS in AGW-A at 

atmospheric PCO2 are compared in Figure 2.22, 

which illustrates the key differences between the 

two actinides. The strong effect of carbonate 

complexation on U(VI) sorption is not evident in 

Np(V) sorption at atmospheric PCO2, primarily 

due to the predominance of NpO2

+

 over much of 

the pH range studied. In order to study the effect 

of carbonate complexation, several sorption 

edge experiments were conducted over an 

extended pH range, which included uptake 

between pH 9-11. For example, Np(V) sorption 

to 125 g L-1 treated NABS at atmospheric PCO2

in 0.1 M NaClO4 (see Figure 2.20b) displays a 

slow increase in sorption with increasing pH 

even in the alkaline pH range (pH 9-11), where 

the negatively charged NpO2CO3

-

 and 

NpO2(CO3)2

3- species are expected to be the 

primary Np(V) aqueous complexes. 
49 

0

10

20

30

40

50

60

70

80

90

100

6.5 7.0 7.5 8.0 8.5 9.0

pH

Percent Sorbed

Np(V)

U(VI)

[U(VI)]T =5.0x10-7 M

[Np(V)]T =5.0x10-7 M

Atmospheric PCO2

125 g L-1

AGW-A

Figure 2.22. Comparison of U(VI) and Np(V) sorption edges using 5.0x10-7 M, 125 g L-1

treated NABS in AGW-A at atmospheric PCO2. 

The findings in the current study are in 

agreement with numerous studies presented in 

the Np(V) sorption literature. For example, Del 

Nero et al. (2004) showed that Np(V) sorption to 

Al or Fe silicates steadily increased from 

approximately pH 3 to 10 under atmospheric 

conditions. Nakayama and Sakamoto (1991) 

found that 6x10-6 M Np(V) sorption to a variety 

of natural and synthetic Al and Fe oxides (1 g L1

) continuously moved upward to the highest pH 

investigated (pH 11). Experiments were 

conducted without solid present, which 

confirmed that Np(V) remained soluble below 

pH 11. In addition, Tochiyama et al. (1996) 

found that 5.5x10-13 M Np(V) sorption onto 20 g 

L-1 natural and synthetic α-Al2O3 increased 

relatively rapidly between pH 6 and 10. In 

summary, the majority of Np(V) sorption studies 

in the literature exhibit an increase in uptake 

with increasing pH at atmospheric PCO2. These 

findings as well as recent spectroscopic evidence 

for neptunyl-carbonato complexation at the 

hematite surface (Arai et al., 2007) are 

consistent with the possible occurrence of 

ternary complexes at the NABS surface. 

In order to gain further insight into the role of 

neptunyl-carbonato aqueous and/or surface 

reactions in Np(V) sorption, CO2 was excluded 

from the Np(V)-NABS system using a N2

atmosphere. Figure 2.23a shows sorption of 

5.0x10-7 M Np(V) on 50 and 125 g L-1 treated 

NABS in 0.1 M NaClO4 using a N2 atmosphere. 

At both solid concentrations, Np(V) uptake 

slowly increases by 60-65% between 

approximately pH 5 and 12. However, sorption 

under CO2-free conditions is very similar to the 

behavior at atmospheric PCO2 (Figure 2.23b). 

Examination of the aqueous speciation under the 

CO2-free conditions shows that NpO2

+

 is the 

dominant aqueous species up to pH 11, at which 

point NpO2OH0

 and NpO2(OH)2

-

 begin to 

become important. 

Relatively few data sets are available for 

comparison to Figure 2.23a that are 1) 

maintained under CO2-free conditions and 2) 

investigated above pH 9. However, Turner et al. 

(1998) observed an increase in 9x10-7 M Np(V) 

sorption to 4 g L-1 montmorillonite up to pH 

10.5, the highest pH investigated, using a N2
50 

0

10

20

30

40

50

60

70

80

90

100

5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 pH

Percent Np(V) Sorbed

 50 g L-1

 125 g L-1 

[Np(V)]T =5.0x10-7 M

CO2-Free Atmosphere

0.1 M NaClO4

Figure 2.23a. Sorption edges using 

5.0x10-7 M Np(V), 50 or 125 g L-1 treated 

NABS, in 0.1 M NaClO4 using a N2

atmosphere (CO2-free). 

0

10

20

30

40

50

60

70

80

90

100

5.0 6.0 7.0 8.0 9.0 10.0 11.0

pH

Percent Np(V) Sorbed

 [Np(V)]T =5.0x10-7 M

0.1 M NaClO4

125 g L-1

 Atmospheric PCO2

N2 Atmosphere

Figure 2.23b. Sorption edges using 

5.0x10-7 M Np(V), 125 g L-1 treated 

NABS, in 0.1 M NaClO4 at atmospheric 

PCO2 or using a N2 atmosphere (CO2-

free). 

atmosphere. Bertetti et al. (1998) studied 10-6-

10-7 M Np(V) sorption to 4 and 40 g L-1 quartz, 

montmorillonite and α-alumina as well as 4 and 

8 g L-1 clinoptilolite up to approximately pH 11 

using CO2-free conditions and atmospheric 

PCO2 conditions. Bertetti and coworkers 

showed an increase in Np(V) sorption to all the 

minerals investigated over the entire pH range 

when CO2-free conditions were employed. 

A few studies demonstrate unambiguous 

decreases in percent Npsorb as pH increases. For 

example, Turner et al. (1998) examined 9x10-7

M Np(V) sorption to 4 g L-1 montmorillonite at 

atmospheric PCO2. Unlike the system using an 

N2 atmosphere, uptake decreased above pH 8. 

Bertetti et al. (1998) observed the same trends 

for the quartz, montmorillonite and clinoptilolite 

systems discussed above. As a general rule, 

pHpzc for silica and clay minerals are below pH 4 

while Al and Fe oxides and oxyhydroxides have 

pHpzc > 7 (Sposito, 1998). It follows that the 

low sorption affinity of NpO2

+

 observed by 

Turner et al. (1998) and Bertetti et al (1998), 

beginning at pH 8, can be explained by the low 

pHpzc of the mineral surfaces and the relatively 

steep decline in NpO2

+

 as the dominant aqueous 

species. 

Figure 2.24a shows the sorption of Np(V) to 500 

g L-1 treated NABS in 0.1 M NaClO4 at 8% 

PCO2. The most significant feature is a sharp 

increase in sorption at approximately pH 7.3 

followed by a 37% decrease in uptake from pH 

7.6 to 8.1, the highest pH data point. The 

sorption edges at atmospheric and 8% PCO2, 

with all other conditions held the same, are 

compared in Figure 2.24b. This sorption 

behavior can be explained by either 1) 

precipitation of calcite followed by Np(V) 

sorption to the calcite surface sites or 2) coprecipitation of Np(V) with calcite. The 

dissolution of 500 g L-1 treated NABS leads to 

5x10-4 M Ca2+ being released into solution 

between approximately pH 6.5 and 8.5 (see 

Figure 2.18c). Therefore, at 8% PCO2 solubility 

calculations estimate that calcite oversaturation 

will occur at pH 7.3. Np(V) sorption or coprecipitation is substantiated by sorption edge 

experiments conducted at atmospheric PCO2 in 

AGW-3, which show that strong Np(V) uptake 

takes place as pH increases (Figure 2.24c). In 

AGW-3, the sharp increase in sorption that 

occurs at approximately pH 8.2 coincides with 

calcite oversaturation at atmospheric PCO2. 

Aqueous speciation calculations suggest that the 

primary aqueous species is NpO2CO3

-

 under 

both atmospheric and 8% PCO2 conditions. The 

decrease in sorption observed above 

approximately pH 7.6 at 8% PCO2 is consistent 

with the transition to NpO2(CO3)2

3- as the 

dominant aqueous complex. A possible 

decrease in Np(V) uptake is evident below pH 

7.0-7.5 due to the increase in PCO2 to 8%. The 

edge experiments show that at pH 8.0 and 

atmospheric PCO2, sorption is substantially 

higher than sorption at pH 6.8 and 8% PCO2. 
51

0

10

20

30

40

50

60

70

80

90

100

4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5

pH

Percent Np(V) Sorbed

[Np(V)]T =5.0x10-7 M

8% PCO2

0.1 M NaClO4

500 g L-1

Figure 2.24a. Sorption edge using 5.0x10-

7

 M Np(V), 500 g L-1 treated NABS, in 0.1 

M NaClO4 at 8% PCO2. 

0

10

20

30

40

50

60

70

80

90

100

4.5 5.5 6.5 7.5 8.5 9.5 10.5

pH

Percent Np(V) Sorbed

[Np(V)]T =5.0x10-7 M

500 g L-1

0.1 M NaClO4

 Atmospheric PCO2

8% PCO2

Figure 2.24b. Comparison of 5.0x10-7 M 

Np(V) sorption edges at atmospheric and 

8% PCO2 using 500 g L-1 treated NABS 

in 0.1 M NaClO4. 

0

10

20

30

40

50

60

70

80

90

100

3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5

pH

Percent Np(V) Sorbed

[Np]T =5x10-7 M 

Atmospheric PCO2

AGW-3

 50 g L-1

 125 g L-1

Figure 2.24c. Comparison of 5.0x10-7 M 

Np(V) sorption edges at atmospheric 

PCO2 using 50 and 125 g L-1 treated 

NABS in AGW-3. 

This is evidence that the decrease in sorption 

observed for the isotherm experiments (e.g. see 

Figures 2.12a and 2.12b) is significant, since the 

expected aqueous species distribution for both 

PCO2 systems includes 75% NpO2

+

 and 25% 

NpO2CO3

-

. 

2.2.3.7 Effect of Groundwater 

Constituents on Np(V) Sorption 

Comparing sorption edges at the same solidsolution ratio using AGW-3, AGW-A or 

NaClO4, at pH below approximately pH 8.1 (to 

avoid precipitation of calcite in AGW-3), 

suggests that changes in solution composition 

effect the sorption affinity of NpO2

+

 for treated 

NABS. A decrease in sorption was most 

pronounced when comparing sorption in AGW3 and AGW-A using 125 g L-1 treated NABS 

(Figure 2.25a). NaClO4 experiments were 

conducted at 0.1 M while the ionic strength of 

AGW-3 and AGW-A were both 0.02 M. AGW3 included 5.4x10-4 M additional HCO3

-

 while 

AGW-A and 0.1 M NaClO4 had no added 

HCO3

-

. Np(V) sorption to 125 g L-1 treated 

NABS in 0.1 M NaClO4 and AGW-A are 

compared in Figure 2.25b. The sorption edges 

clearly show that Np(V) sorption decreases 

above approximately pH 8 in AGW-A. These 

results are consistent with the Del Nero et al. 

(1998) study of Np(V) sorption to hydrargilite 

using a N2 atmosphere and adding 0, 10-3 M and 

10-2 M CO3

2-. For example, Del Nero and 

coworkers found that sorption at pH 8.5 

decreased from approximately 75% to 43% 

when the carbonate concentration was increased 

from 0 to 10-2 M. Sorption using 10-3 M CO3

2-

showed similar behavior to the CO2-free 

experiments. 

The Ca2+ concentration was an order of 

magnitude higher in AGW-3 than AGW-A, 

specifically 4.7x10-3 and 4.7x10-4 M, 

respectively. Given that lower Np(V) sorption 

to sediment was previously observed with 

additional Ca2+ (Routson et al., 1977; McCubbin 

and Leonard, 1995), it is reasonable to conclude 

that relatively high Ca2+ concentrations will 

compete with Np(V) for NABS surface sites. 
52 

0

10

20

30

40

50

60

70

80

90

100

4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0

pH

Percent Np(V) Sorbed

125 g L 

-1 Treated NABS

Atmospheric PCO2

 [Np]T = 5.0x10-7 M 

 AGW-3

 AGW-A

 NaClO4

Figure 2.25a. Ny(V) sorption edges with 

5x10-7 M Np(V), 125 g L-1 treated NABS, 

in AGW-A, 0.1 M NaClO4 or AGW-3 at 

atmospheric PCO2. 

0

10

20

30

40

50

60

70

80

90

100

4.0 5.0 6.0 7.0 8.0 9.0 10.0

pH

Percent Np(V) Sorbed

 125 g L-1 Treated NABS

Atmospheric PCO2

 [Np]T = 5.0x10-7 M 

 AGW-A

NaClO4

Figure 2.25b. Np(V) sorption edges with 

5x10-7 M Np(V), 125 g L-1 treated NABS, 

in AGW-A solution or 0.1 M NaClO4 at 

atmospheric PCO2. 

Preliminary experiments using 0.1 M and 0.01 

M Ca2+ suggest that increasing Ca2+

concentration by several orders of magnitude 

higher than in either type of AGW has no 

obvious impact on sorption (data not shown). 

The impact of HA on Np(V) uptake to NABS 

was investigated over the range of conditions 

summarized in Table 2.11. Figures 2.26a and b 

show that sorption of 5.0x10-7 M or 5.0x10-6 M 

Np(V) to 500 g L-1 treated NABS at atmospheric 

PCO2 is unaltered by the addition of 10 mg L-1

HA. However, Np(V) sorption is substantially 

decreased at both Np(V) concentrations over 

much of the pH range studied in the presence of 

100 mg L-1 HA. These results suggest the 

formation of an NpO2-HA aqueous complex. 

Figures 2.27a and b show that Np(V) sorption 

increases as sorption of 100 mg L-1 HA to 

treated NABS decreases at high pH, which 

suggests that HA sorption to NABS is 

competing with Np(V) for NABS surface sites. 

2.2.4 Summary of Np(V) Sorption 

Results 

Np(V) sorption to treated and untreated NABS 

was investigated over a wide range of system 

conditions. The most important findings follow: 

1. The effect of pH is relatively weak compared 

to that observed for Np(V) to monomineralic 

oxides and oxyhydroxides as well as for U(VI) 

sorption to NABS (Sanpawanitchakit, 2002; this 

work). However, the increase in sorption even 

in the alkaline pH range is consistent with 

Np(V) sorption to many pure minerals at 

atmospheric PCO2 and CO2-free conditions. 

2. Varying PCO2 has either a minor or no impact 

on Np(V) sorption to NABS. Increasing PCO2

while allowing pH to reach equilibrium values 

has minimal impact on Np(V) sorption to treated 

and untreated NABS, due to the similarities in 

aqueous speciation at the equilibrium pH values. 

Sorption edge experiments were also minimally 

impacted below pH 7.5 by adjusting the PCO2 to 

8%. However, above about pH 7.5, precipitation 

of Np(V) is suggested by a steep increase in 

sorption. Sorption edge experiments conducted 

using CO2-free conditions produced similar 

results to experiments under atmospheric PCO2. 

3. Sorption of carbonate species to the NABS 

surface appears to cause a minor reduction in 

Np(V) sorption. In contrast, previous studies by 

Sanpawanitchakit (2002), USNRC (2003) and 

Davis et al. (2004) show that U(VI) sorption to 

NABS is considerably more PCO2 dependent. 

4. Increasing the solid-solution ratio increases 

Np(V) sorption due to the additional surface site 

availability. 

5. NpO2

+

-HA aqueous complexation and HA 

sorption to the sediment reduces the Np(V) 

sorption affinity for treated NABS at high HA 

concentrations only.
53 

0

10

20

30

40

50

60

70

80

90

100

3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0

pH

Percent Np(V) Sorbed

[Np(V)]T = 5.0x10-7 M

500 g L-1 Treated NABS

Atmospheric PCO2

0.1 M NaClO4

 HA =10 mg L-1

 HA =100 mg L-1

 No HA

Figure 2.26a. Effect of 10 mg L-1 and 100 mg L-1 HA on Np(V) sorption edges using 5.0x10-

7

 M Np(V), 500 g L-1 treated NABS, in 0.1 M NaClO4 at atmospheric PCO2. 

0

10

20

30

40

50

60

70

80

90

100

3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0

pH

Percent Np(V) Sorbed

 

 

 

[Np(V)]T = 5.0x10-6 M

500 g L-1 Treated NABS

Atmospheric PCO2

0.1 M NaClO4

 HA =10 mg L-1

 HA =100 mg L-1

 No HA

Figure 2.26b. Effect of 10 and 100 mg L-1 HA on Np(V) sorption edges using 5.0x10-6 M 

Np(V), 500 g L-1 treated NABS, in 0.1 M NaClO4 at atmospheric PCO2. 
54

0

10

20

30

40

50

60

70

80

90

100

3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0

pH

Percent Np(V) or HA Sorbed

500 g L-1

Atmospheric PCO2

0.1 M NaClO4

 HA = 100 mg L-1

 Np(V) = 5.0x10-7 M 

Figure 2.27a. Comparison of HA and Np(V) sorption edges using 100 mg L-1 HA, 5.0x10-7

M Np(V), 500 g L-1 treated NABS in 0.1 M NaClO4 at atmospheric PCO2. 

0

10

20

30

40

50

60

70

80

90

100

3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5

pH

Percent Np(V) or HA Sorbed

500 g L-1

Atmospheric PCO2

0.1 M NaClO4 HA = 100 mg L-1

 Np(V) = 5.0x10-6 M 

Figure 2.27b. Comparison of HA and Np(V) sorption edges using 100 mg L-1 HA, 5.0x10-6

M Np(V), 500 g L-1 treated NABS in 0.1 M NaClO4 at atmospheric PCO2. 
55 

2.3 Surface Complexation 

Modeling of Neptunium(V) 

Sorption in Natural Systems

2.3.1 Introduction 

Performance assessments assist in evaluating the 

long-term safety of underground nuclear waste 

repositories. Generally, transport calculations 

within performance assessments account for 

sorption to mineral surfaces using distribution 

coefficients, Kd values (Bethke and Brady, 

2000). However, changing geochemical 

conditions, such as pH, PCO2 and mineral 

surface properties can substantially alter Kd

values, resulting in inaccurate transport 

simulations (Glynn, 2003). Surface 

complexation models (SCMs) of the 

water/mineral interface have the capability of 

more accurately describing metal-ion sorption 

over a range of system conditions. Few SCMs 

have been developed to describe sorption to 

complex geomedia; this is in part due to 

difficulties in describing the electric double 

layer (EDL) of the water/mineral interface and 

in identifying specifically which minerals are 

involved in sorption (Davis et al., 1998). 

However, successful model simulations will 

foster the assimilation of SCMs into reactive 

transport models. In order to facilitate this 

transition, Curtis et al. (2006) recently 

demonstrated that incorporation of an SCM 

within a reactive transport calculation leads to an 

acceptable model for U(VI) transport through a 

shallow alluvial aquifer. 

As illustrated in Table 2.13, a number of studies 

show that SCMs can accurately simulate Np(V) 

sorption to pure mineral phases over a range of 

system conditions characteristic of 

geochemical environments (e.g. Girvin et al., 

1991; Kohler et al., 1999; Del Nero et al., 2004). 

An extensive literature review showed that 

SCMs have yet to be used to describe Np(V) 

sorption to complex geomedia. The goal of the 

current study is to test the ability of SCMs to 

describe Np(V) sorption to an alluvial aquifer 

sediment under a range of geochemical 

conditions. 

2.3.2 Generalized Composite Modeling 

Approach 

To date, GC modeling of sorption to complex 

geomedia has been accomplished without 

explicit correction for electrostatic interactions. 

The choice to use a non-electrostatic model 

(NEM) in this study is due to the difficulty in 

obtaining EDL information for the study 

sediment. However, an ‘effective’ point of zero 

charge (pHpzc) was determined in Section 2.3.3.1 

for treated (carbonate mineral-removed) Naturita 

alluvial background sediment (NABS), allowing 

surface reactions to be derived to describe the 

protonation and deprotonation of treated NABS 

surface sites. 

Np(V) sorption was simulated using FITEQL 

(version 4.0), a nonlinear, least-squares 

optimization program (Herbelin and Westall, 

1999). The extent of agreement between model 

results and experimental data, the goodness-offit, was established graphically and/or using the 

weighted sum of the squares of the residuals 

divided by the degrees of freedom (WSOS/DF) 

(Cernik et al., 1995). Valid WSOS/DF values 

are obtained when realistic error estimates are 

input into the model calculations. 

Thermodynamic surface speciation models, such 

as the component additivity (CA) SCM 

developed by Honeyman (1984), require 

spectroscopic evidence to substantiate surface 

reactions and electric double layer properties 

(Hiemstra and van Riemsdijk, 1999). In 

contrast, the GC SCM does not require 

validation by spectroscopy, but instead is based 

on chemically plausible surface reactions. If 

possible, it is preferable to develop a GC model 

based on molecular level information. Few 

studies are available that explore the surface 

complexation behavior of Np(V) on a molecular 

scale. The Combes et al. (1992) study used Xray absorption fine structure spectroscopy 

(EXAFS) to elucidate the surface coordination 

of Np(V) at the goethite (α-FeOOH) surface. 

Their work showed that NpO2

+

 sorbs to the 

goethite surface as a mononuclear inner-sphere 

complex at pH 7.2, 7x10-7 M Np(V), I = 0.08 M 

NaNO3 and 1 g L-1 goethite. However, Combes 

and coworkers acknowledged that the presence 
56

Table 2.13 Surface Complexation Modeling Studies of Np(V) Sorption to Pure Mineral P

AlOH = aluminum oxide surface hydroxyl groups, FeOH= iron oxide surface hydroxyl groups, IX= ion exchange s

 I =0.1 M NaNO3, NaClO4 or where noted with * tris(hydroxymethyl)amino-methane + 2-morpholinoethane sulfon

Reference 

Mineral 

Phase Surface Reaction Log K 

Np(V) 

(M)

PCO2

(Atm)

I 

(M) EDL 

Sou

Expe

Alumina 

(α-Al2O3) 

AlOH + H+ = AlOH2

+

AlOH = AlO- + H+ 

AlOH + NpO2

+ = AlONpO2 + H+ 

AlOH + NpO2

+ = AlOHNpO2+ 

AlOH + NpO2

+ +H2O = AlONpO2OH-

+ 2H+

AlOH + NpO2

+ + H2O = 

AlOHNpO2OH + H+

AlOH2

+ + NpO2

+ + H2O = 

AlOH2NpO2OH+ + H+

2AlOH + NpO2+ = (AlO)2NpO2

- + 2H+

DDL 7.29 

DDL -8.93 

DDL -4.26 

DDL 5.07 

DDL-13.37 DDL -4.26 

DDL 5.07 

DDL -7.99 

6.0x10-6 <10-3.5 0.1 CCM, 

DDL, 

TLM 

Tu

Na

Sak

Alumina AlOH + H+ = AlOH2

+

AlOH = AlO- + H+ 

AlOH + NpO2

+ = AlONpO2 + H+ 

AlOH + NpO2

+ + H2O = 

AlOHNpO2OH- + 2H+

8.33 

-9.73 

-2.52 to -4.86 

-12.44 to -14.29 

1.9x10-7, 

1.9x10-9, 1.0x10-6, 

6.0x10-6

10-3.5, 

0.05 M 

0.01, 

0.1 

NEM Zavar

(2004

(1995

(1998) 

and S

and 

Alumina AlOH + H+ = AlOH2

+

AlOH = AlO- + H+

2AlOH + NpO2+ = (AlO)2NpO2

- + 2H+

8.33 

-9.73 -8.23 

6.0x10-6 <10-3.5 0.1* DDL Wan

Na

Sakam

Toc
57 

Table 2.13 (continued). Surface Complexation Modeling Studies of Np(V) Sorption to Pure Min

Reference 

Mineral 

Phase Surface Reaction Log K 

Np(V) 

(M)

PCO2

(Atm)

I 

(M) EDL 

Sou

Expe

Alumina AlOH + H+ = AlOH2

+

AlOH = AlO- + H+ 

AlOH + NpO2

+ = AlONpO2 + H+ 

AlOH + NpO2

+ + H2O = 

AlOHNpO2OH- + 2H+ 

8.33 

-9.73 

-2.18 to -2.49 

-10.19 to -13.65 

10-14 and 

6.0x10-6

10-3.5, 

0.05 M 

0.1 NEM Z

Brut

Tu

Berte

and N

Sak

and 

(1988

e

Alumina 

(γ-Al2O3) 

AlOH + H+ = AlOH2

+

AlOH = AlO- + H+

AlOH + NpO2

+ +H2O = 

AlONpO2OH- + 2H+

6.85 

-9.05 

-10.93 

10-14 0 0.1 DDL Wan

Ri

Amorphous 

Al Silicates 

(Si/Al = 

4.3 -10) 

SiOH + H+ = SiOH2

+

SiOH = SiO- + H+ 

AlOH + H+ = AlOH2

+

AlOH = AlO- + H+ 

AlOH + NpO2

+ = AlONpO2 + H+ 

SiOH + NpO2

+ = SiOHNpO2

+

1.0 

-7.0 

7.5 

-10.2 

-2.9 

0.44 

6x10-6 – 

1x10-9

0, 10-3.5 0.001

, 0.1 

DDL De

(

Sv

Sah

Bonn

et a

Ner

Amorphous 

Fe Silicates 

(Si/Al = 

2.3 -10) 

SiOH + H+ = SiOH2

+

SiOH = SiO- + H+ 

FeOH + H+ = FeOH2

+

FeOH = FeO- + H+ 

FeOH + NpO2

+ = FeONpO2 + H+ 

SiOH + NpO2

+ = SiOHNpO2

+

1.0 

-7.0 

7.29 

-8.93 

-2.9 

0.44 

1x10-6 – 

1x10-9

0, 10-3.5 0.01, 

0.1 

DDL De

(200
58 

Table 2.13 (continued). Surface Complexation Modeling Studies of Np(V) Sorption to Pure Min

Reference 

Mineral 

Phase Surface Reaction Log K 

Np(V) 

(M)

PCO2

(Atm)

I 

(M) EDL 

S

Amorphous 

SiO2

SiOH = SiO- + H+

SiOH + NpO2

+ = SiONpO2 + H+

SiOH + NpO2

+ = SiOHNpO2+ 

SiOH + NpO2

+ +H2O = SiONpO2OH- + 

2H+

SiOH + NpO2

+ + H2O = SiOHNpO2OH0 

+ H+

SiOH2

+ + NpO2

+ + H2O = 

SiOH2NpO2OH+ + H+

2SiOH + NpO2

+ = (SiO)2NpO2

- + 2H+

DDL -7.20 DDL -5.39 

DDL 1.30 

DDL -11.89 

DDL -5.39 

DDL 1.30 

DDL -12.53 

10-14 0 0.1 CCM, 

DDL, 

TLM 

T

R

Amorphous 

SiO2

SiOH = SiO- + H+

SiOH + NpO2

+ +H2O = SiONpO2OH- + 

2H+

-7.20 

12.54 

1.0x10-14 0 0.1 DDL 

S

R

Biotite 

(Si/Al = 3) 

AlOH + H+ = AlOH2

+

AlOH = AlO- + H+

SiOH = SiO- + H+

SiOH + NpO2

+ = SiONpO2 + H+

AlOH + NpO2

+ +H2O = AlONpO2OH- + 

2H+

SiOH + NpO2

+ +H2O = SiONpO2OH- + 

2H+

8.33 

-9.73 -7.20 -4.15 

-12.39 -11.58 

6.0x10-6 0 0.1 DDL 

S

N

Sa

Biotite 

(Si/Al = 3) 

AlOH + H+ = AlOH2

+

AlOH = AlO- + H+ 

SiOH = SiO- + H+ 

SiOH + NpO2

+ = SiONpO2 + H+

AlOH + NpO2

+ +H2O = AlONpO2OH- + 

2H+

8.33 

-9.73 -7.20 -4.17 

-11.50 

6.0x10-6 0 0.1 DDL 

S

N

Sa
59 

Table 2.13 (continued). Surface Complexation Modeling Studies of Np(V) Sorption to Pure Min

Reference 

Mineral 

Phase Surface Reaction Log K 

Np(V) 

(M)

PCO2

(Atm)

I 

(M) EDL 

S

Boehmite 

(γ-AlOOH) 

AlOH + H+ = AlOH2

+

AlOH = AlO- + H+ 

AlOH + NpO2

+ = AlONpO2 + H+ 

AlOH + NpO2

+ = AlOHNpO2+ 

AlOH + NpO2

+ +H2O = AlONpO2OH- + 

2H+

AlOH + NpO2

+ + H2O = AlOHNpO2OH

+ H+

AlOH2

+ + NpO2

+ + H2O = 

AlOH2NpO2OH+ + H+

2AlOH + NpO2+ = (AlO)2NpO2

- + 2H+

DDL 6.85 

DDL -9.05 

DDL -3.36 

DDL 4.09 

DDL-10.84 DDL -3.36 

DDL 4.09 

DDL -7.60 

6.0x10-6 <10-3.5 0.1 CCM, 

DDL, 

TLM 

T

N

Sa

Boehmite AlOH + H+ = AlOH2+ 

AlOH = AlO- + H+

AlOH + NpO2

+ = AlOHNpO2+ 

AlOH + NpO2

+ = AlONpO2 + H+

6.85 

-9.05 

3.89 

-4.50 

6x10-6 <10-3.5 0.1 DDL W

a

S

N

Sa

Calcite 

(CaCO3) 

IX + Ca2+ = IXCa2+

IX + 2NpO2

+ = IX(NPO2

+

)2

CO3H + NpO2

+ = CO3HNpO2

+

CO3H + Ca2+ = CO3Ca+ + H+

CaOH + NpO2

+ + H2O = CaONpO2OH-

+ 2H+

CaOH + NpO2

+ + CO32- = 

CaOHNpO2CO3

IX 2.4 IX 12.05 

CC 8.6 

CC -1.9 CC -9.6 

CC 12.4 

1.6x10-6 10-3.5 ~0.1 

to 

0.6 

CCM, 

IX 

B

B

Calcite >Ca2+ + NpO2

+ = >NpO2

+ + Ca2+ 1.46 ~10-7 10-3.5 0.1 NEM 

B

B
60 

Table 2.13 (continued). Surface Complexation Modeling Studies of Np(V) Sorption to Pure Min

Reference 

Mineral 

Phase Surface Reaction Log K 

Np(V) 

(M)

PCO2

(Atm)

I 

(M) EDL 

S

Exp

Clinoptilolite AlOH + H+ = AlOH2

+

AlOH = AlO- + H+

SiOH = SiO- + H+ 

SiOH + NpO2

+ = SiOHNpO2+ 

AlOH + NpO2

+ +H2O = AlONpO2OH- + 

2H+

8.33 

-9.73 -7.20 

3.27 

-13.20 

1x10-6 0 0 DDL Wa

an

Sa

Clinoptilolite AlOH + H+ = AlOH2

+

AlOH = AlO- + H+ 

AlOH + NpO2

+ = AlONpO2 + H+ 

AlOH + NpO2

+ +H2O = AlONpO2OH- + 

2H+

8.33 

-9.73 

Average -

4.67 

Average -

14.26 

1.0x10-6 0, 

10-3.5

0.01, 

0.1 

NEM 

B

Diaspore 

(α-AlOOH) 

AlOH + H+ = AlOH2+ 

AlOH = AlO- + H+

AlOH + NpO2

+ = AlONpO2 + H+

AlOH + NpO2

+ = AlOHNpO2+ 

AlOH + NpO2

+ +H2O = AlONpO2OH- + 

2H+

8.33 

-9.73 -3.62 

5.50 

-13.10 

5.5x10-13 0 0.1* DDL Wa

an

Sa

To

Ferrihydrite 

(Fe2O3•H2O) 

FeOH + H+ = FeOH2

+

FeOH = FeO- + H+ 

FeOH + Na+ = FeO---Na+ + H+

FeOH + H+ + NO3

- = FeOH2 +--NO3- 

FeOH + NpO2

+ = FeONpO2 + H+ 

FeOH + NpO2

+ +H2O = FeOHNpO2OH

+H+

5.4 

-10.4 

-8.6 

7.5 5.6 

-3.2 

4.7x10-12 10-3.5 0.1 TLM 

(1

Ferrihydrite FeOH + H+ = FeOH2

+

FeOH = FeO- + H+ 

FeOH + NpO2

+ = FeONpO2 + H+

7.29 

-8.93 -2.72 

4.7x10-12 10-3.5 0.1 DDL Wa

an

Sa

Gir
61 

Table 2.13 (continued). Surface Complexation Modeling Studies of Np(V) Sorption to Pure Min

Reference 

Mineral 

Phase Surface Reaction Log K 

Np(V) 

(M)

PCO2

(Atm)

I 

(M) EDL 

So

Exp

Ferrihydrite FeOH + H+ = FeOH2

+

FeOH = FeO- + H+ 

FeOH + NpO2

+ = FeONpO2 + H+

FeOH + NpO2

+ = FeOHNpO2+ 

FeOH + NpO2

+ +H2O = 

FeONpO2OH- + 2H+

FeOH + NpO2

+ + H2O = 

FeOHNpO2OH + H+

FeOH2

+ + NpO2

+ + H2O = 

FeOH2NpO2OH+ + H+

2FeOH + NpO2

+ = (FeO)2NpO2

- + 

2H+

DDL 7.29 

DDL -8.93 

DDL -3.02 

DDL 5.04 

DDL-11.08 DDL -3.02 

DDL 5.04 

DDL -8.28 

4.7x10-12 10-3.5 0.1 CCM, 

DDL, 

TLM 

Tu

Girv

Ferrihydrite FeOH + H+ = FeOH2

+

FeOH = FeO- + H+ 

FeOH + NpO2

+ = FeOHNpO2+ 

FeOH + NpO2

+ +H2O = 

FeONpO2OH- + 2H+

Average 7.29 

Average -8.93 

Average 4.32 

Average 

-11.26 

4.7x10-12 10-3.5 0.1 NEM Z

Br

Girv

Gibbsite 

(γ-Al(OH)3) 

AlOH + H+ = AlOH2+ 

AlOH = AlO- + H+

AlOH + NpO2

+ = AlNpO2 + H+ 

AlOH + NpO2

+ = AlOHNpO2

+

6.85 

-9.05 -3.53 

4.95 

5.5x10-13 0 0.1* DDL Wan

an

Sas

To

Goethite 

(α-FeOOH) 

FeOH + H+ = FeOH2

+

FeOH = FeO- + H+ 

FeOH + Na+ = FeO---Na+ + H+

FeOH + H+ + ClO4

- = FeOH2 +--

ClO4

-

FeOH + NpO2

+ = FeONpO2

++ H+ 

FeOH + NpO2

+ + H+ + CO32- = 

FeONpO2(HCO3)2-

7.9 

-9.9 

-8.76 

8.89 

-1.57 

22.81 

1.2x10-7 0, 

10-3.5, 

10-2.7

0.1 TLM K

(19

al. (

Gee
62 

Table 2.13 (continued). Surface Complexation Modeling Studies of Np(V) Sorption to Pure Min

Reference 

Mineral 

Phase Surface Reaction Log K 

Np(V) 

(M)

PCO2

(Atm)

I 

(M) EDL 

Sou

Expe

Goethite 

(α-FeOOH) 

FeOH + H+ = FeOH2

+

FeOH = FeO- + H+ 

FeOH + NpO2

+ = FeOHNpO2+ 

FeOH + NpO2

+ +H2O = 

FeONpO2OH- + 2H+

7.35 

-9.17 

6.03 

-12.0 

6.0x10-6, 

1.2x10-12

0 0.1 DDL Wan

and 

Sas

Na

Sak

and 

Goethite FeOH + NpO2

+ = FeONpO2 + H+

FeOH + H+ + CO32- = FeOH2CO3

-

FeOH + 2H+ + CO32- = FeOH2HCO3

0.8 

16.5 21.5 

4x10-7- 

6x10-6

10-3.5 0.1 CCM Fuji

Fuji

Goethite FeOH + H+ = FeOH2

+

FeOH = FeO- + H+ 

FeOH + NpO2

+ = FeONpO2 + H+

FeOH + NpO2

+ = FeOHNpO2+ 

FeOH + NpO2

+ +H2O = 

FeONpO2OH- + 2H+

FeOH + NpO2

+ + H2O = 

FeOHNpO2OH + H+

FeOH2

+ + NpO2

+ + H2O = 

FeOH2NpO2OH+ + H+

2FeOH + NpO2

+ = (FeO)2NpO2

- + 2H+

DDL 7.35 

DDL -9.17 DDL -2.54 

DDL 5.21 

DDL-10.39 DDL -2.54 

DDL 5.21 

DDL -5.96 

6.0x10-6 0 0.1 CCM, 

DDL, 

TLM 

Tu

Na

Sak

Goethite FeOH + H+ = FeOH2

+

FeOH = FeO- + H+ 

FeOH + NpO2

+ = FeOHNpO2+ 

FeOH + NpO2

+ +H2O = 

FeONpO2OH- + 2H+

Average 7.29

Average -

8.93 

Average 4.32

Average -

11.26 

6.0x10-6, 

1.2x10-12

0 0.1 NEM Zava

(2004

(199

an

Hematite 

(α-Fe2O3) 

FeOH + H+ = FeOH2

+

FeOH = FeO- + H+

FeOH + Na+ = FeO---Na+ + H+

FeOH + H+ + ClO4

- = FeOH2 +--ClO4- 

FeOH + NpO2

+ = FeO-NpO2

++ H+ 

FeOH + NpO2

+ + H+ + CO32- = 

FeO-NpO2(HCO3)2-

8.4 

-10.4 -8.55 

10.33 

-2.09 

24.62 

1.2x10-7- 

1.3x10-6

0, 

10-3.5, 

10-2.7

0.005, 

0.01, 0.05, 

0.1 

TLM Kohl

Koh
63 

Table 2.13 (continued). Surface Complexation Modeling Studies of Np(V) Sorption to Pure Min

Reference 

Mineral 

Phase Surface Reaction Log K 

Np(V) 

(M)

PCO2

(Atm)

I 

(M) EDL 

S

E

Hematite FeOH + H+ = FeOH2+

FeOH = FeO- + H+

FeOH + NpO2+ = FeOHNpO2+ 

2FeOH + NpO2+ = (FeO)2NpO2- + 2H+

7.35 

-9.17 

12.07 

-6.79 

6.0x10-6, 

1.2x10-12

0 0.1 DDL 

Sa

N

Sa

a

Hydrargilite 

(α-Al(OH)3) 

AlOH + H+ = AlOH2+ 

AlOH = AlO- + H+

AlOH + NpO2+ = AlONpO2 + H+

5.8 9.5 

-3.4 

≈10-13 0, 

10-4 M 

HCO3-

0.1, 

0.01 

NEM D

(1

Hydrargilite AlOH + H+ = AlOH2+ 

AlOH = AlO- + H+

AlOH + NpO2+ = AlONpO2 + H+

5.7 

-9.4 -3.6 

≈10-13 0, 

10-4 M 

HCO3-

0.1 NEM D

(1

Hydrargilite AlOH + H+ = AlOH2+

AlOH = AlO- + H+

AlOH + NpO2+ = AlONpO2 + H+

8.33 

-9.73 -2.81 

5.5x10-14 

– 

1.0x10-13 

0, 10-3, 

10-2

0.1* DDL 

Sa

D

(

Kaolinite 

(Si/Al = 1.0) 

AlOH + H+ = AlOH2+

AlOH = AlO- + H+

SiOH = SiO- + H+

AlOH + NpO2+ = AlONpO2 + H+ 

SiOH + NpO2+ = SiOHNpO2+

8.33 

-9.72 -7.20 -4.04 

4.09 

7.0x10-6 0 0.1 DDL 

Sa
64 

Table 2.13 (continued). Surface Complexation Modeling Studies of Np(V) Sorption to Pure Min

Reference 

Mineral 

Phase Surface Reaction Log K 

Np(V) 

(M)

PCO2

(Atm)

I 

(M) EDL 

S

Lepidocrocite 

(γ-FeOOH) 

FeOH + H+ = FeOH2

+

FeOH = FeO- + H+ 

FeOH + NpO2

+ = FeONpO2 + H+

FeOH + NpO2

+ = FeOHNpO2+ 

FeOH + NpO2

+ +H2O = FeONpO2OH- + 

2H+

FeOH + NpO2

+ + H2O = FeOHNpO2OH

+ H+

FeOH2

+ + NpO2

+ + H2O = 

FeOH2NpO2OH+ + H+

2FeOH + NpO2

+ = (FeO)2NpO2

- + 2H+

DDL 7.35 

DDL -9.17 

DDL -3.53 

DDL 4.51 

DDL-11.51 DDL -3.53 

DDL 4.51 

DDL -7.52 

6.0x10-6 <10-3.5 0.1 CCM, 

DDL, 

TLM 

N

S

Lepidocrocite FeOH + H+ = FeOH2

+

FeOH = FeO- + H+

FeOH + NpO2

+ = FeOH-NpO2+ 

AlOH + NpO2

+ +H2O = AlONpO2OH- + 

2H+

7.35 

-9.17 

4.25 

-12.83 

6.0x10-6 <10-3.5 0.1 DDL 

N

S

Magnetite 

(Fe3O4) 

FeOH + NpO2

+ = FeONpO2 + H+

FeOH + H+ + CO32- = FeOH2CO3

-

FeOH + 2H+ + CO32- = FeOH2HCO3

1.7 

20.3 24.9 

4x10-7- 

6x10-6

10-3.5 0.1 CCM 

(

Magnetite FeOH + H+ = FeOH2

+

FeOH = FeO- + H+ 

FeOH + NpO2

+ = FeONpO2 + H+

FeOH + NpO2

+ = FeOHNpO2+ 

FeOH + NpO2

+ +H2O = FeONpO2OH- + 

2H+

FeOH + NpO2

+ + H2O = FeOHNpO2OH

+ H+

FeOH2

+ + NpO2

+ + H2O = 

FeOH2NpO2OH+ + H+

2FeOH + NpO2

+ = (FeO)2NpO2

- + 2H+

DDL 6.72 

DDL -6.37 

DDL -3.42 

DDL 3.55 

DDL -10.53 

DDL -3.42 

DDL 3.55 

DDL -4.82 

1.0x10-6 <10-3.5 0.1 CCM, 

DDL, 

TLM 

N

S
65 

65 

Table 2.13 (continued). Surface Complexation Modeling Studies of Np(V) Sorption to Pure Min

Reference 

Mineral 

Phase Surface Reaction Log K 

Np(V) 

(M)

PCO2

(Atm)

I 

(M) EDL 

So

Exp

Magnetite FeOH + H+ = FeOH2+

FeOH = FeO- + H+ 

FeOH + NpO2+ +2H2O = 

FeONpO2(OH)22- + 3H+

2FeOH + NpO2+ = (FeO)2NpO2- + 2H+

6.72 

-6.37 

-18.57 

-5.11 

1.0x10-6, 

1.0x10-12

0 0.1 DDL Wa

an

Sa

N

Sa

Montmorillonite Na-mont. + NpO2+ = NpO2-mont. + Na+ AlOH + NpO2+ = AlONpO2 + H+ 

AlOH + NpO2+ + H2O = AlOHNpO2OH- 

+ 2H+

1.1 

-2.0 

-12 

<10-13 10-3.5 0.01 

and 

0.1 

IX 

and 

NEM 

B

B

Montmorillonite AlOH + H+ = AlOH2+ AlOH = AlO- + H+ 

AlOH + NpO2+ = AlONpO2 + H+ 

AlOH + NpO2+ + H2O = AlOHNpO2OH- 

+ 2H+

8.33 

-9.73 

-2.52 to -4.86 

-12.44 to -14.29 

9x10-7 0, 

10-3.5

0.1 NEM Z

Bru

T

Tur

Montmorillonite 

(Si/Al = 1.2) 

AlOH + H+ = AlOH2+

AlOH = AlO- + H+

SiOH = SiO- + H+

AlOH + NpO2+ +H2O = AlONpO2OH- + 

2H+ 

SiOH + NpO2+ +H2O = SiOHNpO2+ + 

H+

8.33 

-9.73 -7.20 

-13.72 

3.04 

1x10-6 0 0.1 DDL Wa

an

Sa

B

Montmorillonite AlOH + H+ = AlOH2+ AlOH = AlO- + H+

SiOH = SiO- + H+

AlOH + NpO2+ +H2O = AlONpO2OH- + 

2H+ 

SiOH + NpO2+ = SiOHNpO2+

8.33 

-9.73 -7.20 

-13.79 

4.05 

8.79x10-7 0, 

10-3.5

0.1 DDL T

(19

a

Sa
66 

66 

Table 2.13 (continued). Surface Complexation Modeling Studies of Np(V) Sorption to Pure Min

Quartz 

(SiO2) 

SiOH = SiO- + H+

SiOH + NpO2+ = SiONpO2 + H+ 

SiOH + NpO2+ + H2O = SiOHNpO2OH- 

+ 2H+

-7.20 

-3.56 to -5.31 

-11.59 to -12.90 

1x10-14, 

1x10-7, 

1x10-6

0, 

10-3.5

0.1 NEM Z

Bru

T

B

R

Quartz SiOH + H+ = SiOH2+

SiOH = SiO- + H+ 

SiOH + Na+ = SiO---Na+ + H+

SiOH + H+ + ClO4- = SiOH2 +--ClO4- 

SiOH + NpO2+ = SiONpO2++ H+

-2.3 -6.8 -7.1 -6.4 

-6.93 

1.2x10-7 0 0.1 TLM Koh

a

E

Koh
67 

67 

of sorbed neptunyl hydroxo or carbonato 

complexes could not be ruled out. Recently, 

Arai et al. (2007) gave evidence for the presence 

of bidentate mononuclear inner-sphere neptunyl 

biscarbonato complexation and outer-sphere 

neptunyl triscarbonato complexation at the 

hematite surface. Interestingly, the study 

conducted by the Arai group found that sorption 

of free neptunyl was not observed under the 

study conditions, specifically atmospheric PCO2, 

pH 7.0, 8.2 and 8.8, 4 – 5 μM Np(V), 0.1 M 

NaClO4 and 0.3 g L-1 hematite. The resulting 

reactions determined by spectroscopy as well as 

some chemically feasible reactions are presented 

in Table 2.14. 

The total site concentrations were calculated 

using an average site density of 2.31 sites nm-2

(3.84 μmol m-2) recommended by Davis and 

Kent (1990) and BET surface area 

measurements determined by Davis et al. (2004) 

for unmodified NABS (5.15 m2

 g-1) and by 

Sanpawanitchakit (2002) for treated NABS 

(4.47 m2

 g-1). Site concentrations were only 

varied as described above and were otherwise 

considered nonadjustable. Initial model 

simulations were conducted using a single site 

type. The use of a two-site type model is 

consistent with the approach of Dzombak and 

Morel (1990) for modeling cation binding to 

hydrous iron oxides. Slight improvements in the 

fits were obtained by adding a second site type 

having a higher sorption affinity. Therefore, a 

two-site type model using 99% weak affinity 

sites and 1% strong sites was chosen to model 

Np(V) sorption. This site type distribution was 

selected based on best fits to preliminary model 

simulations using a range of site distributions 

(0.01-10% strong sites). 

Sanpawanitchakit (2002) obtained excellent 

model simulations of U(VI) sorption to treated 

NABS under atmospheric and 2% PCO2 using a 

two-site type GC SCM with 0.8% strong sites. 

In addition, Davis et al. (2004) used a GC SCM 

with 99.89% weak (YOH), 0.1% strong (SOH) 

and 0.01% very strong (SSOH) site types to 

describe U(VI) sorption to unmodified NABS. 

For this study, a noticeable improvement in 

model fits was not observed during the initial 

model simulations and therefore a third site type 

was excluded from any further model 

calculations. Finally, a maximum of 3 surface 

reactions were considered in order to minimize 

the model fitting parameters. 

To begin, model simulations for 5.0x10-7 M 

Np(V) sorption to treated NABS in AGW-A 

were obtained for individual data sets as a 

Table 2.14 Plausible Np(V) Surface Reactions Considered in Models 

1 ≡XOH + NpO2

+

 ⇄ ≡XOHNpO2

+

 

2 ≡XOH + NpO2

+

 ⇄ ≡XONpO2 + H+ 

3 ≡XOH + NpO2

+

 + H2O ⇄ ≡XONpO2OH- + 2H+ 

4 ≡XOH + NpO2

+ + H2CO3* ⇄ ≡XONpO2CO32- + 3H+

 

5 ≡XOH + NpO2

+ + H2CO3* ⇄ ≡XOHNpO2CO3 - + 2H+

6 ≡XOH + NpO2

+ + H2CO3* ⇄ ≡XONpO2HCO3-

 + 2H+

7 ≡XOH + NpO2

+ + 2H2CO3* ⇄ ≡XONpO2(HCO3)22- + 3H+

 

8 ≡XOH + NpO2

+ + 2H2CO3* ⇄ ≡XOHNpO2(HCO3)2-

 + 2H+ 

9 ≡XOH + NpO2

+ + 2H2CO3* ⇄ ≡XONpO2(CO3)24- + 5H+

 

10 ≡XOH + NpO2

+ + 2H2CO3* ⇄ ≡XOHNpO2(CO3)23- + 4H+

 

11 ≡XOH + NpO2

+

 + H2O + H2CO3

* ⇄ ≡XONpO2OH(CO3)

3- + 4H+

 
68 

function of pH at fixed solid-solution ratios. 

These initial model calculations, though part of a 

range finding exercise, give some valuable 

insight into the process of obtaining an 

acceptable GC model fit. In addition, a single 

site type was used for the initial model fits. A 

summary of the reactions used, optimized log K 

values and WSOS/DF values for the initial 

model simulations are given in Table 2.15. 

Figures 2.28a and b contain the model 

simulation using the following surface reaction, 

≡XOH + NpO2

+

 ↔ ≡XOHNpO2

+

 [2.8] 

 

 with 50 and 125 g L-1, respectively. In these 

simulations constant Np(V) sorption is expected 

below approximately pH 8.0 as a result of the 

pH independence of reaction 2.8, while a steady 

decrease in sorption is shown as aqueous 

neptunyl carbonato complexes begin to 

dominate. The models are unable to fit the 

slight pH dependence at low pH and 

overestimate the reduction in sorption at high 

pH. In contrast, model simulations using the 

following reaction show that proton release 

results in rapid Np(V) uptake (Figures 2.29a and 

b). 

≡XOH + NpO2

+

 ↔ ≡XONpO2 + H+ [2.9] 

Similar to reaction 2.8, a rapid decrease in 

sorption is observed as pH increases past 8.2. 

Of all the single reaction model simulations, 

reactions 2.8 and 2.9 give the best fits, although 

these fits are clearly poor. Reaction 2.9 is one of 

the most commonly used surface reactions to 

simulate Np(V) sorption to pure mineral phases 

(see Table 2.13). This is in part due to the 

spectroscopic evidence supporting the species 

(see Combes et al., 1992). However, it is also 

based on observation, since the strong pH 

dependence of Np(V) sorption seen in many 

experimental systems is well fit using a surface 

reaction that also exhibits strong pH 

dependence. For example, Wang et al. (2001) 

successfully simulated the experimental data 

from the Girvin et al. (1991) study of Np(V) 

sorption to ferrihydrite at 3 different solidTable 2.15 Summary of Adjustable Parameters and Goodness-Of-Fit: One-Site Models 

 (5x10-7 M Np(V) with 50 or 125 g L-1 in AGW-A at atmospheric PCO2) 

Figure #/solid-solution ratio 

Np(V) concentration/solution 

Surface 

Complexes *Log K WSOS/DF 

2.28a/50 g L-1

5x10-7 M/ AGW-A ≡XOHNpO2

+ 2.72 300 

2.28b/125 g L-1

5x10-7 M/ AGW-A ≡XOHNpO2

+ 2.66 197 

2.29a/50 g L-1

5x10-7 M/ AGW-A ≡XONpO2 -4.85 500 

2.29b/125 g L-1

5x10-7 M/ AGW-A ≡XONpO2 -5.09 365 

≡XOHNpO2

+ 2.30a/50 g L 2.25 -1

5x10-7 M/ AGW-A ≡XONpO2 -5.07 

68 

≡XOHNpO2

+ 2.30b/125 g L 2.14 -1

5x10-7 M/ AGW-A ≡XONpO2 -5.31 

53 

≡XOHNpO2

+ 2.51 

≡XONpO2 -5.36 2.31a/50 g L-1

5x10-7 M/ AGW-A ≡XONpO2(HCO3)2

2- -13.4 

14 

≡XOHNpO2

+ 2.42 

≡XONpO2 -5.62 2.31b/125 g L-1

5x10-7 M/ AGW-A ≡XONpO2(HCO3)2

2- -13.7 

13 

*Binding constants for a single site type model
69 

0

10

20

30

40

50

60

70

80

90

100

4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5

pH

Percent Np(V) Sorbed

[Np(V)]T =5.0x10-7 M 

Atmospheric PCO2

AGW-A

≡XOH + NpO2

+ ↔ ≡XOHNpO2+

50 g L-1 Treated NABS 

Model 

Simulation

Experimental 

Data

Figure 2.28a. Model calibration with a 

single surface species (≡XOHNpO2

+

) and 

a single data set; 5x10-7 M Np(V), 50 g L-1

treated NABS in AGW-A at atmospheric 

PCO2. 

0

10

20

30

40

50

60

70

80

90

100

4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0

pH

Percent Np(V) Sorbed

[Np(V)]T =5.0x10-7 M 

Atmospheric PCO2

AGW-A

≡XOH + NpO2

+ ↔ ≡XOHNpO2+

125 g L-1 Treated NABS 

Experimental 

Data

Model 

Simulation

Figure 2.28b. Model calibration with a 

single surface species (≡XOHNpO2

+

) and 

a single data set; 5x10-7 M Np(V), 125 g L1

 treated NABS in AGW-A at 

atmospheric PCO2. 

0

10

20

30

40

50

60

70

80

90

100

4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5

pH

Percent Np(V) Sorbed

[Np(V)]T =5.0x10-7 M 

Atmospheric PCO2

AGW-A

≡XOH + NpO2

+ ↔ ≡XONpO2 + H+

50 g L-1 Treated NABS

 Model Simulation

 Experimental Data

Figure 2.29a Model calibration with a 

single surface species (≡XONpO2) and a 

single data set; 5x10-7 M Np(V), 50 g L-1

treated NABS in AGW-A at atmospheric 

PCO2. 

0

10

20

30

40

50

60

70

80

90

100

4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0

pH

Percent Np(V) Sorbed

Experimental Data

Model Simulation

[Np(V)]T =5.0x10-7 M 

Atmospheric PCO2

AGW-A

≡XOH + NpO2

+ ↔ ≡XONpO2 + H+

125 g L-1 Treated NABS

Figure 2.29b. Model calibration with a 

single surface species (≡XONpO2) and a 

single data set; 5.0x10-7 M Np(V), 125 g L1

 treated NABS in AGW-A at 

atmospheric PCO2.

solution ratios using reaction 2.9 and a DDL 

SCM. The sorption of Np(V) to ferrihydrite 

shows an increase in sorption from 

approximately 0 to 100% between pH 4 and 7 

using 4.7x10-7 M Np(V), 0.89 g L-1, 0.1 M 

NaNO3 and atmospheric PCO2. Similarly, Del 

Nero et al. (1997) used a NEM SCM with 

surface protolysis reactions and reaction 2.9 to 

effectively model 10-13 M Np(V) sorption to 10 

g L-1 hydrargilite in 0.01 and 0.1 M NaClO4

using CO2-free conditions. The variation in 

Np(V) sorption from approximately 10% to 95% 

between pH 6 and 9 was well fit using the 

≡XONpO2 surface complex. The strong pH 

dependence of the sorption data from Girvin et 

al. (1991) and Del Nero et al. (1997) is 

consistent with numerous studies of Np(V) 

sorption to monomineralic phases (e.g. 

Nakayama and Sakamoto, 1991; Niitsu et al., 

1997; Kohler et al. 1999). In contrast, the 

sorption data from this study is only minimally 

pH dependent under the conditions studied. 

Therefore, reaction 2.9 alone cannot successfully 

simulate the sorption data. 
70 

The model fit is greatly improved by combining 

reactions 2.8 and 2.9 and re-optimizing the 

associated log K values (Figures 2.30a and b). 

Compared to the single reaction models, the 

minimal pH dependence of the experimental 

data is better fit by the 2-reaction model below 

approximately pH 7.5. In addition, the fits 

improve substantially in the alkaline pH range. 

However, the 2-reaction model continues to 

inadequately simulate the experimental data 

above approximately pH 7.5 by showing 

decreasing sorption at high pH that is 

inconsistent with the experimental data. 

Addition of a third reaction, based on the 

formation of a ternary neptunyl carbonate 

surface complex, greatly improves the model fit 

to both data sets. Figures 2.31a and b show the 

single data set simulations obtained by adding 

the following reaction, 

≡XOH + NpO2

+ + 2H2CO3*

 ↔ 

≡XONpO2(HCO3)2

2- + 3H+

 [2.10] 

Using the above approach to model multiple 

data sets gives further insight into the models 

ability to simulate Np(V) sorption to treated 

NABS. Figure 2.32a shows the model fit when 

both data sets are modeled simultaneously using 

a single site type. Although the weak pH 

dependence for these data sets is well simulated, 

the model generally overestimates the 125 g L-1

data and slightly underestimates the 50 g L-1

data. In order to obtain a better model fit, a two 

site type model containing 1% strong sites was 

used to produce the model fit in Figure 2.32b. 

The model simulations presented above do not 

explicitly incorporate protolysis reactions for 

surface sites. From the batch salt titration 

results, an approximate value of pHpzc ≈ 7 was 

chosen to obtain approximate acidity constants 

for the surface protolysis reactions, log K- and 

log K+, using the following equation, 

pHpzc = 1

2

[ ] logK+ − logK- [2.11] 

The difference between the log K values (Δlog 

K = 1.4) for the surface protonation and 

deprotonation reactions was chosen based on the 

Turner and Sassmann (1996) acidity constants 

for clay and aluminum oxide surfaces, log K- = -

9.73 and log K+ = 8.33, determined using a DDL 

SCM. Using equation 2.11, the estimated 

acidity constants for protolysis of NABS surface 

sites are as follows: 

≡ XOH←K− ⎯ → ≡ XO− + H+

 Log K- = -7.7 [2.12] 

≡ XOH + H+←K+ → ⎯ ≡ XOH2

+

 Log K+ = 6.3 [2.13] 

Figure 2.32c shows the result of incorporating 

the surface protolysis reactions (reactions 2.12 

and 2.13) into the best fit model (Figure 2.32b) 

for the AGW-A data sets using 5.0x10-7 M 

Np(V) with 50 and 125 g L-1 treated NABS at 

atmospheric PCO2 (WSOS/DF = 126). At low 

pH, the model is unable to simulate the weak pH 

dependence. The sorption data is better fit as the 

pH increases, but the sorption is overestimated 

for the 125 g L-1 data set and underestimated for 

the 50 g L-1 data set at pH greater than 

approximately 6.5. The initial model simulation 

in Figure 2.32b follows a similar trend but fits 

the experimental data better (WSOS/DF =31). 

Incorporation of the surface protolysis reactions 

decreases the pH at which the maximum 

sorption is simulated. For example, in Figure 

2.32b maximum sorption is calculated to occur 

at approximately pH 8.2 while the maximum 

Np(V) uptake occurs at approximately pH 7.2 

when the surface protolysis reactions are 

included. 

Ultimately the true test of the usefulness of the 

model may be its transferability to experimental 

data at other experimental conditions. Ideally, 

the model parameters are transferable to 

conditions and substrates other than those used 

to develop the initial model. In general, the 

transferability of GC models is expected to be 

limited to conditions within the model 

calibration range (Payne et al., 2004). The 

model simulations presented in Figure 2.33a are 

based on the best fit model without further 

optimization, but applied to the data sets with 

5.0x10-6 M Np(V) under otherwise similar 

conditions. The model appropriately captures 

the increase in Np(V) sorption for both solid-
71 

0

10

20

30

40

50

60

70

80

90

100

4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0

pH

Percent Np(V) Sorbed

[Np(V)]T =5.0x10-7 M 

Atmospheric PCO2

AGW-A

≡XOH + NpO2

+ ↔ ≡XOHNpO2+ 

≡XOH + NpO2

+ ↔ ≡XONpO2 +H+

50 g L-1 Treated NABS

 Model Simulation

 Experimental Data

Figure 2.30a. Model calibration with two 

surface species (≡XOHNpO2

+

 and 

≡XONpO2) and a single data set; 5x10-7 M 

Np(V), 50 g L-1 treated NABS in AGW-A 

at atmospheric PCO2. 

0

10

20

30

40

50

60

70

80

90

100

4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0

pH

Percent Np(V) Sorbed

[Np(V)]T =5.0x10-7 M 

Atmospheric PCO2

AGW-A

≡XOH + NpO2

+ ↔ ≡XOHNpO2+ 

≡XOH + NpO2

+ ↔ ≡XONpO2 +H+

125 g L-1 Treated NABS 

 Experimental Data

 Model Simulation

Figure 2.30b. Model calibration with two 

surface species (≡XOHNpO2

+

 and 

≡XONpO2) and a single data set; 5x10-7 M 

Np(V), 125 g L-1 treated NABS in AGWA at atmospheric PCO2. 

0

10

20

30

40

50

60

70

80

90

100

4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0

pH

Percent Np(V) Sorbed

 Model Simulation

 Experimental Data

[Np(V)]T =5.0x10-7 M 

Atmospheric PCO2: AGW-A

≡XOH + NpO2

+ ↔ ≡XOHNpO2+ 

≡XOH + NpO2

+ ↔ ≡XONpO2 

≡XOH + NpO2

+ +H2CO3* ↔ ≡XONpO2(HCO3)22-+ 3H+

125 g L-1 Treated NABS 

Figure 2.31a. Model calibration including 

a neptunyl carbonato ternary surface 

complex (≡XONpO2(HCO3)2

2-) and a 

single data set; 5x10-7 M Np(V), 50 g L-1

treated NABS in AGW-A at atmospheric 

PCO2. 

0

10

20

30

40

50

60

70

80

90

100

4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0

pH

Percent Np(V) SorbedExperimental Data

Model Simulation

[Np(V)]T =5.0x10-7 M 

Atmospheric PCO2: AGW-A

≡XOH + NpO2

+ ↔ ≡XOHNpO2+ 

≡XOH + NpO2

+ ↔ ≡XONpO2 

≡XOH + NpO2

+ +H2CO3* ↔ ≡XONpO2(HCO3)22-+ 3H+

125 g L-1 Treated NABS 

Figure 2.31b. Model calibration 

including a neptunyl carbonato ternary 

surface complex (≡XONpO2(HCO3)2

2-) 

and a single data set; 5x10-7 M Np(V), 125 

g L-1 treated NABS in AGW-A at 

atmospheric PCO2. 

solution ratios but the WSOS/DF = 76 indicates 

a less than adequate fit. Optimizing the log K 

values results in a slightly more acceptable fit 

considering the relatively limited amount of 

experimental data collected at 5.0x10-6 M 

(Figure 2.33b). However, the slight reduction in 

sorption at higher Np(V) concentrations 

observed for sorption when pH is varied is not 

well captured by the calibration model. A 

summary of the surface species employed, log K 

values and WSOS/DF values for the model 

simulations in Figures 2.32 and 2.33 are given in 

Table 2.16. 

The next step to examining model transferability 

is to apply the best fit model (Figure 2.32b) to 

the isotherm experimental data sets using treated 

NABS. This is tested using the model to 

simulate Np(V) sorption to 125 g L-1 treated 

NABS using a maximum range of Np(V) from 

2.0x10-7 M to 3.0x10-6 M, in AGW-3 (pH 8.0 ±

0.1) at atmospheric PCO2 and in AGW-6 (pH 6.8 

± 0.1) at 8% PCO2. Figure 2.34a shows the 
72 

0

10

20

30

40

50

60

70

80

90

100

4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0

pH

Percent Np(V) Sorbed

[Np(V)]T =5.0x10-7 M 

Atmospheric PCO2: AGW-A

 Treated NABS 

125 g L-1 

Experimental Data

125 g L-1 Model 

Simulation

50 g L-1 Model 

Simulation

50 g L-1 

Experimental Data

Figure 2.32a. Model calibration with both AGW-A data sets, 5x10-7 M Np(V), 50 and 125 g 

L-1 treated NABS at atmospheric PCO2, using a 1-site type model.

0

10

20

30

40

50

60

70

80

90

100

4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0

pH

Percent Np(V) Sorbed

[Np(V)]T =5.0x10-7 M 

Atmospheric PCO2 

AGW-A

 Treated NABS 

125 g L-1 

Experimental Data

125 g L-1 Model 

Simulation

50 g L-1 Model 

Simulation

50 g L-1 

Experimental Data

Figure 2.32b. Model calibration with both AGW-A data sets, 5x10-7 M Np(V), 50 and 125 g 

L-1 treated NABS at atmospheric PCO2, 2-site type model with 99% weak sites and 1% 

strong sites. 

0

10

20

30

40

50

60

70

80

90

100

4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0

pH

Percent Np(V) Sorbed

125 g L-1 

Experimental Data

125 g L-1 Model 

Simulation

50 g L-1 Model

Simulation

50 g L-1 

Experimental Data

[Np(V)]T =5.0x10-7 M 

Atmospheric PCO2 

AGW-A

 Treated NABS 

Figure 2.32c. Model calibration with both AGW-A data sets; specifically 5x10-7 M Np(V), 

50 and 125 g L-1 treated NABS at atmospheric PCO2 with incorporation of surface 

protolysis reactions. Surface species include ≡XOHNpO2

+

, ≡XONpO2 and 

≡XONpO2(HCO3)2

2-. 
73 

0

10

20

30

40

50

60

70

80

90

100

4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0

pH

Percent Np(V) Sorbed

[Np(V)]T =5.0x10-6 M 

Atmospheric PCO2 

AGW-A

 Treated NABS 125 g L-1 Model 

Simulation

125 g L-1 

Experimental Data

50 g L-1 Model 

Simulation

50 g L-1 

Experimental Data

Figure 2.33a. Test of model performance for the higher Np(V) concentration AGW-A data 

sets without additional parameter optimization. 

0

10

20

30

40

50

60

70

80

90

100

4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0

pH

Percent Np(V) Sorbed

[Np(V)]T =5.0x10-6 M 

Atmospheric PCO2: AGW-A

 Treated NABS 

125 g L-1 Model 

Simulation

125 g L-1 

Experimental Data

50 g L-1 Model 

Simulation

50 g L-1 

Experimental Data

Figure 2.33b. Test of model performance for the higher Np(V) concentration AGW-A data 

sets with additional log K optimization.
74 

Table 2.16 Summary of Adjustable Parameters and Goodness-Of-Fit: Two-Site Models 

(5x10-7 M Np(V) with 50 or 125 g L-1 in AGW-A at atmospheric PCO2)

Figure #/solid-solution ratio 

Np(V) concentration /solution Surface Complexes Log Ks

Log 

Kw

WSOS/DF 

Log K 

2.47 

-5.45 

2.32a/50 and 125 g L-1

5.0x10-7 M/ AGW-A 

≡XOHNpO2

+

≡XONpO2

≡XONpO2(HCO3)2

2-

-13.6 

28 

2.32b/50 and 125 g L-1

5.0x10-7 M / AGW-A 

≡XOHNpO2

+

≡XONpO2

≡XONpO2(HCO3)2

2-

4.18 

-3.64 

-11.5 

2.17 

-5.92 

-14.4 

31 

4.76 1.90 

-3.06 -5.92 2.32c/50 and 125 g L-1

5.0x10-7 M / AGW-A 

≡XOHNpO2

+

≡XONpO2

≡XONpO2(HCO3)2

2- -9.88 -14.2 

126 

4.18 2.17 

-3.64 -5.92 2.33a/50 and 125 g L-1

5.0x10-6 M / AGW-A 

≡XOHNpO2

+

≡XONpO2

≡XONpO2(HCO3)2

2- -11.5 -14.4 

76 

3.85 2.18 

-3.51 -5.92 2.33b/50 and 125 g L-1

5.0x10-6 M / AGW-A 

≡XOHNpO2

+

≡XONpO2

≡XONpO2(HCO3)2

2- -11.9 -14.4 

66 

initial model simulations without parameter 

optimization from the best fit model. In Figure 

2.34a the sorption is overestimated for both 

isotherms and the goodness-of-fit is poor 

(WSOS/DF = 88). Optimization of the log K 

values for each surface reaction results in a very 

good fit to both the atmospheric and 8% PCO2

data (Figure 2.34b). Sorption pH edge data 

required the formation of 3 surface species, 

specifically ≡XONpO2, ≡XOHNpO2

+ and 

≡XONpO2(HCO3)2

2-, in order to obtain 

reasonable model fits over the entire pH range. 

To illustrate this point, Figure 2.34c shows the 

calculated sum of the surface speciation and the 

surface species distribution of 5.0x10-7 M Np(V) 

sorption to 125 g L-1 treated NABS at 

atmospheric PCO2 given in Figure 2.31b. This 

surface species distribution shows that at pH 8.0 

and atmospheric PCO2, the contribution of the 

≡XOHNpO2

+

 species (≡SOHNpO2

+ and 

≡YOHNpO2

+

) are becoming less important 

compared to the neutral species, ≡XONpO2. 

Figure 2.34d shows the re-calibrated 

experimental data with additional log K 

optimization following removal of the 

≡XOHNpO2

+

 species. Removal of ≡XOHNpO2

+ 

species results in a similar fit to the experimental 

data as the 3 surface reaction fit shown in Figure 

2.34b. Therefore, the number of fitting 

parameters can be reduced when modeling 

Np(V) sorption at fixed pH while varying other 

conditions such as Np(V) concentration, solidsolution ratio and PCO2. The resulting model 

simulation fits the experimental data at both 

atmospheric and 8% PCO2 very well (WSOS/DF 

= 8). Table 2.17 summarizes the log K values, 

surface species and goodness-of-fit values for 

simulations of all isotherm data. 

The same steps used to test the transferability of 

the model to Np(V) sorption isotherms using 

treated NABS were followed to explore model 

performance in describing Np(V) sorption to the 

unmodified sediment at atmospheric and 8% 

PCO2 (WSOS/DF = 54). The primary change to 

the initial model was an increase in surface site 

concentration due to the higher BET surface area 

of unmodified NABS. Figure 2.35a shows the 

model fit without additional parameter 

optimization beyond the increase in site 

concentration. The model overestimates the 
75 

-9.3

-9.1

-8.8

-8.6

-8.3

-8.1

-7.8

-7.0 -6.8 -6.5 -6.3 -6.0 -5.8

Log Npsoln (mol L-1)

Log Γ (mol g-1)

125 g L-1 Treated

NABS

 [Np]T = 2.0x10-7 

to 3.0x10-6 M

Atmospheric PCO2: AGW-3: pH 8.0 ± 0.1

8% PCO2: AGW-6 pH 6.8 ± 0.1 

8% PCO2 

Experimental Data

Model Simulation

Atmospheric PCO2 

Experimental Data

Model Simulation

Figure 2.34a. Test of model performance 

to Np(V) sorption isotherm data sets 

without additional parameter 

optimization using the best fit model 

(Figure 2.32b). Experimental data 

includes Np(V) sorption isotherms with a 

range of total Np(V) concentration from 

2x10-7 M to 3x10-6 M, 125 g L-1 treated 

NABS, in AGW-3 (pH 8) at atmospheric 

PCO2 or in AGW-6 (pH 6.8) at 8% PCO2. 

-9.4

-9.2

-9.0

-8.8

-8.6

-8.4

-8.2

-8.0

-7.8

-7.0 -6.8 -6.5 -6.3 -6.0 -5.8

Log Npsoln (mol L-1)

Log Γ (mol g-1)

8% PCO2 

Experimental Data

Model Simulation

Atmospheric PCO2 

Experimental Data

Model Simulation

Atmospheric PCO2: AGW-3: pH 8.0 ± 0.1

8% PCO2: AGW-6 pH 6.8 ± 0.1 

125 g L-1 Treated

NABS

 [Np]T = 2.0x10-7 

to 3.0x10-6 M

Figure 2.34b. Test of model performance 

to Np(V) sorption isotherm data sets with 

additional log K optimization. 

Experimental data includes Np(V) 

sorption isotherms with a range of total 

Np(V) concentration from 2x10-7 M to 

3x10-6 M, 125 g L-1 treated NABS, in 

AGW-3 (pH 8) at atmospheric PCO2 or in 

AGW-6 (pH 6.8) at 8% PCO2. 

0

10

20

30

40

50

4.7 5.2 5.7 6.2 6.7 7.2 7.7 8.2 8.7

pH

Percent Np(V) Sorbed

[Np(V)]T =5.0x10-7 M 

Atmospheric PCO2: AGW-A

125 g L-1 Treated NABS 

≡YONpO2

≡YOHNpO2

+

≡YONpO2(HCO3)2

2-

≡SOHNpO2

+

 ≡SONpO2

≡SONpO2(HCO3)2

2-

Figure 2.34c. Np(V) surface species 

distribution for the model simulation 

presented in Figure 2.31b with the 5x10-7

M Np(V), 125 g L-1 treated NABS in 

AGW-A at atmospheric PCO2 data set. 

-9.4

-9.2

-9.0

-8.8

-8.6

-8.4

-8.2

-8.0

-7.8

-7.0 -6.8 -6.5 -6.3 -6.0 -5.8

Log Npsoln (mol L-1)

Log Γ (mol g-1)

125 g L-1 Treated

NABS

 [Np]T = 2.0x10-7

to 3.0x10-6 M

Atmospheric PCO2: AGW-3: pH 8.0 ± 0.1

8% PCO2: AGW-6 pH 6.8 ± 0.1 

8% PCO2 

Experimental Data

Model Simulation

Atmospheric PCO2 

Experimental Data

Model Simulation

Figure 2.34d. Model calibration for the 

Np(V) sorption isotherms using 

≡XONpO2 and ≡XONpO2(HCO3)2

2- with 

a range of total Np(V) concentrations 

from 2x10-7 M to 3x10-6 M, 125 g L-1

treated NABS, in AGW-3 (pH 8) at 

atmospheric PCO2 or in AGW-6 (pH 6.8) 

at 8% PCO2.
76 

Table 2.17 Summary of Adjustable Parameters and Goodness-Of-Fit: Isotherm Data 

(50 or 125 g L-1 in AGW-3 at atmospheric PCO2 and AGW-6 at 8% PCO2)

Figure #/solid-solution ratio 

solution 

Surface 

Complexes 

Log 

Ks

Log 

Kw

WSOS/DF 

≡XOHNpO2

+ 4.18 2.17 

≡XONpO2 -3.64 -5.92 

2.34a/125 g L-1 treated/ 

AGW-3 (atm PCO2) and 

AGW-6 (8% PCO2) ≡XONpO2(HCO3)2

2- -11.5 -14.4 

88 

≡XOHNpO2

+ 3.90 2.17 

≡XONpO2 -4.88 -5.98 

2.34b/125 g L-1 treated/ 

AGW-3 (atm PCO2) and 

AGW-6 (8% PCO2) ≡XONpO2(HCO3)2

2- -12.0 -14.4 

8.1 

≡XONpO2 2.34d/125 g L -3.53 -5.92 -1 treated/ 

AGW-3 (atm PCO2) and 

AGW-6 (8% PCO2) ≡XONpO2(HCO3)2

2- -11.1 -14.4 

7.6 

≡XOHNpO2

+ 4.18 2.17 

≡XONpO2 -3.64 -5.92 

2.35a/125 and 500 g L-1

unmodified /AGW-3 (atm 

PCO2), AGW-6 (8% PCO2) ≡XONpO2(HCO3)2

2- -11.5 -14.4 

54 

≡XOHNpO2

+ 4.03 2.17 

≡XONpO2 -5.59 -5.98 

2.35b/125 and 500 g L-1/ 

unmodified /AGW-3 (atm 

PCO2), AGW-6 (8% PCO2) ≡XONpO2(HCO3)2

2- -11.7 -14.4 

9.2 

≡XONpO2 2.35c/125 and 500 g L -3.48 -5.98 -1/ 

unmodified /AGW-3 (atm 

PCO2), AGW-6 (8% PCO2) ≡XONpO2(HCO3)2

2- -11.0 -14.2 

13 

sorption at atmospheric PCO2 using both 125 

and 500 g L-1 but gives a good fit to the 8% 

PCO2 data. The un-optimized model 

simulations more closely represent untreated 

NABS experimental data at fixed pH and 

variable Np(V) concentration than the model fits 

to treated NABS data (see Figure 2.34a). Reoptimization of log K values gives a very good 

model fit to the data (WSOS/DF = 9) (Figure 

2.35b). Figure 2.35c shows that an acceptable 

fit to the experiment data (WSOS/DF = 13) is 

also obtained by removing the ≡XOHNpO2

+

followed by log K re-optimization. These 

results confirm that the initial model developed 

for pH sorption edges needs to be adjusted by 

either log K value optimization and/or 

modification of the surface speciation to 

successfully fit experimental isotherm data for 

Np(V) sorption to unmodified NABS. 

Figure 2.36a shows that the original best-fit 

model (Figure 2.32b) inadequately models pH 

sorption edge data sets at higher ionic strength 

(0.1 M) using 50, 125 and 500 g L-1 treated 

NABS even after log K re-optimization 

(WSOS/DF = 95). FITEQL did not converge on 

a solution when data above approximately pH 10 

were included, likely due to the increase in 

sorption at high pH that the original 3-reaction 

model, calibrated between pH 4.5 and 9.0, is 

unable to capture. The ternary NpO2

+

-carbonate 

surface complex initially postulated is relatively 

weak, and therefore as pH increases the model 

simulation deviates significantly from the 

experimental data. The original 3-reaction 

model simulates very weak pH dependence over 

the entire pH range studied. Therefore, the 

slightly stronger pH dependence of Np(V) 

sorption to treated NABS in 0.1 M NaClO4 is 

inadequately described by the original model 

developed using AGW-A. It is desirable to 

obtain an acceptable model fit to the 500 g L-1

treated NABS data in 0.1 M NaClO4 at 

atmospheric PCO2, since similar conditions are 

employed for the Np(V)-HA sorption 

experiments discussed below. For simplicity as 

well as consistency with the HA sorption models 

(Moran, 2007), a single site type model was 

employed to describe Np(V) sorption in Figure 

2.36b. Substantial improvements in the model 
77 

-9.3

-9.1

-8.8

-8.6

-8.3

-8.1

-7.8

-7.0 -6.8 -6.5 -6.3 -6.0 -5.8 -5.5

Log Npsoln (mol L-1)

Log Γ (mol g-1)

Unmodified NABS

Atmospheric PCO2: 

AGW-3: pH 8.0 ± 0.1

8% PCO2: AGW-6: pH 

6.8 ± 0.1 

8% PCO2

Model Experimental

 125 g L-1

Atmospheric PCO2 

Model Experimental

 125 g L-1

 500 g L-1

Figure 2.35a. Test of model performance 

to Np(V) sorption isotherm data sets 

without additional parameter 

optimization. Experimental data includes 

Np(V) sorption isotherms with a range of 

total Np(V) concentration from 3x10-7 M 

to 5x10-6 M, 125 and 500 g L-1 unmodified 

NABS, in AGW-3 (pH 8) at atmospheric 

PCO2 and 125 g L-1 unmodified NABS in 

AGW-6 (pH 6.8) at 8% PCO2. 

-9.3

-9.1

-8.8

-8.6

-8.3

-8.1

-7.8

-7.0 -6.8 -6.5 -6.3 -6.0 -5.8 -5.5

Log Npsoln (mol L-1)

Log Γ (mol g-1)

Unmodified NABS

Atmospheric PCO2: 

AGW-3: pH 8.0 ± 0.1

8% PCO2: AGW-6: pH 

6.8 ± 0.1 

8% PCO2

Model Experimental

 125 g L-1

Atmospheric PCO2 

Model Experimental

 125 g L-1

 500 g L-1

Figure 2.35b. Test of model performance 

to Np(V) sorption isotherm data sets with 

additional log K optimization. 

Experimental data includes Np(V) 

sorption isotherms with a range of total 

Np(V) concentration from 3x10-7 M to 

5x10-6 M, 125 and 500 g L-1 unmodified 

NABS, in AGW-3 (pH 8) at atmospheric 

PCO2 and 125 g L-1 unmodified NABS in 

AGW-6 (pH 6.8) at 8% PCO2. 

-9.3

-9.1

-8.8

-8.6

-8.3

-8.1

-7.8

-7.0 -6.8 -6.5 -6.3 -6.0 -5.8 -5.5

Log Npsoln (mol L-1)

Log Γ (mol g-1)

Unmodified NABS

Atmospheric PCO2: 

AGW-3: pH 8.0 ± 0.1

8% PCO2: AGW-6: pH 

6.8 ± 0.1 

8% PCO2

Model Experimental

 125 g L-1

Atmospheric PCO2 

Model Experimental

 125 g L-1

 500 g L-1

Figure 2.35c. Test of model performance 

to Np(V) sorption isotherm data sets with 

additional parameter optimization and 

exclusion of a ≡XOHNpO2

+

 surface 

species. Experimental data includes the 

Np(V) sorption isotherms with a range of 

total Np(V) concentration from 3x10-7 M 

to 5x10-6 M, 125 g L-1 unmodified NABS, 

in AGW-3 (pH 8) at atmospheric PCO2 or 

in AGW-6 (pH 6.8) at 8% PCO2. 

fit are not achieved using the 2-site type model. 

The simulation of the 500 g L-1 data set alone 

produces a better goodness-of-fit than seen for 

the multiple data set simulations as expected, but 

the model highly overestimates Np(V) uptake 

between pH 7.0 and 9.5. Figure 2.36b also 

shows that the model can more closely match 

the shape of the sorption envelope by increasing 

the strength of the ternary neptunyl carbonate 

surface complex. A summary of the surface 

species employed, log K values and WSOS/DF 

values for the model simulations in Figures 

2.36a and b are given in Table 2.18. 

2.3.3 Effect of Humic Acid Presence 

It is clear from the experimental data presented 

in Figures 2.24-2.25 that Np(V) sorption to 

treated NABS in the presence of HA is impacted 

to varying degrees depending on the 

concentration of HA. For example, sorption of 

both 5.0x10-6 M and 5.0x10-7 M Np(V) to 500 g 

L-1 treated NABS is substantially decreased in 

the presence of 100 mg L-1 Suwannee River HA 

whereas addition of 10 mg L-1 HA had an 

insignificant impact on sorption of Np(V) at 
78 

0

10

20

30

40

50

60

70

80

90

100

2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0

pH

Percent Np(V) Sorbed

 [Np(V)]T =5.0x10-7 M

Atmospheric PCO2

0.1 M NaClO4

 125 g L-1

 50 g L-1

 500 g L-1

Figure 2.36a. Test of model performance for the 5x10-7 M Np(V) data sets using 50, 125 

and 500 g L-1 treated NABS in 0.1 M NaClO4 with additional log K optimization. 

0

10

20

30

40

50

60

70

80

90

100

2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0

pH

Percent Np(V) Sorbed

 [Np(V)]T =5.0x10-7 M

Atmospheric PCO2

0.1 M NaClO4

500 g L-1 

Experimental 

Data

500 g L-1 Best 

Fit Model

500 g L-1 

Alternative Model

Figure 2.36b. Test of model performance for the 5x10-7 M Np(V) data set using 500 g L-1

treated NABS in 0.1 M NaClO4 with additional log K optimization and an additional fit 

showing the effect of increasing the importance of the ternary neptunyl carbonate surface 

species. 
79 

Table 2.18 Summary of Adjustable Parameters and Goodness-Of-Fit: NaClO4 Solution 

(5x10-7 M Np(V) with 125 or 500 g L-1 in 0.1M NaClO4 at atmospheric PCO2) 

Figure #/solid-solution ratio 

Np(V) concentration/solution Surface Complexes Log 

Ks

Log 

Kw

WSOS/DF 

4.18 2.17 

-3.64 -5.92 2.36a/50, 125 and 500 g L-1 

5.0x10-7 M / 0.1 M NaClO4

≡XOHNpO2

+

≡XONpO2

≡XONpO2(HCO3)2

2-

-11.5 -14.4 

95.3 

Log K 

2.36b/ 500 g L-1

5.0x10-7 M / 0.1 M NaClO4

≡XOHNpO2

+

≡XONpO2

≡XONpO2(HCO3)2

2-

1.62 

-4.4 

-13.6

55 

both Np(V) concentrations studied. The likely 

processes that decrease Np(V) sorption in the 

presence of 100 mg L-1 HA include 

complexation of Np(V) with HA and sorption of 

HA to the NABS surface. In all likelihood these 

processes are acting in tandem and therefore any 

SCM must take into account both aqueous 

complexation with HA and surface 

complexation in order to successfully simulate 

the data. 

To begin, model simulations of Np(V) sorption 

to treated NABS in the presence of HA are 

obtained by combining the GC SCM discussed 

above with the discrete ligand modeling 

approach for Np(V)-HA complexation (Moran, 

2007). The polyfunctionality and heterogeneous 

nature of HA is captured using a suite of 5 

hypothetical ligands with monoprotic acidic 

functional groups having pKa values of 2, 4, 6, 8 

and 10. Table 2.19 shows the concentration of 

each ligand determined by fitting the discrete 

ligand model to potentiometric titration data of 

Suwannee River HA at 0.004 and 0.04 M 

NaClO4 (Moran, 2007). The discrete ligand 

approach is expanded upon in order to describe 

both HA complexation with NpO2

+

 as well as 

surface complexation of HA with treated NABS. 

The hypothetical ligands (L1-L5) are assumed to 

sorb to NABS surface sites independently. 

The relevant binding constants for the sorption 

of 100 mg L-1 HA to treated NABS at 

atmospheric PCO2 without Np(V) present 

(binary system) are obtained using ligand 

exchange reactions between the NABS surface 

sites and the discrete ligands L1-L4 and the 

“electrostatic-type” reaction for L5 with NABS 

(Figure 2.37a). Although the goodness-of-fit 

value is low (WSOS/DF = 88), likely due to 

scatter in the data, the model captures the trend 

in HA sorption behavior relatively well. The 

sorption of HA to treated NABS in the presence 

of Np(V) (ternary system) is well simulated 

(WSOS/DF = 14) using only the ligand 

exchange reactions between NABS surface sites 

and ligands L1-L4 (Figure 2.37b). This is 

consistent with the obvious trend towards 

minimal HA sorption in the ternary system at 

high pH, which is less apparent in the binary HA 

system (Moran, 2007). The high goodness-of-fit 

was obtained by removing any dependence of 

the HA sorption on the sorption of 5x10-7 M 

Np(V). Much less acceptable model fits are 

obtained from simulations using 10 mg L-1 HA, 

due to higher variability in the sorption data 

resulting from variability in the concentration of 

desorbed organic carbon (TOC) (Moran, 2007). 

Therefore, the systems containing 10 mg L-1 HA 

were not explored in depth. The log K values 

for the HA surface reactions are given in Table 

2.20. 

Combining the models discussed above in an 

additive manner in an attempt to simulate Np(V) 

sorption in the presence of HA results in limited 

success. This ternary system model only allows 

for a small concentration of Np(V) to sorb to 

treated NABS in the presence of 100 mg L-1 HA. 

Rather, in order to produce a somewhat adequate 
80 

Table 2.19 Humic Acid Solution Reactions* 

Reactions HLi concentration

(mmol g-1) 

Log K 

(I = 0) 

HL1 = L1

-

 + H+ 0.608 -2 

HL2 = L2

-

 + H+ 1.65 -4 

HL3 = L3

-

 + H+ 1.46 -6 

HL4 = L4

-

 + H+ 0.578 -8 

HL5 = L5

-

 + H+ 0.678 -10 

Na+ + Li

-

 = NaLi -2.34 

NpO2

+

 + Li

-

 = NpO2Li

0

 4.4 

*Total HA = 4.97 mmol g-1 as determined by model fit to potentiometric 

titration data 

0

10

20

30

40

50

60

70

80

90

100

3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5

pH

Percent HA Sorbed

Binary System

HA = 100 mg L-1

No Np(V) 

500 g L-1

Atmospheric PCO2

0.1 M NaClO4

Figure 2.37a. Model simulation of 100 mg L-1 HA sorption to 500 g L-1 treated NABS at 

atmospheric PCO2 in 0.1 M NaClO4 without Np(V) present. 

0

10

20

30

40

50

60

70

80

90

100

3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 pH

Percent Np(V) Sorbed

Model Simulation

Experimental TOC

Data

HA = 100 mg L-1

[Np(V)]T = 5.0x10-7 M

500 g L-1 Treated NABS

Atmospheric PCO2

0.1 M NaClO4

Figure 2.37b. Model simulation of 100 mg L-1 HA sorption to 500 g L-1 treated NABS at 

atmospheric PCO2 in 0.1 M NaClO4 with 5x10-7 M Np(V) present. 
81 

Table 2.20 Humic Acid Surface Reactions 

 Binary System Ternary System 

 Reactions Log K (I = 0) Log K (I = 0) 

≡XOH + HL1 ↔ ≡XL1 + H2O 20.2 21.5 

≡XOH + HL2 ↔ ≡XL2 + H2O 15.6 13.9 

≡XOH + HL3 ↔ ≡XL3 + H2O 15.0 14.1 

≡XOH + HL4 ↔ ≡XL4 + H2O 14.2 13.7 

≡XOH + HL5 ↔ ≡XOHL5

-

 + H+ 6.29 – 

0

10

20

30

40

50

60

70

80

90

100

3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0

pH

Percent Np(V) Sorbed

[Np(V)]T = 5.0x10-7 M

500 g L-1 Treated NABS

Atmospheric PCO2

0.1 M NaClO4

Experimental Data

Model Fit

Figure 2.38. Model simulation of 5x10-7 M Np(V) to 500 g L-1 treated NABS in the presence 

of 100 mg L-1 HA sorption at atmospheric PCO2 in 0.1 M NaClO4. 

fit to the data (WSOS/DF= 58), the strength of 

all the Np(V) surface complexes must be 

increased substantially in order to compete with 

HA sorption (Figure 2.38). A good model fit is 

limited to below pH 9.5, the highest pH for 

which the ternary system samples were analyzed 

for TOC (Moran, 2007). 

 

2.3.4 Summary of Np(V) Surface 

Complexation Modeling 

Np(V) sorption to 50 and 125 g L-1 treated 

NABS in AGW at atmospheric PCO2 was well 

simulated using a 3-reaction, 2 site-type GC 

SCM. Transferability of this model to other 

system conditions met with limited success. In 

most cases, good model transferability is 

possible when surface reaction log K values are 

re-optimized. For example, the model transfers 

exceptionally well to isotherm experimental data 

at atmospheric and 8% PCO2 once the strengths 

of the surface complexes are adjusted. 

Incorporation of surface protolysis reactions 

results in shifts in the maximum simulated 

sorption to lower pH and additional pH 

dependence, both results being inconsistent with 

the experimental data. 

 
82 
83 

3 DEVELOPMENT OF A SURFACE COMPLEXATION MODEL FOR 

URANIUM(VI) SORPTION ONTO FORTY MILE WASH (NEVADA) 

AQUIFER SEDIMENTS

3.1 Materials and Methods 

3.1.1 Sediments 

Sediments from the saturated zone of the Forty 

Mile Wash aquifer, Nye County, Nevada, were 

collected for this study. Dry and moist sediment 

samples were obtained from Borehole NCEWDP 19PB, ranging in depth from 350 to 587 

feet. Sediments were air dried in the laboratory 

and sieved through a 3 mm mesh. The Forty 

Mile Wash composite sample (FMW-COMP) is 

a mixture of the <3 mm individual depth 

fractions, weighted according to their mass per 

depth ratio. FMW-COMP was well mixed and 

N2-BET specific surface area measurements 

were made on a Tristar Micromeritics analyzer. 

X-ray diffraction results for samples from this 

site indicate that quartz, cristobalite, alkali 

feldspars, plagioclase, zeolites and clays are 

present in the sediments (P. Bertetti, personal 

communication). The total uranium content of 

FMW-COMP, assessed by γ-spectrometry, is 3.6 

ppm. 

3.1.2 Artificial Groundwater Solutions 

An artificial groundwater was developed with a 

composition that reflected an average 

groundwater composition of 37 quarternary 

alluvium aquifer water samples from the area 

near where the aquifer sediment sample was 

collected. The composition of the Forty Mile 

Wash artificial groundwater (referred to as 

FMW-2) was as follows: [Ca2+] = 5.50·10-4M, 

[Mg2+] = 1.00·10-4M, [K+

] = 1.70·10-4M, [Na+] = 

2.50·10-3M, [HCO3

-

] = 2.50·10-3M, [SO4

2-] = 

5.50·10-4M and [ClO4

-

] = 3.70·10-4M. Its calcite 

saturation index is 0.24 at atmospheric levels of 

CO2 (=373ppm). Modifications to the 

groundwater composition were made in order to 

increase alkalinity (by adding NaHCO3), to 

increase the level of pCO2 (by adding NaHCO3

and H2SO4), or to increase the ionic strength to 

approximately 0.1M (by adding 0.1M NaClO4). 

Addition of chloride ion to the artificial 

groundwater was avoided because of 

interference with the method for determing 

dissolved uranium(VI) by kinetic 

phosphorescence analysis (KPA). To avoid the 

addition of chloride anion, perchlorate and/or 

sulfate salts were used to prepare the artificial 

groundwater solutions. Target partial pressures 

of carbon dioxide gas (pCO2) for sorption 

experiments ranged from atmospheric (ca. 400 

ppm) to approximately 2%. For higher ionic 

strength experiments, the concentration of 

NaClO4 was simply increased from 1.7·10-4M in 

FMW-2 to 0.1M (FMW-2I), keeping the other 

components unchanged. 

3.1.3 Sodium Carbonate Extractions 

Extractions were performed on FWM-COMP at 

a solid to liquid ratio of 100 g/L with a solution 

composed of 1.44·10-2M NaHCO3 and 2.8·10-3M 

Na2CO3. The extraction solution had an 

alkalinity of 20 meq/L and a pH of 9.45 under 

atmospheric pCO2 and was previously proved to 

desorb labile U(VI) (Davis et al., 2006; Kohler 

et al., 2004; USNRC, 2003). The extraction time 

was 3 weeks. 

3.1.4 Uranium(VI) Sorption Experiments 

Prior to sorption experiments, aliquots of the 

FMW-COMP sample were equilibrated for 

several days (2-6 days) with artificial 

groundwater solution at a solid to liquid ratio of 

200 g/L. Aliquots of 4 and 8 g of solid were 

weighed into centrifuge tubes and 20 or 40 mL 

of artificial groundwater were added, 

respectively. Centrifuge tubes were rotated 

during the equilibration time. At the end of this 

pretreatment (PT), the pH was measured, and the 

samples were centrifuged at 7000 rpm for 15 

minutes. After PT, 0.45 μm filtered samples 

from each centrifuge tube were taken for 

alkalinity measurements (Gran titrations), for 

U(VI) analysis by kinetic phosphorescence 

analysis (KPA), and for the determination of 
84 

major ions by inductively-coupled argon plasma 

optical emission spectrometry (ICAP-OES). The 

samples were weighed to determine the amount 

of PT solution remaining with the sediment, and 

fresh groundwater was added to the solids prior 

to spiking the samples with 120 to 400 μL of 

U(VI) stock solution (concentration of the stock 

solution was usually about 100 times greater 

than the experimental target concentration). 

The background uranium(VI) adsorbed on the 

sediments (0.253 nanomoles U(VI)/g) was 

determined by the sodium carbonate extraction 

method described in the section above. 

Desorption of a portion of the background 

adsorbed U(VI) occurred during PT and was 

accounted for in mass balance calculations. The 

total U(VI) concentrations added in the 

experiments were 1·10-7, 3·10-7, 1·10-6, 3·10-6, 

and 1·10-5M. For calculations of the 

uranium(VI) Kd values (or percent adsorbed), 

the total U(VI) concentrations in the 

experiments were calculated to be equal to the 

U(VI) added to the experiments plus the 

remaining background adsorbed U(VI) 

associated with the sediments after the PT. The 

inclusion of the remaining background adsorbed 

U(VI) impacted Kd values at the lowest two total 

U(VI) concentrations. At the three higher total 

U(VI) concentrations, the background U(VI) 

was negligible. Samples were rotated for a 

sorption time of 6 days and processed as 

described above. In kinetic experiments, 

supernatant samples were taken after 2 hours 

and up to 4 weeks. 

3.2 Results 

The BET surface area of the FMW-COMP 

sample was measured as 19.15 m2

/g.

3.2.1 Uranium(VI) Sorption Kinetics 

onto Forty-Mile Wash Sediments 

Figure 3.1 shows the results of U(VI) sorption 

onto 200 g/L the FMW-COMP sample as a 

function of time at a total U(VI) concentration of 

10-6M and at low ionic strength of (5.1±0.1) 

mM. Duplicate samples were taken for each 

time point. The errors in percent adsorbed reflect 

analytical uncertainties of 3% in the analysis of 

dissolved U(VI) (and their propagation). The 

points shown as open squares illustrate that 

lowering the amount of solid per sample, while 

keeping the same solid/solution ratio, had no 

measurable effect on the uptake of U(VI) within 

experimental error. In addition, U(VI) uptake by 

sediment samples pretreated three times was the 

same as samples pretreated only once (diamonds 

vs. circles, respectively). U(VI) uptake reached a 

plateau after approximately one week. For all 

other experiments in this system, a standard 

method was adopted with a single pretreatment 

time of 2 days and a sorption period of 6 days. 

3.2.2 Uranium(VI) Sorption onto FortyMile Wash Sediments at Variable pH and 

Alkalinity 

Figure 3.2 shows the results of 6-day sorption 

experiments at low ionic strength (4.9 to 15 

mM) for a solid/liquid ratio of 200 g/L in 

isotherm format. Different sets of symbols 

reflect different total U(VI) concentrations in the 

systems. Each data point reflects a set of 

parameters, consisting of pH, alkalinity and 

pCO2. The latter can be calculated from the 

former two values via the Henry’s Law constant 

and the acidity constants for carbonic acid. Data 

points illustrated by the open symbols reflect 

conditions for a small pH range of 8.53 to 8.56, 

alkalinities between 2.46 and 2.51 meq/L, and 

calculated pCO2 values of 400 to 440 ppm. 

Because of the relatively narrow range in each 

of these parameters, the data set can be 

considered as approximating an adsorption 

isotherm. In double logarithmic representation, 

the data describe a non-linear isotherm with a 

slope of 0.750 (linear regression analysis) and a 

correlation coefficient of 1.000. 

Figures 3.3 and 3.4 illustrate the same data as in 

Figure 3.2 plus additional batch data, while 

representing the data as log Kd values for U(VI) 

sorption or percent U(VI) adsorbed. 

The results for a corresponding series of 

experiments at higher ionic strength (0.1M 

NaClO4) are shown in Figures 3.5 through 3.7. 

Again, the different symbols reflect different 
85 

0 100 200 300 400 500 600 700 800

Time (hours)

0

10

20

30

40

50

Percent U(VI) adsorbed

8.0

8.2

8.4

8.6

8.8

9.0

pH

8g/40 mL; one AGW pretreatment

8g/40 mL; three AGW pretreatments

4g/20 mL; one AGW pretreatment

8g/40 mL; one AGW pretreatment; pH measurement

pH

Figure 3.1. Kinetics of U(VI) adsorption on the composite sample of Forty-Mile Wash 

subsurface sediments (FMW-COMP) in artificial groundwater solution (FMW-2). Circles 

show percent of 1 μM total U(VI) adsorbed; triangles show pH values. 

-7.5 -7.0 -6.5 -6.0 -5.5 -5.0

Log dissolved U(VI) (moles/L)

-10.0

-9.5

-9.0

-8.5

-8.0

-7.5

Log adsorbed U(VI) (moles/g)

-11.0

-10.5

-10.0

-9.5

-9.0

Log adsorbed U(VI) (moles/m2)

Total U(VI) in experiment (moles/L)

1.3 x 10-7

3.4 x 10-7

1.04 x 10-6

3.03 x 10-6

1.0 x 10-5

Artificial Groundwater solutions (I = 0.005-0.015M)

Isotherm for pH 8.55, 440 ppm CO2

Figure 3.2. U(VI) sorption data on the composite sample of Forty Mile Wash subsurface 

sediments (FMW-COMP) in artificial groundwater solutions at low ionic strength. 6-day 

equilibration time was used for U(VI) sorption following one pretreatment of the sediments 

with artificial groundwater solution. The open symbols represent data with nearly 

identical conditions for pH (8.53-8.56), alkalinity (2.46-2.51 meq/L), and pCO2 (400-440 

ppm), meaning that these data approximately represent a traditional isotherm. 
86 

-7.5 -7.0 -6.5 -6.0 -5.5 -5.0

Log dissolved U(VI) (moles/L)

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

Log Kd for U(VI) adsorption (mL/g)

Artificial Groundwater solutions (I = 0.005-0.015M)

pH 8.5; 440 ppm CO2

pH 8.45; 550 ppm CO2

pH 8.35; 740 ppm CO2

pH 7.95; 1260 ppm CO2

pH 8.0; 2300 ppm CO2

pH 7.7; 3400 ppm CO2

pH 7.6; 7000 ppm CO2

pH 7.2; 1.7% CO2

pH 7.1; 1.9% CO2

Figure 3.3. U(VI) sorption data on the composite sample of Forty Mile Wash subsurface 

sediments (FMW-COMP) in artificial groundwater solutions at low ionic strength as a 

function of dissolved U(VI) and PCO2. Data expressed as log Kd for U(VI) sorption versus 

log dissolved U(VI) concentration. 

-7.5 -7.0 -6.5 -6.0 -5.5 -5.0

Log dissolved U(VI) (moles/L)

0.0

10.0

20.0

30.0

40.0

50.0

60.0

Percent U(VI) adsorbed

Artificial Groundwater solutions (I = 0.005-0.015M)

pH 8.5; 440 ppm CO2

pH 8.45; 550 ppm CO2

pH 8.35; 740 ppm CO2

pH 7.95; 1260 ppm CO2

pH 8.0; 2300 ppm CO2

pH 7.7; 3400 ppm CO2

pH 7.6; 7000 ppm CO2

pH 7.2; 1.7% CO2

pH 7.1; 1.9% CO2

Figure 3.4. U(VI) sorption data on the composite sample of Forty Mile Wash subsurface 

sediments (FMW-COMP) in artificial groundwater solutions at low ionic strength as a 

function of dissolved U(VI) and PCO2. Data expressed as percent U(VI) adsorbed versus 

log dissolved U(VI) concentration. 
87 

-7.5 -7.0 -6.5 -6.0 -5.5 -5.0

Log dissolved U(VI) (moles/L)

-10.0

-9.5

-9.0

-8.5

-8.0

-7.5

Log adsorbed U(VI) (moles/g)

-11.0

-10.5

-10.0

-9.5

-9.0

Log adsorbed U(VI) (moles/m2

)

Total U(VI) in experiment (moles/L)

1.3 x 10-7

3.3 x 10-7

1.02 x 10-6

3.01 x 10-6

1.0 x 10-5

Artificial Groundwater solutions (I = 0.1M)

Isotherm for pH 7.95, 1300 ppm CO2

Figure 3.5. U(VI) sorption data on the composite sample of Forty Mile Wash subsurface 

sediments (FMW-COMP) in artificial groundwater solution at ionic strength of 0.1M. 6-

day equilibration time was used for U(VI) sorption following one pretreatment of the 

sediments with the artificial groundwater solution. The open symbols represent data with 

nearly identical conditions for pH (7.94-7.96), alkalinity (2.19-2.32 meq/L), and pCO2

(1240-1310 ppm), meaning that these data approximately represent a traditional isotherm. 

-7.5 -7.0 -6.5 -6.0 -5.5 -5.0

Log dissolved U(VI) (moles/L)

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

Log Kd for U(VI) adsorption (mL/g)

Artificial Groundwater solutions (I = 0.1M) pH 7.95; 1300 ppm CO2

pH 7.7; 3400 ppm CO2

pH 7.1; 1.9% CO2

Figure 3.6. U(VI) sorption data on the composite sample of Forty Mile Wash subsurface 

sediments (FMW-COMP) in artificial groundwater solution at ionic strength of 0.1M as a 

function of dissolved U(VI) and PCO2. Data expressed as log Kd for U(VI) sorption versus 

log dissolved U(VI) concentration. 
88 

-7.5 -7.0 -6.5 -6.0 -5.5 -5.0

Log dissolved U(VI) (moles/L)

0.0

10.0

20.0

30.0

40.0

50.0

60.0

Percent U(VI) adsorbed

Artificial Groundwater solutions (I = 0.1M)

pH 7.95; 1300 ppm CO2

pH 7.7; 3400 ppm CO2

pH 7.1; 1.9% CO2

Figure 3.7. U(VI) sorption data on the composite sample of Forty Mile Wash subsurface 

sediments (FMW-COMP) in artificial groundwater solution at ionic strength of 0.1M as a 

function of dissolved U(VI) and PCO2. Data expressed as percent U(VI) adsorbed versus 

log dissolved U(VI) concentration.

total U(VI) concentrations and the open symbols 

in Figure 3.5 shows conditions in which the 

parameters pH (7.94 to 7.96), alkalinity (2.19 to 

2.32 meq/L) and pCO2 (1240-1310 ppm) varied 

over a relatively narrow range. A linear 

regression line through these data had a slope of 

0.755 with a correlation coefficient of 0.999. 

3.3 Uranium(VI) Surface 

Complexation Modeling for FortyMile Wash Sediments 

3.3.1 Introduction 

There is considerable experimental data for 

U(VI) adsorption in systems with one mineral 

phase, and surface complexation models (SCM) 

have been developed to accurately describe 

these data (e.g., Arnold et al., 2001; Pabalan et 

al., 1998; Waite et al., 1994). Nevertheless, the 

application of SCM to the mixtures of minerals 

in soils and sediments is difficult because of the 

presence of secondary mineral and organic 

coatings (USNRC, 2003; Penn et al. 2001, 

Padmanabhan and Mermut, 1996; Coston et al., 

1995) that affect the stoichiometry and 

coulombic correction factors of surface 

complexation reactions. There are two major 

approaches for applying the SCM concept to 

soils and sediments: the Component Additivity 

(CA) and Generalized Composite (GC) 

approaches (Davis et al., 2004; 1998). 

In the CA approach, it is assumed that a mineral 

assemblage is composed of one or more 

reference phases, whose surface chemical 

reactions are known from independent studies of 

each phase (e.g., Arnold et al., 2001; Prikryl et 

al., 2001; Turner et al., 1996; Cowan et al., 

1992). Then, based on a measurement of the 

relative amounts or surface areas of each mineral 

present in the soil or sediment, adsorption by the 

mixture of phases can be predicted by an 

equilibrium calculation, without any fitting of 

experimental data for the mixture (Honeyman, 

1984). CA model predictions are sometimes 

made by assuming that one mineral component 

dominates adsorption (Barnett et al., 2002; 
89 

Waite et al., 2000; Schmeide et al., 2000; Davis 

et al., 1998), allowing a straightforward 

equilibrium calculation if the exposed surface 

area of that mineral component in the soil or 

sediment can be quantified. 

In the GC approach, the surface of the mineral 

assemblage is considered too complex to be 

quantified in terms of the contributions of 

individual phases to adsorption. The complexity 

is caused, in part, by the difficulties in 

quantifying the electrical field and proportions 

of surface functional groups at the mineral-water 

interface in the mixture of mineral phases and 

associated surface coatings. In the GC approach 

it is assumed that adsorption can be described by 

mass laws written with “generic” surface 

functional groups, with the stoichiometry and 

formation constants for each mass law 

determined by fitting experimental data for the 

mineral assemblage as a whole (Davis et al., 

2004; Davis et al., 2002; Davis et al., 1998; 

Westall et al., 1998; van Benschoten et al., 

1998). GC model calculations are presented in 

this section to simulate U(VI) adsorption by 

subsurface sediments collected from Forty-Mile 

Wash aquifer (Nye County, Nevada). 

3.3.2 Modeling Calculations and 

Approach 

FITEQL 4.0 (Herbelin and Westall, 1999) was 

used to determine the best fit of various U(VI) 

surface reactions or combinations of reactions to 

the experimental U(VI) adsorption data in the 

GC model calculations. The Davies equation 

was used for activity correction of aqueous 

species only. Relative errors of 1% in the 

concentrations of surface sites, total U(VI), and 

adsorbed U(VI), and relative errors of 5% in log 

[H+

] and log [H2CO3] were used as FITEQL 

inputs. Thermodynamic data used in the 

modeling are consistent with the most recent 

NEA database for uranium (Guillaumont et al., 

2003), except the aqueous species, 

CaUO2(CO3)3

2- and Ca2UO2(CO3)30

(aq) 

(Kalmykov and Choppin, 2000; Bernhard et al., 

2001) were also included. Corrected to zero 

ionic strength, Bernhard et al. (2001) estimated 

values of 25.4±0.3 and 30.6±0.3 for the log of 

the formation constants for reactions (3.1) and 

(3.2), respectively, and Kalmykov and Choppin 

(2000) estimated a value of 29.8±0.7 for reaction 

(3.2): 

Ca2+ + UO2+ + 3CO3

2- = CaUO2(CO3)32- 

 (3.1) 

2Ca2+ + UO2+ + 3CO3

2- = Ca2UO2(CO3)30 (aq) 

 (3.2) 

To be consistent with the new NEA database 

(Guillaumont et al., 2003), the log K values for 

reactions (3.1) and (3.2) were corrected upwards 

by 0.24 log unit (Fox et al., 2006). For reaction 

(3.2), the value of Kalmykov and Choppin 

(2000) was chosen for modeling calculations 

because it gave a better prediction of the 

experimental data for U(VI) sorption on quartz 

in the presence of Ca (Fox et al., 2006). 

The GC model was developed based on a semiempirical fit to the experimental data for U(VI) 

adsorption by the Forty-Mile Wash aquifer 

sediments. In the GC approach, laboratory 

experiments are conducted with sediments from 

the field site to be modeled, and mass law 

relationships are derived that describe the 

change in metal ion adsorption with variations in 

groundwater chemical conditions observed or 

anticipated in the aquifer (Davis et al., 2004; 

2002; 1998). Because no electrostatic 

information was available for the charge and 

potential on Forty-Mile Wash sediment surfaces, 

a non-electrostatic GC model was used. In 

principle, potentiometric titration can be used to 

determine the surface proton balance of soils and 

sediments (Charlet and Sposito, 1987), as is 

commonly done to determine surface charge on 

pure, insoluble mineral phases, such as goethite 

(Lützenkirchen et al., 2002). However, the 

interpretation of the titration data is complex in 

the case of soils and sediments, because the 

consumption of titrant is ofter dominated by the 

dissolution of mineral phases, the release of 

organic acids, or other adsorption/desorption 

reactions. 

In order to simplify the GC model further, the 

following decisions were made a priori: a) a 

total site density of 3.84 μmoles/m2

 was 

assumed (based on the recommended total site 
90 

density (Davis and Kent, 1990) of 3.84 

μmoles/m2), b) at least two types of sites (strong 

and weak) were assumed to exist on the surface 

to account for the non-linear adsorption 

isotherms commonly observed for U(VI) 

adsorption (Davis, 2001; Waite et al., 1994), and 

as observed here for Forty-Mile Wash sediments 

(Figs. 3.2 and 3.5), and c) it was assumed that 

U(VI) adsorption could be described by three or 

less surface reactions. The latter constraint was 

arbitrary but was adopted to reduce the number 

of model fitting parameters. In general, a goal of 

the semi-empirical GC modeling approach is to 

develop the simplest model possible that 

describes the major features of adsorption as 

chemical conditions are varied over fieldrelevant ranges (Davis et al., 1998). 

Based on previous work with U(VI) (Davis et 

al., 2004; USNRC, 2003), at least nine 

monomeric U(VI) surface reactions are possible 

in a uranyl/carbonate/oxide system (Table 3.1). 

Other U(VI) surface ternary complexes are 

plausible, including uranyl-phosphate and -

silicate ternary complexes, but these were not 

considered because their existence has not yet 

been confirmed in model systems. Because of 

the excess of surface sites in comparison to the 

U(VI) concentrations in the experiments, 

combined with the assumption of a nonelectrostatic model, it is not necessary to 

consider ionization reactions of the surface 

hydroxyl groups (Davis et al., 1998). 

Initially the experimental data were fit with a 

one-site model. FITEQL calculations were 

completed to determine which single reaction 

(Table 3.1) would provide the best fit to the 

experimental data. FITEQL output includes a 

goodness-of-fit parameter, WSOS/DF, the 

weighted sum of squares of the difference in 

value between model simulations and 

experimental data points, divided by the degrees 

of freedom (Herbelin and Westall, 1999). 

Lower values of WSOS/DF mean the proposed 

model is a better fit to the data; WSOS/DF will 

be referred to as a “fit value” below. 

Representing the U(VI) adsorption data with a 

single reaction produced a reasonably good fit, 

with the best fits (fit values = 4.9 or 11.3) 

provided by reactions 2 or 4, respectively (Table 

3.1). The second step in model development 

was to consider combinations of two reactions to 

represent the data. Unlike the case for modeling 

U(VI) adsorption on Naturita sediments (Davis 

et al., 2004; USNRC, 2003), the fit to the data 

was not improved by adding a second reaction; 

the best fits were still the single reactions 2 or 4. 

Further testing showed that the fit could be 

improved by adding a second site type, a strongbinding site. The site density of the strongbinding site was optimized by comparing the fits 

to the data as a function of the site density; this 

was done in separate calculation with only 

reaction 2 or 4 alone, and with a combination of 

reaction 2 and 4. The best overall fit to the 

experimental data occurred with reaction 2 alone 

and with a strong site density of 0.002% of total 

sites (fit = 1.17). The log K values for reaction 

2 in the optimized fit to the experimental data 

were -0.541 and -5.761 for the strong-site and 

weak-site reactions, respectively. The fit of the 

GC model to experimental data is illustrated in 

Figures 3.8 through 3.10 and the model 

Table 3.1. U(VI) Surface Reactions Considered for the Forty-Mile Wash GC Model 

Number Reaction 

1 SOH + UO2

2+ = SOUO2+

 + H+

2 SOH + UO2

2+ + H2O = SOUOOH + 2H+

3 SOH + UO2

2+ + H2CO3 = SOUO2HCO3 + 2H+

4 SOH + UO2

2+ + H2CO3 = SOUO2CO3-

 + 3H+

5 SOH + UO2

2+ + H2CO3 + H2O = SOUO2OHCO32- + 4H+

6 SOH + UO2

2+ + 2H2CO3 = SOUO2(HCO3)2-

 + 3H+

7 SOH + UO2

2+ + 2H2CO3 = SOUO2(CO3HCO3)

2- + 4H+

8 SOH + UO2

2+ + 2H2CO3 = SOUO2(CO3)23- + 5H+

9 SOH + UO2

2+ + 2H2CO3 + H2O = SOUO2OH(CO3)24- + 6H+
91 

-7.5 -7.0 -6.5 -6.0 -5.5 -5.0

Log dissolved U(VI) (moles/L)

-0.2

-0.0

0.2

0.4

0.6

0.8

1.0

Log Kd (mL/g)

pH 8.5; 440 ppm CO2

Model - pH 8.5; 440 ppm CO2

pH 8.35; 740 ppm CO2

Model - pH 8.35; 740 ppm CO2

pH 8.0; 2300 ppm CO2

Model - pH 8.0; 2300 ppm CO2 pH 7.1; 1.9% CO2

Model - pH 7.1; 1.9% CO2

Artificial Groundwater solutions (I = 0.005-0.015M)

Figure 3.8a. Comparison of U(VI) sorption data and surface complexation model 

calculations for the composite sample of Forty Mile Wash subsurface sediments (FMWCOMP) in artificial groundwater solutions at low ionic strength as a function of dissolved 

U(VI) and PCO2. Data expressed as log Kd for U(VI) sorption versus log dissolved U(VI) 

concentration and shown with large symbols. Model calculations shown as curved splines, 

with individual model points shown with small symbols. 

-7.5 -7.0 -6.5 -6.0 -5.5 -5.0

Log dissolved U(VI) (moles/L)

-0.4

-0.2

-0.0

0.2

0.4

0.6

0.8

1.0

Log Kd (mL/g)

pH 8.45; 550 ppm CO2

Model - pH 8.45; 550 ppm CO2

pH 7.95; 1260 ppm CO2

Model - pH 7.95; 1260 ppm CO2

pH 7.7; 3400 ppm CO2

Model - pH 7.7; 3400 ppm CO2

pH 7.2; 1.7% CO2

Model - pH 7.2; 1.7% CO2

Artificial Groundwater solutions (I = 0.005-0.015M)

pH 7.6; 7000 ppm CO2

Model - pH 7.6; 7000 ppm CO2

Figure 3.8b. Comparison of U(VI) sorption data and surface complexation model 

calculations for the composite sample of Forty Mile Wash subsurface sediments (FMWCOMP) in artificial groundwater solutions at low ionic strength as a function of dissolved 

U(VI) and PCO2. Data expressed as log Kd for U(VI) sorption versus log dissolved U(VI) 

concentration and shown with large symbols. Model calculations shown as curved splines, 

with individual model points shown with small symbols. 
92 

-7.5 -7.0 -6.5 -6.0 -5.5 -5.0

Log dissolved U(VI) (moles/L)

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

Log Kd for U(VI) adsorption (mL/g)

Artificial Groundwater solutions (I = 0.1M)

pH 7.95; 1300 ppm CO2

pH 7.7; 3400 ppm CO2

pH 7.1; 1.9% CO2

Model - pH 7.95; 1300 ppm CO2

Model - pH 7.7; 3400 ppm CO2

Model - pH 7.1; 1.9% CO2

Figure 3.9. Comparison of U(VI) sorption data and surface complexation model 

calculations for the composite sample of Forty Mile Wash subsurface sediments (FMWCOMP) in artificial groundwater solutions at an ionic strength of 0.1M as a function of 

dissolved U(VI) and PCO2. Data expressed as log Kd for U(VI) sorption versus log 

dissolved U(VI) concentration and shown with large symbols. Model calculations shown as 

curved splines, with individual model points shown with small symbols. 

2.0 2.4 2.8 3.2 3.6 4.0 4.4 4.8

Alkalinity (meq/L)

0

10

20

30

40

50

60

70

% U(VI) adsorbed 

Utot = 1.0 x 10-7M

Utot = 3.0 x 10-7M

Utot = 1.0 x 10-6M

Utot = 3.0 x 10-6M

Figure 3.10. Comparison of selected U(VI) sorption data and surface complexation model 

calculations for the composite sample of Forty Mile Wash subsurface sediments (FMWCOMP) in artificial groundwater solutions at low ionic strength. Data expressed as percent 

U(VI) adsorbed versus alkalinity and shown with large symbols. Model calculations shown 

as curved splines, with individual model points shown with small symbols.
93 

describes the data quite well. The fits are shown 

for the variation of log Kd value as a function of 

dissolved U(VI) concentration and PCO2 at low 

ionic strength (Fig. 3.8) and high ionic strength 

(Fig. 3.9). Figure 3.10 shows the fit of a portion 

of the dataset with the data expressed as percent 

U(VI) adsorbed versus the alkalinity in the 

artificial groundwater solution. The 

experimental data are shown as large symbols in 

the figures; model simulations are shown as 

smooth curves, with the individual model 

calculations shown as small symbols.
94 

 
95 

4 DEVELOPMENT OF A SURFACE COMPLEXATION MODEL FOR 

NICKEL SORPTION ONTO CAPE COD (MASSACHUSETTS) 

 AQUIFER SEDIMENTS 

4.1 Materials and Methods 

4.1.1 Preparation of an Aquifer Sediment 

Composite Sample 

Aquifer sediment was collected in 1994 along a 

transect two meters east of the multilevel 

sampler array at the USGS research site at Cape 

Cod, Massachusetts (Davis et al., 2000), using a 

wireline-piston core barrel and plastic core liners 

approximately 1.5 meters in length. Sufficient 

core material was collected from the suboxic 

zone to make three composites of the sediments, 

one of which was used in the following 

experiments (suboxic composite #2). Cores 

were frozen immediately after collection and 

stored frozen until processing in the USGS 

laboratory in Menlo Park, CA. 

Each core was sectioned at 10 cm intervals, 

dried under a laminar flow hood at room 

temperature, and then sieved to remove grains 

>1000 microns in diameter. A sample of <1000 

micron sediment from the core section was then 

leached with 0.25 hydroxylamine hydrochloride 

solution in 0.25 M HCl (HH) at 50°C for 30 

minutes; the solid to solution ratio in the 

extractions was 200 g/L. The supernatants of the 

extractions were measured by inductivelycoupled argon plasma optical emission 

spectrometry (ICAP-OES) using single point 

external calibration standards (Coston et al. 

1998). Analysis of the extraction solutions was 

used to determine the amount of zinc (Zn) 

extracted in each subsample. The amount of zinc 

was used to determine where the Zncontaminated transition zone was in each core 

(Rea et al. 1991, Coston et al. 1995, Kent et al., 

2000), in order to prepare a composite of 

suboxic material that was located beneath the 

Zn-contaminated transition zone. BET surface 

areas were measured by nitrogen gas adsorption 

for the composite sand sample, using methods 

previously discussed in Coston et al. (1995). 

4.1.2 Nickel Sorption Experiments 

Batch experiments were conducted using the 

same suboxic artificial groundwater solution 

(SAGW) used in previous experiments by 

Coston et al. (1995). The pH was controlled by 

addition of 2-N-morpholinoethane sulfonic acid 

buffer (MES) to the artificial groundwater 

solution. MES is known to be an extremely 

weak chelator of metals and is used in 

biochemical studies of metal binding by amino 

acids and peptides (Coston et al., 1995). SAGW 

was adjusted within the pH range 4.5-6.7. All 

salts used to make primary solutions were trace 

metal grade. Since the sediment was dried, these 

experiments included a pretreatment procedure 

using the SAGW. A solid/solution ratio of 400 

g of sediment per L of artificial groundwater 

solution was used in all nickel (Ni) sorption 

experiments. 

4.2 Results 

Preliminary experiments demonstrated that there 

was no significant interference of the MES 

buffer with Ni sorption. The surface area of the 

Cape Cod aquifer composite sample was 0.33 

m2

/g. 

Figures 4.1 and 4.2 show the adsorption of 

Ni(II) at total concentrations of 1, 18 and 29 μM 

with increasing pH, and Ni sorption isotherms at 

pH 5.4 and 6.1. As expected, Ni adsorption 

increases with increasing pH and the slope of the 

isotherms (log-log scale) is less than one, 

indicating that the average Ni adsorption bond is 

weaker with increasing Ni concentration. 

Sodium carbonate solution extractions of the 

Cape Cod sand composite showed that reactive 

phosphate concentrations averaged 0.0046 

milligrams of P per gram of sand. 1M 

hydrochloric acid extractions of the sand yielded 

0.02 milligrams of P per gram of solid. Sorption 

experiments were carried out to determine the
96 

4.6 5.0 5.4 5.8 6.2 6.6 7.0

pH

0

10

20

30

40

50

60

70

80

90

100

Percent Ni adsorbed

1.0 x 10-6M Ni

1.8 x 10-5M Ni

2.9 x 10-5M Ni

400 g/L sediment in artificial groundwater

Figure 4.1. Percent Ni adsorbed as a function of pH and total Ni concentration for 

suspensions with 400 g/L of Cape Cod aquifer sand composite in artificial groundwater 

solution. 

-6.2 -6.0 -5.8 -5.6 -5.4 -5.2 -5.0 -4.8 -4.6 -4.4 -4.2 -4.0

Log dissolved Ni (moles/L)

-8.6

-8.4

-8.2

-8.0

-7.8

-7.6

-7.4

-7.2

-7.0

-6.8

Log adsorbed Ni (moles/m2)

pH 5.4

pH 6.1

Figure 4.2. Ni adsorption isotherms for Cape Cod aquifer sand composite in artificial 

groundwater solution at pH 5.4 and 6.1. 
97 

possible effects of phosphorus sorption on Ni(II) 

sorption. Phosphate concentrations of both 400 

and 700 micromolar P had no observable effect 

on 20 micromolar Ni sorption at pH 6.25. 

4.3 Surface Complexation Model 

Development for Nickel Adsorption 

FITEQL 4.0 (Herbelin and Westall, 1999) was 

used to determine the best fit of various Ni 

surface reactions or combinations of reactions to 

the experimental Ni adsorption data in GC 

model calculations. The Davies equation was 

used for activity correction of aqueous species 

only. Relative errors of 1% in the concentrations 

of surface sites, total Ni, and adsorbed Ni, and 

relative errors of 5% in log [H+

] and log 

[H2CO3] were used as FITEQL inputs. 

Thermodynamic data used in the modeling are 

consistent with the data given in Smith and 

Martell (1981). 

The GC model was developed based on a semiempirical fit to the experimental data for Ni 

adsorption by the Cape Cod aquifer sediments. 

As in the case for U(VI) sorption on Forty-Mile 

Wash sediments (section 3), a non-electrostatic 

GC model was used. In order to simplify the GC 

model further, the following decisions were 

made a priori: a) a total site density of 3.84 

μmoles/m2 was assumed (based on the 

recommended total site density by Davis and 

Kent, 1990), b) at least two types of sites (strong 

and weak) were assumed to exist on the surface 

to account for the non-linear adsorption 

isotherms observed (Fig. 4.2), and c) it was 

assumed that Ni adsorption could be described 

by two or less surface reactions. Table 4.1 

shows the two Ni surface reactions considered in 

the GC modeling. 

Initially it was attempted to fit the experimental 

data with a one-site model. FITEQL calculations 

were completed to determine which single 

reaction (Table 4.1) would provide the best fit to 

the experimental data. FITEQL output includes 

a goodness-of-fit parameter, WSOS/DF, the 

weighted sum of squares of the difference in 

value between model simulations and 

experimental data points, divided by the degrees 

of freedom (Herbelin and Westall, 1999). 

Lower values of WSOS/DF mean the proposed 

model is a better fit to the data; WSOS/DF will 

be referred to as a “fit value” below. Neither 

reaction 1 or 2 or a combination of the two 

(Table 4.1) could produce a good fit of the data 

with a one-site model. 

Representing the Ni adsorption data with 

reaction 1 (Table 4.1) produced a reasonably 

good fit with a two-site model. No improvement 

in the fit could be achieved by adding reaction 2 

to the model. The site density of the strongbinding site was optimized by comparing the fits 

to the data as a function of the site density; the 

best overall fit to the experimental data occurred 

with reaction 1 alone and with a strong site 

density of 1% of total sites (fit = 14.8). The log 

K values for reaction 1 in the optimized fit to the 

experimental data were -0.675 and -3.331 for the 

strong-site and weak-site reactions, respectively. 

The fit of the GC model to experimental data is 

illustrated in Figures 4.3 and 4.4 and the model 

describes the data reasonably well. The fits are 

shown for the percent Ni adsorbed as a function 

of pH and total Ni concentration (Fig. 4.3) and 

for the Ni adsorption isotherms at pH 5.4 and 6.1 

(Fig. 4.4).

 

Table 4.1. Ni Surface Reactions Considered for the Cape Cod GC Model 

Number Reaction 

1 SOH + Ni2+ = SONi+ + H+

2 SOH + Ni2+ + H2O = SONiOH + 2H+
98 

4.6 5.0 5.4 5.8 6.2 6.6 7.0

pH

0

10

20

30

40

50

60

70

80

90

100

Percent Ni adsorbed

1.0 x 10-6M Ni

1.8 x 10-5M Ni

2.9 x 10-5M Ni

400 g/L sediment in artificial groundwater

Model; 1.0 x 10-6M Ni

Model; 1.8 x 10-5M Ni

Model; 2.9 x 10-5M Ni

Figure 4.3. Comparison of GC model simulation and experimental data for percent Ni 

adsorbed as a function of pH and total Ni concentration, for suspensions with 400 g/L of 

Cape Cod aquifer sand composite in artificial groundwater solution. 

-6.2 -6.0 -5.8 -5.6 -5.4 -5.2 -5.0 -4.8 -4.6 -4.4 -4.2 -4.0

Log dissolved Ni (moles/L)

-8.6

-8.4

-8.2

-8.0

-7.8

-7.6

-7.4

-7.2

-7.0

-6.8

Log adsorbed Ni (moles/m2)

pH 5.4

pH 6.1

Model pH 5.4 isotherm

Model pH 6.1 isotherm

Figure 4.4. Comparison of GC model simulations and experimental data for Ni adsorption 

isotherms for Cape Cod aquifer sand composite in artificial groundwater solution at pH 5.4 

and 6.1. 
99 

5 CONCLUDING REMARKS AND SUMMARY 

Prior to this investigation, the Generalized 

Composite (GC) modeling approach to surface 

complexation model development had been 

tested at the field scale in two other studies 

(Curtis et al., 2006; USNRC, 2003; Kent et al., 

2000) and in a small number of laboratory 

investigations. The objective of the current 

study was to demonstrate the GC modeling 

approach for a number of different sorbing 

radionuclides and geomaterials. Geomaterials 

chosen for the study included the Naturita and 

Cape Cod aquifer sediments because these 

materials have been well characterized in the 

literature (Davis et al., 2004; USNRC, 2003; 

Kohler et al., 2004; Davis et al., 1998; Coston et 

al., 1995). In addition, it was desired to 

demonstrate the GC model for uranium(VI) on a 

new geomaterial; the Forty-Mile Wash aquifer 

was chosen for that purpose. Most previous GC 

model studies have been done with uranium(VI); 

in this work GC models were developed for 

Np(V) and Ni to expand the number of 

radionuclides for which adsorption has been 

modeled with this approach. 

It is known from studies of pure mineral phases 

that dissolved carbonate concentration and pH 

are significant aqueous chemical variables that 

influence the adsorption of U(VI) and Np(V) 

(Davis et al., 2004; Waite et al., 1994; Kohler et 

al., 1999). As expected, adsorption of U(VI) by 

the Forty-Mile Wash sediments was strongly 

influenced by the carbonate concentration 

(Section 3), however, the influence of carbonate 

and humic acids on Np(V) sorption by Naturita 

sediments was much weaker (Section 2). For Ni, 

pH is the main variable that influences its 

sorption (Section 4). 

In the literature, at least three types of adsorption 

modeling approaches can be distinguished: 1) 

empirical partitioning relationships (Davis and 

Kent, 1990), 2) thermodynamic surface 

speciation models that describe the molecular 

details of chemical species formation at mineral 

surfaces (Hiemstra and van Riemsdijk, 1999; 

Sahai and Sverjensky, 1997), and 3) semiempirical site-binding models that utilize 

concepts from the first two model types (Davis 

et al., 2004; Davis et al., 2002; Borkovec et al., 

1998; Davis et al., 1998). 

In the first approach, empirical relationships 

such as Kd values, sorption isotherms, and 

partitioning equations are used to predict 

adsorption as a function of aqueous 

concentration variables (Davis and Kent, 1990). 

Kd values are defined as: 

Kd = RNads / RNaq

where RNads refers to the quantity of 

radionuclide sorbed and RNaq refers to the 

dissolved concentration of the same 

radionuclide. The relationship implies that a 

constant ratio is obtained between these two 

quantities, as in a linear isotherm. 

There are many types of non-linear adsorption 

isotherms. The most common, the Langmuir 

isotherm can be represented as (Davis and Kent, 

1990): 

KL = RNads / (RNaq)(Γfree) 

where KL is a constant and Γfree refers to the 

quantity (or concentration) of available surface 

sites. The Langmuir isotherm accounts for the 

decrease in Kd values that occurs as an 

adsorbing surface becomes partially saturated 

with adsorbed species (Davis and Kent, 1990). 

The relationship is usually determined for a 

specific set of constant chemical and physical 

conditions while a radionuclide concentration is 

varied. 

Kd values for radionuclides have been 

determined experimentally on a wide variety of 

geological solids (Altmann et al., 2001; USEPA, 

1999), and the variability of Kd values with 

chemical conditions has been the subject of 

several studies (Waite et al., 1994; Davis et al., 

2002; USEPA, 1999; Pabalan et al., 1998; 

Altmann et al, 2001; Turner and Sassman, 1996; 

Prikryl et al., 2001; Barnett et al., 2002). For 

example, it has been shown that Kd values for 
100 

adsorption of U(VI) on sediments and pure 

mineral phases may vary by more than 8 orders 

of magnitude over a wide range of chemical 

conditions in which U(VI) aqueous speciation 

changes significantly (e.g., pH 4-10) (USEPA, 

1999). This is in agreement with other reports 

that show that Kd values are highly sensitive to 

chemical conditions (Curtis et al., 2004; 

USNRC, 2003, Davis et al., 2002; Bethke and 

Brady, 2000; Kent et al., 2000; USNRC, 2001). 

For the second and third approaches, surface 

complexation models (SCM) use mass action 

laws analogous to aqueous phase reactions to 

describe adsorption (USNRC, 2001; Davis and 

Kent, 1990) as a function of aqueous chemical 

conditions, thus taking into account changes in 

chemical speciation, competitive adsorption, and 

other multi-solute interactive chemical effects. 

The possible advantages of applying the surface 

complexation concept to describe radionuclide 

sorption in nuclear waste performance 

assessment (PA) include the following factors: 

1) the modeling approach provides a 

thermodynamic framework to describe 

adsorption reactions of radionuclides, 

2) the stability constants for the adsorption 

reactions can be included as part of an overall 

network of chemical reactions in geochemical 

equilibrium or coupled reactive transport 

models, and thus coupled with thermodynamic 

databases for radionuclide aqueous speciation 

and solubilities, such as the NEA 

thermodynamic database (Guillaumont et al., 

2003), 

3) the modeling approach allows predictive 

calculations for a range of chemical conditions 

without adjustment of the values of the model 

parameters, as chemical conditions are varied in 

space or time, unlike the condition-dependent 

empirical relationships, 

4) the modeling approach can be included 

efficiently in transport simulations (or PA 

scenarios) in which there are chemical gradients 

in space or time. 

The thermodynamic surface speciation and GC 

modeling approaches represent two extremes of 

surface complexation modeling (Davis et al., 

2004; Davis et al., 1998). Table 5.1 compares 

the modeling approaches and their data 

requirements. In thermodynamic surface 

speciation models (Weerasoriya et al., 2001; 

Hiemstra and van Riemsdijk, 1999), the surface 

species postulated must be supported with 

spectroscopic evidence (e.g., Arai et al., 2007; 

Bargar et al., 2000, 1999). Thermodynamic 

surface speciation models typically include 

electrical double layer terms in the mass law 

equations, and hence, adsorption predictions 

with these models are sensitive to the double 

layer parameters, as has been shown for U(VI) 

adsorption on hematite (USNRC, 2003). 

The sensitivity to electrostatic terms illustrates a 

significant practical problem in extending 

thermodynamic surface speciation models 

directly to simulate radionuclide adsorption on 

complex mineral assemblages in the 

environment. Mineral surfaces in the 

environment are typically coated with poorly 

crystalline secondary mineral coatings, as has 

been shown for the Naturita and Cape Cod 

aquifer sediments (Davis et al., 2004; USNRC, 

2003; Sanpawanitchakit, 2002). The coatings 

make it extremely difficult to assess the 

electrostatic contribution to the free energy of 

adsorption quantitatively. In the literature one 

finds the frequently made assumption (e.g., 

Barnett et al., 2002; Arnold et al., 2001) that the 

electrical double layer properties of pure mineral 

phases studied in the laboratory are the same in a 

mineral assemblage in the environment. It 

seems highly unlikely that this assumption is 

valid given the fact that coatings of various 

materials on mineral surfaces are very prevalent 

in soils and sediments. In addition to the fact 

that coatings drastically change the electrical 

double layer at surfaces, the Component 

Additivity modeling approach is difficult to 

apply because the site densities of the mineral 

and organic phases in the coatings that are 

contributing to radionuclide adsorption are 

unknown (Davis et al., 2004; Davis et al., 2002; 

Sanpawanitchakit, 2002; Davis et al., 1998). 
101 

Table 5.1. Characteristics of Surface Complexation Modeling 

Approaches for Environmental Adsorbents 

Thermodynamic Surface Speciation 

Modeling1

Semi-Empirical (GC) Modeling 

A Adsorption is predicted from 

thermodynamic constants and known 

(confirmed) surface species 

Adsorption data are simulated (fit) 

using site-specific adsorbent samples, 

using chemically plausible surface 

reactions 

B Surface sites are unique and defined 

for each specific mineral phase present 

in environmental samples 

Generic surface sites are assumed, 

with average chemical characteristics 

C Surface site densities are quantified by 

detailed characterization of the surface 

of environmental samples 

Surface site densities are quantified 

by the measurement of specific 

surface area or by fitting adsorption 

data of environmental samples 

D Apparent stability constants and 

reaction stoichiometries are obtained 

from studies of adsorption by 

reference mineral phases present in 

environmental samples 

Apparent stability constants and 

reaction stoichiometries are fit 

to experimental adsorption data for 

environmental samples 

E Overall adsorption is predicted by 

the sum of adsorption calculated for 

each specific mineral phase present in 

environmental samples 

Numbers of surface site types and 

chemical reactions are increased as 

necessary to achieve good model 

simulations and to meet modeling 

objectives 

1 Thermodynamic surface speciation models that are developed by predicting adsorption as the sum of 

contributions from individual mineral phases are called Component Additivity models (Davis et al., 2004; 

Davis et al., 2002; Sanpawanitchakit, 2002; Davis et al., 1998; Honeyman, 1984). Characteristics D and 

E for thermodynamic surface speciation modeling in Table 5.1 apply to the Component Additivity 

modeling approach. 

Thus, the challenge in applying the surface 

complexation concept in the environment is to 

simplify the adsorption model, such that 

predicted adsorption is still calculated with mass 

laws that are coupled with aqueous speciation, 

while lumping parameters that are difficult to 

characterize in the environment in with other 

parameters. This modeling approach can be 

achieved with the semi-empirical, site-binding 

(GC) modeling approach used in this report and 

elsewhere (Davis et al., 2004; Davis et al., 2002; 

Borkovec et al., 1998; Davis et al., 1998). 

While a thermodynamic surface speciation 

model must be validated with spectroscopic 

evidence and other detailed data to confirm 

surface speciation and electrical double layer 

properties (Hiemstra and van Riemsdijk, 1999), 

the GC modeling approach is more easily 

applied and less experimental data need to be 

collected. The range of applicability of a GC 

model with respect to chemical variation is 

determined by the type and amount of 

experimental data collected. GC model 

parameters are calibrated by fitting a simple 

surface speciation model such that the major 

features of radionuclide adsorption are simulated 

as chemical conditions are varied over fieldrelevant ranges (Curtis et al., 2004; Kohler et al., 

2004; Davis et al., 2004; Davis et al., 1998). 

An important limitation of the GC approach is 

that adsorption predictions should not be 

extrapolated to conditions outside of the range 
102 

for which data were collected for model 

calibration. However, a GC model can be used 

for predictive calculations that interpolate within 

the range of chemical conditions studied. 

 

While the GC modeling approach has not been 

widely used to date, we believe it is a practical 

approach in terms of data collection needs and 

the capability to predict radionuclide 

distributions and transport over a range of 

chemical conditions at field sites (Curtis et al., 

2006; Kent et al., 2000). The GC modeling 

approach is also an important compromise 

between the simple constant Kd approach and 

the most complex SCM, the thermodynamic 

surface speciation models (Hiemstra and van 

Riemsdijk, 1999). In order to be applied by 

solute transport modelers and within PA 

applications, the complexity of the adsorption 

model needs to be balanced with the goal of 

using the simplest model possible that is 

consistent with observed data. Historically, 

solute transport modelers have lacked the 

necessary expertise to apply the SCM modeling 

approach and many have believed that the SCM 

approach is too complex to be applied. While it 

is true that the thermodynamic surface 

speciation models are currently difficult to 

apply, it has been demonstrated in Curtis et al. 

(2006), USNRC (2003), and Kent et al. (2000) 

that the GC modeling approach can be easily 

applied to simulations of radionuclide transport 

at the field scale. 

The GC models developed here for U(VI), 

Np(V) and Ni adsorption by aquifer sediments 

did not correct the mass laws with terms that 

estimate electrostatic attraction or repulsion of 

surface species by average surface charge. In 

USNRC (2003) it was shown that transport 

simulations were not sensitive to the use of nonelectrostatic adsorption models by comparing 

simulations with and without explicit 

consideration of surface charge. 

A limitation of the GC approach is that the mass 

action equations and associated stability 

constants are valid only for site-specific 

materials; a database that is transferable to other 

mineral assemblages is not expected. However, 

the GC approach is preferable to completely 

empirical approaches, such as the constant Kd

model or adsorption isotherms, because the 

important linkage between surface and aqueous 

species (and associated thermodynamic data) is 

retained in the modeling through the coupling of 

mass action equations. 

Our current understanding and models for 

adsorption are well advanced at the molecular 

scale (Arai et al., 2007; Bargar et al., 2000), but 

our understanding and models become 

increasingly uncertain as the physical scale 

increases. The observed adsorption of 

radionuclides by aquifer sediments is ultimately 

controlled by sorptive phases with dimensions 

on the order of tens of nanometers. This inherent 

heterogeneity of environmental samples makes 

application of thermodynamic surface speciation 

models difficult at present, even at the 

microscale level. However, in the authors’ 

opinion, the current operational paradigm that 

employs constant-Kd values for individual 

radionuclides introduces more uncertainty than 

is necessary in our predictions of radionuclide 

retardation during transport. This uncertainty 

could be reduced in the future with the use of 

site-binding (GC-SCM) models, such as 

described in this study. To reduce uncertainty, 

more emphasis needs to be placed on sitespecific characterization of natural mineral 

assemblages that are expected to be encountered 

along major flowpaths away from disposal sites 

or other source areas. From a practical point of 

view, a limiting factor in applying any type of 

SCM is the knowledge of the spatial and 

temporal variation in aqueous chemical 

conditions and mineral surfaces exposed along 

flow paths in PA scenarios. 
103 

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Predictions” Radiochimica Acta. 81, 93-101, 1998. 

Allard B., Olofsson U., Torstenfelt B., Kipatsi H. and Andersson K. “Sorption of 

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Altmann, S.A., J. Bruno, and C. Tweed, Using Thermodynamic Sorption Models for Guiding 

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