w a t e r r e s e a r c h 4 7 ( 2 0 1 3 ) 2 5 7 2 e2 5 8 2Available online at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/watres

Sensitivity of the transport and retention of stabilized silver
nanoparticles to physicochemical factors
Yan Liang a, Scott A. Bradford b,*, Jiri Simunek c, Harry Vereecken a, Erwin Klumpp aaAgrosphere Institute, IBG-3, Forschungszentrum Jülich GmbH, Jülich, Germany
US Salinity Laboratory, USDA, ARS, Riverside, CA, United Statesc
Department of Environmental Sciences, University of California, Riverside, CA, United Statesbarticle info

abstract

Article history:

Saturated sand-packed column experiments were conducted to investigate the influence of

Received 11 December 2012

physicochemical factors on the transport and retention of surfactant stabilized silver

Received in revised form

nanoparticles (AgNPs). The normalized concentration in breakthrough curves (BTCs) of

14 February 2013

AgNPs increased with a decrease in solution ionic strength (IS), and an increase in water

Accepted 16 February 2013velocity, sand grain size, and input concentration (Co). In contrast to conventional filtration

Available online 26 February 2013

theory, retention profiles (RPs) for AgNPs exhibited uniform, nonmonotonic, or hyperexponential shapes that were sensitive to physicochemical conditions. The experimental

Keywords:

BTCs and RPs with uniform or hyperexponential shape were well described using a nu-

Stabilized silver nanoparticles

merical model that considers time- and depth-dependent retention. The simulated

Saturated porous media

maximum retained concentration on the solid phase (Smax) and the retention rate coeffi-

Time- and depth-dependent

cient (k1) increased with IS and as the grain size and/or Co decreased. The RPs were more

retention

hyperexponential in finer textured sand and at lower Co because of their higher values ofSurfactant

Smax. Conversely, RPs were nonmonotonic or uniform at higher Co and in coarser sand that

Competitive attachment

had lower values of Smax, and tended to exhibit higher peak concentrations in the RPs atlower velocities and at higher solution IS. These observations indicate that uniform andnonmonotonic RPs occurred under conditions when Smax was approaching filled conditions. Nonmonotonic RPs had peak concentrations at greater distances in the presence ofexcess amounts of surfactant, suggesting that competition between AgNPs and surfactant
diminished Smax close to the column inlet. The sensitivity of the nonmonotonic RPs to IS
and velocity in coarser textured sand indicates that AgNPs were partially interacting in asecondary minimum. However, elimination of the secondary minimum only produced
recovery of a small portion (<10%) of the retained AgNPs. These results imply that AgNPs
were largely irreversibly interacting in a primary minimum associated with microscopic
heterogeneity.
Published by Elsevier Ltd.

1.

Introduction

Silver nanoparticles (AgNPs) have been widely used in various
commercial products because of their strong antimicrobialactivity (Kang et al., 2011). However, generation of AgNP

* Corresponding author. Tel.: þ1 951 369 4857; fax: þ1 951 342 4964.
E-mail address: scott.bradford@ars.usda.gov (S.A. Bradford).
0043-1354/$ e see front matter Published by Elsevier Ltd.
http://dx.doi.org/10.1016/j.watres.2013.02.025

aggregates can lead to a loss of antibacterial activity (Kvitek
et al., 2008; Zhang et al., 2012). AgNPs are therefore
commonly modified with surfactants or polymers to increase
their stability in liquids (Kvitek et al., 2008; Thio et al., 2012;
Zhang et al., 2012). AgNPs in consumer products have thew a t e r r e s e a r c h 4 7 ( 2 0 1 3 ) 2 5 7 2 e2 5 8 2

potential to be released into the environment (Benn andWesterhoff, 2008; Geranio et al., 2009). Concentrations of
AgNPs were estimated to range between 0.7 and 2.2 mg/kg in
sediment, but they can be even higher in sludge-treated soil
(Gottschalk et al., 2009). Consequently, there is concern thatthe antimicrobial effects of released AgNPs may adversely
impact ecosystem health (Choi et al., 2010). It has been reported that surface modification of NPs are crucial to determine the fate of NPs in the environment (Kvitek et al., 2008;
Tian et al., 2010; Lin et al., 2012; Wang et al., 2012). Knowledge
about the processes controlling the subsurface transport andretention of surface modified AgNPs in soils and sediments istherefore critical for accurate exposure assessment and todevelop efficient remediation techniques.
Simplified systems are typically employed to determine
fundamental mechanisms and models for colloid and NP
transport and deposition in the environment. For example,
quartz sands or glass beads are normally used as model
porous media and monovalent salts are used in electrolyte
solutions. Previous studies with quartz sands (Tian et al., 2010;
Lin et al., 2011, 2012; Song et al., 2011; Thio et al., 2012) or soils(Sagee et al., 2012) have demonstrated that AgNP transport issensitive to the surface properties of both AgNPs and porous
media, and the chemical composition of solution. The presence of organic matter such as surfactants or humic acidtended to enhance the mobility of AgNPs (Tian et al., 2010; Linet al., 2012; Thio et al., 2012), while higher ionic strength anddivalent cations promoted aggregation and retention (Linet al., 2011; Thio et al., 2012; Zhang et al., 2012). Other researchers have demonstrated a complex coupling between
many physicochemical factors (e.g., grain size, flow velocity,
the chemistry of the aqueous and solid phases, organic matter, particle concentration, functionalized surfaces) and theenvironmental transport of NPs (Jaisi et al., 2008; Liu et al.,2009; Tong et al., 2010; Godinez and Darnault, 2011; Wanget al., 2012). Properties of AgNPs (e.g., surface modification)and the interacting matrices will subsequently influence thedissolution, aging, and transformation (e.g., into silver sulfide
NPs) of AgNPs that are released into the environment (Bennand Westerhoff, 2008; Geranio et al., 2009; Kim et al., 2010;
Nowack, 2010; Cornelis et al., 2012; Coutris et al., 2012; Levard
et al., 2012; Li and Lenhart, 2012).
Filtration theory (Yao et al., 1971) has commonly been
employed to predict the influence of physicochemical factors
on the mass transfer of NPs to collector surfaces (Jaisi et al.,2008; Liu et al., 2009; Tian et al., 2010). However, deviations
have frequently been reported between filtration theory predictions and experimental observations for both NPs andcolloids (Adamczyk et al., 1995; Ko and Elimelech, 2000;
Tufenkji and Elimelech, 2005). For example, filtration theory
predicts an exponential distribution of retained NPs with
distance, whereas hyperexponential, uniform, and nonmonotonic retention profiles (RPs) have been commonly reported (Li et al., 2004; Li and Johnson, 2005; Tong et al., 2005;
Bradford et al., 2006). A wide variety of factors have been
demonstrated to contribute to deviations of RPs between
filtration theory predictions and experimental observations
including: heterogeneity of the particle and collector surfaces(Tufenkji and Elimelech, 2005), roughness (Kretzschmar et al.,1997), the presence of stabilizing agents (Wang et al., 2012),

2573

straining (Li et al., 2004), aggregation (Bradford et al., 2006),
and hydrodynamic drag (Li and Johnson, 2005; Johnson et al.,2007). Most previous research has been directed to explain theoccurrence and causes of hyperexponential RPs, whereas
relatively little attention has been directed at studying nonmonotonic RPs (Tong et al., 2005; Yuan and Shapiro, 2011). To
date, no information on RPs for AgNPs has been reported in
the literature. The retention of NPs will determine their longterm transport potential, thus information on RPs is essential
to predict their fate (Chowdhury et al., 2011).
Filtration theory employs an empirical sticking efficiency(a) to account for the fraction of the NPs that is immobilized on
the collector surface. Colloid immobilization is implicitly
assumed to occur without further mobilization. In reality, onlya small portion of the collector surface may contribute to NP
retention under unfavorable attachment conditions (Bradford
et al., 2009), and this “favorable” area can change with solution
chemistry and velocity (Adamczyk et al., 1995; Ko andElimelech, 2000). Enhanced retention of colloids and/or NPs
occurs at locations associated with surface roughness,grainegrain contacts, and locations of chemical heterogeneities because of lower velocities or increased adhesion (Liet al., 2005; Wang et al., 2011). Particles in unfavorable regions can be slowly transported along the collector surface byhydrodynamic forces to these “favorable” locations (Li et al.,2005; Xu et al., 2008). Particle retention in “favorable” locations will decrease over time as these locations are blocked orfilled (Ko and Elimelech, 2000). The rate of filling of these“favorable” locations will depend on the particle concentration. Higher particle concentrations will fill these “favorable”
locations more rapidly and thereby increase their transportpotential (Bradford et al., 2009; Chowdhury et al., 2011).
The objective of this study is to improve our understanding
and ability to simulate the transport and retention of stabilized AgNPs in subsurface environments. This information isneeded to assess the risk of groundwater contamination andto improve process descriptions in mathematical models.
Saturated packed column experiments were conducted undera variety of ionic strengths, grain sizes, NP concentrations,and flow velocities to systematically investigate physicochemical factors that influence AgNP transport. The collected
data and modeling provides valuable insight on the evolutionof RP shape for AgNPs and other particles (NPs, colloids, andmicroorganisms) in porous media, transitioning from hyperexponential to nonmonotonic, and to uniform RPs.

2.

Materials and methods

2.1.

Solution chemistry and porous media

Electrolyte solutions were made using Milli-Q water and KNO3.
Three solution ionic strengths (IS) were considered, namely: 1,
2.5, and 5 mM KNO3. These electrolyte solutions were unbuffered, and the pH of influent and effluent ranged from 6 to 7
during the course of the column transport experiments.
Three sizes of quartz sands were employed in the column
experiments. The median grain size of these sands are 240,350 (Quarzwerke GmbH, Germany) and 607 mm (Teco-Sil, CE
Minerals Greenville, USA). The sands are reported to consist of

2574

w a t e r r e s e a r c h 4 7 ( 2 0 1 3 ) 2 5 7 2 e2 5 8 2

at least 99.7% SiO2 (quartz) and trace amounts of metal oxides.
Additional purification procedures that are described in the
Supplementary Information (S1) were applied to control themetal oxide content and organic impurities in the sands,
which can limit or enhance NP transport (Lin et al., 2011; Thioet al., 2012). The surface charge characteristics of the clean
sand were determined using a Nano-Zetasizer apparatus(Malvern ZetaSizer 4) after it had been crushed to a powder
and placed in a selected electrolyte solution.

2.2.

AgNPs

AgNP suspensions (10.16% w/w) were produced by chemical
precipitation of silver nitrate using an aqueous reduction
method (AgPUREä, rent a scientistÒ GmbH, Germany). The
AgNPs were modified using mixture of two stabilizers, 4% w/w
each of Polyoxyethylene Glycerol Trioleate and Polyoxyethylene (20) Sorbitan mono-Laurat (Tween 20) (Figure S1, in
the Supplementary Information). The total unbounded/freesurfactant was around 5% in the original concentrated AgNPs
suspension when measured after ultracentrifugation for 6 h.
Both of these surfactants are non-ionic and form steric
repulsion barriers between AgNPs that help to stabilize thesuspension and minimize aggregation. This product corresponds to the OECD reference material NM-300 Silver
currently being used for nanomaterial research. Release of
Agþ from the NM-300 product employed in our study andother research (Kaegi et al., 2011; Klein et al., 2011) was lessthan 1% of the total mass over three days, which is was much
longer than the experiments in this study (<20 h).
The size and morphology of dry AgNPs were measured
using a scanning electron microscope (Gemini 1550 VP, Carl
Zeiss, Jena, Germany) and a transmission electron microscope(Philips CM 200 FEG). The suspension for each experiment was
freshly prepared (to minimize the dissolution of AgNPs) by
dilution of the concentrated stock suspension of stabilized
AgNPs into selected electrolyte solutions to achieve approximate concentrations of 10, 5, and 1 mg L1 and then sonicatedfor 15 min in a sonication bath. The final amount of surfactant
in the suspension was less than 1  103 % (W/W) and was
proportional to the AgNP concentration. To investigate thepresence of free surfactant molecules on AgNPs retention,
additional experiments were conducted by adding 10 or
30 mg L1 of the surfactants in the AgNP suspension. The
added surfactants were the same as the original surface
coating, a mixture of polyoxyethylene glycerol trioleate andTween 20 at equal concentrations. The Nano-Zetasizer apparatus was also used to determine the size distribution as afunction of time and the electrophoretic mobility (EPM) of the
AgNPs in different KNO3 solutions.

2.3.

Extended DLVO calculations

Extended DLVO (XDLVO) theory was used to better interpret
AgNP deposition in porous media. This theory considers the
DLVO interaction energy (sum of electrostatic and van der
Waals interactions) and two additional repulsive potentials
arising from osmotic (Vosm) and elastic (Velas) interactions fromthe surface coating. The interaction energy barrier calculation
is presented in the Supplementary Information (S2).

2.4.

Column experiments

Saturated transport experiments were performed in stainlesssteel columns (3 cm inner diameter, 12 cm length) that were
wet-packed with quartz sand. A hydrophilic nylon membrane,
which was supported by a steel plate, was used as a capillarybarrier and filter at the bottom and top of the column. During
packing, the column was vibrated to minimize air entrapment
and to ensure homogenous packing. A peristaltic pump wasused to inject solutions at a steady Darcy velocity(q ¼ 0.03e0.7 cm min1) through the column in an upflow
mode. A three-way valve was used to switch flow to the column between a D2O tracer in electrolyte solution, AgNPs
suspensions, and particle-free electrolyte solutions.
Before initiating a transport experiment, the packed column was conditioned with around 50 pore volumes of KNO3
solution. A nonreactive tracer experiment was then conducted by injecting 90 mL of D2O into the column. The same
electrolyte solution and injection velocity were used in D2O
and NP transport experiments in a given column. Column
effluent samples (2 mL each) were collected continuously inultra high performance plastic centrifuge tubes (VWR International GmbH, Germany) using a fraction collector. Tracer
D2O concentrations were quantified by high-performanceliquid chromatography (D-7000 HPLC, High-Technologies
Corporation, Japan) with a RI detector L-2490. The concentrations of D2O were evaluated using a calibration curve between the peak area of the RI signal and standard solutions(correlation coefficients were greater than 0.999). The column
pore water velocity and dispersivity were obtained by fitting
D2O breakthrough curves (BTCs) to the one-dimensional formof the advective-dispersive transport equation using the
CXTFIT code (Toride et al., 1999).Table 1 provides a summaryof the column transport properties for all experiments.
After the tracer experiment, a 90 mL pulse of AgNP suspension was injected into the column, followed by flushingwith several pore volumes of particle-free electrolyte solution.
The electrolyte concentration and flow rate were kept constant during a given experiment. Effluent concentrations of
AgNPs were determined from Ag concentrations that were
determined by inductively coupled plasma mass spectrometry (ICP-MS, Agilent 7500ce). In brief, AgNPs were treated by
15% HNO3 to dissolve the particles and to diminish sorption
onto the sampling tubes. Solutions were spiked with Rh solution (1 mg L1) as an internal standard, and diluted asneeded before analysis by ICP-MS. Each sample was measured
three times and the average value was taken for analysis.
After completion of the transport experiments, the porous
medium was carefully excavated in 1 cm increments (12layers). The sand was freeze dried and then digested by HNO3.
The Ag concentration in the HNO3 digest was again determined by ICP-MS. The AgNPs RPs were subsequently determined from this information and the measured dry mass ofsand in each increment.

2.5.

Theory and model 
Version 4.14 of the HYDRUS-1D computer code (Simunek et al.,2008) was used to simulate the transport and retention of stabilized AgNPs in column experiments conducted under various

2575

w a t e r r e s e a r c h 4 7 ( 2 0 1 3 ) 2 5 7 2 e2 5 8 2

Table 1 e Experimental parameters and the mass recovery for column experiments.
d50 mmFig. 1

Fig. 2

Fig. 3Fig. S3

Fig. 4

Fig. 5

240
350
607
607
607
607
607
607
607
350
350
350
607
607
607
607
607
607
607

Co mg L1

10
10
10
10
5
1
10
10
10
10
10
10
10
10
10
10
10
10a
10b

IS mM

1
1
1
1
1
1
5
2.5
1
5
2.5
1
1
1
1
1
5
5
5

q cm min1

0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.03
0.14
0.35
0.7
0.7
0.7
0.7

l cm

0.055
0.049
0.056
0.056
0.077
0.089
0.097
0.064
0.056
0.038
0.044
0.049
0.110
0.045
0.080
0.056
0.097
0.070
0.057

4

0.353
0.403
0.418
0.418
0.429
0.396
0.403
0.388
0.418
0.369
0.388
0.403
0.427
0.393
0.415
0.418
0.403
0.411
0.428

Recovery, %
Meff

Msand

Mtotal

13.9
41.5
45.6
45.6
43.1
17.4
4.6
21.8
45.6
5.6
11.1
41.5
24.9
27.0
40.1
45.6
4.6
4.5
5.2

115.8
52.0
54.1
54.1
44.8
92.5
103.5
75.6
54.1
101.4
81.0
52.0
73.2
73.2
64.0
54.1
103.5
91.6
96.8

129.7
93.5
99.7
99.7
87.9
109.9
108.1
97.4
99.7
107.0
92.1
93.5
98.1
100.2
104.1
99.7
108.1
96.1
102.0

Fig. 1: grain size effect; Fig. 2: concentration effect; Fig. 3 and Fig. S3: ionic strength effect; Fig. 4: flow rate effect; Fig. 5: surfactant effect; 10a,
10 mg L1 of AgNPs and 10 mg L1 additional surfactant; 10b, 10 mg L1 of AgNPs and 30 mg L1 additional surfactant. d50, grain size; Co, AgNP
input concentration; IS, ionic strength; q, Darcy velocity; l, dispersivity; 4, porosity; Meff, Msand, and Mtotal are mass percentages recovered from
effluent, sand, and total, respectively. The value of l was obtained by fitting tracer BTC, 4 was determined gravimetrically.

physicochemical conditions. The aqueous and solid phasemass balance equations for AgNPs are given in this model as:vðqw CÞ
v
vC
vðqCÞ
¼
qw D

 qw jk1 C
vt
vz
vz
vz

(1)

vðrb SÞ
¼ qw jk1 C
vt(2)

where qw[e] is the volumetric water content, C[Nc L3, Nc and L
denote the number of NPs and units of length, respectively] is
the aqueous phase AgNP concentration, t is time [T, T denotes
time units], z [L] is the distance from the column inlet, D[L2T1] is the hydrodynamic dispersion coefficient, q [L T1] isthe Darcy water flux, j [e] is a dimensionless function to account for time- and depth-dependent blocking, k1 [T1] is thefirst-order retention coefficient, rb [M L3, M denote units ofmass] is the soil bulk density, and S [Nc M1] is the solid phase
AgNP concentration. The first and second terms on the righthand side of Equation (1) account for dispersive and advective
transport of AgNPs, whereas the third term is used to describe
NP retention on the solid phase. The pore water velocity anddispersivity values in the AgNP transport simulations werebased on values determined from the conservative tracer
experiments.
When j ¼ 1, an exponential distribution of retained AgNPs
is predicted with depth. Conversely, j can be less than 1 when
it is given as (Bradford et al., 2006):
j¼


b

S
d50 þ z
1
Smax
d50

of the retention coefficient), and Smax [Nc M1] is the maximum
solid phase concentration of deposited AgNPs. The first term on
the right side of Equation (3) accounts for time-dependent
blocking/filling of retention sites using a Langmuirian
approach (Deshpande and Shonnard, 1999). This blocking term
implies that retention decreases with time and that the retention profile becomes uniform with depth as S approaches Smax.
The second term on the right side of Equation (3) describes
depth-dependent retention (e.g., a decreasing retention ratewith depth). When b ¼ 0, this term equals 1 and an exponential
distribution of retained AgNPs is predicted with depth similar toconventional filtration theory. Conversely, when b > 0 is
employed, then the retention profile of AgNPs exhibits a uniform or hyperexponential shape (e.g., a higher deposition rate
close to the column inlet). In this study, a value of b ¼ 0.432 was
employed based on information presented in the literature(Bradford et al., 2003). The AgNP retention model parameters (k1and Smax) were determined by simultaneously inverse fitting toexperimental BTC and RP data using the LevenbergeMarquardt
nonlinear least squares optimization algorithm (Marquardt,
1963) in HYDRUS-1D. It should be mentioned that Equations(1)e(3) cannot account for nonmonotonic retention profiles (apeak in retention down gradient from the column inlet) thathave sometimes been reported (Li and Johnson, 2005; Tonget al., 2005; Bradford et al., 2006).

3.

Results and discussion

3.1.

Characterization of AgNPs(3)

where d50 [L] is the median grain size of the porous medium, b[e] is an empirical parameter controlling the shape of thespatial distribution of retained NPs (i.e., the depth dependence

Figure S2 presents SEM and TEM images of the stabilized
AgNPs. These images indicate that the AgNPs were spherical

2576

w a t e r r e s e a r c h 4 7 ( 2 0 1 3 ) 2 5 7 2 e2 5 8 2

C/Co

607 µmfit - 607 µm
350 µmfit - 350 µm
240 µmfit - 240 µm

Pore volume

Depth [cm]in shape and had a very narrow size distribution with around
99% of the diameters in the size range of 15e20 nm. The hydrodynamic diameter (dp) of the AgNPs was measured by DLS
to range from 45.1  4.5 to 60.7  14.9 nm when the IS was
1e5 mM and the particle concentration was 10 mg L1. Measurements of dp taken over a 24 h period demonstrated that
the AgNP suspensions were very stable. Similar size results
have been reported in the literature (Klein et al., 2011).
The core surface potentials of the AgNPs in 1, 2.5, and5 mM KNO3 solutions that were predicted by soft particle
theory (Ohshima, 2005) and measurements of electrophoretic
mobility are summarized in Table 2. Core surface potentials of
AgNPs and clean sand were always negative and did not varymuch over the considered range of IS, pH, and Co (Table 2). The
zeta potential of the clean sands was much more negatively
charged than that for the AgNPs.
Measured and predicted surface potential information
given in Table 2 were used to determine the AgNPeSand and
AgNPeAgNP interaction energies based on XDLVO theory. The
maximum van der Waals and electrostatic double layerinteraction energy, (VvdW þ Ved)max, predicted for AgNPesandand AgNPeAgNP slightly decreased with increasing IS (Table
2). A shallow secondary minimum was predicted for both
AgNPesand and AgNPeAgNP interactions. These energies
were very weak compared to steric repulsion formed by theadsorbed surfactant layer. Steric interactions arising from thesurfactants on the AgNPs contributed significantly to the totalenergy barriers. Consequently, unfavorable conditions werepredicted for AgNP retention and aggregation. However, it
should be mentioned that these calculations only consideredmean properties of the AgNPs and sand, and do not accountfor the roles of physical or chemical heterogeneity andgrainegrain contacts on AgNP retention. Furthermore, thesurfactant coating on AgNPs can also attach to collector surfaces (Lin et al., 2012).

607 µmfit - 607 µm
350 µmfit - 350 µm
240 µmfit - 240 µm

S/Co [cm3 g-1]
3.2.

Grain size

In order to investigate the influence of grain size on AgNP
mobility, column experiments were conducted in 607, 350,
and 240 mm sands when IS ¼ 1 mM, q ¼ 0.7 cm min1, andCo ¼ 10 mg L1. Experimental conditions and the corresponding mass balance information for the BTC, RP, and total
column are presented in Table 1. The total column mass balance was >93% and this provides a high degree of confidence
in our experimental procedures and protocols. The BTCs areplotted as the normalized effluent concentrations (C/Co)
versus pore volumes. The RPs are plotted as the normalized

Fig. 1 e Grain size effect: Observed and fitted breakthroughcurves (a) and retention profiles (b) of AgNPs in 240, 350
and 607 mm sands, respectively. Other experimental
conditions were the same: Electrolyte, 1 mM KNO3; input
concentration, 10 mg LL1 AgNPs; Darcy velocity, 0.7 cm/
min.

solid phase concentration (S/Co) as a function of distance fromthe column inlet. Observed and simulated BTCs and RPs areshown in Fig. 1. The model simulation successfully captured
the gradual ascent trend of the BTCs, and the shape of the RPs.

Table 2 e Extended DLVO parameters of stabilized AgNPs and quartz sand.
IS mM dp nm za mV zb mV (Vvdw þ Ved)max NPesand kT (Vvdw þ Ved)max NPeNP kT VTmax NPeNP kT VTmax NPeSand kT
1
2.5
5

45.1
48.2
60.7

16.9
7.5
3.2

55.0
55.4
55.3

6.31
1.40
0.31

1.07
<0
<0

1.71  104
1.84  104
2.30  104

2.16  103
2.29  103
2.89  103

IS, ionic strength; dp, diameter of AgNPs; za, predicted core surface potential of AgNPs; zb, measured zeta potential of quartz sand;(Vvdw þ Ved)max, maximum energy of van der Waals (VvdW) and electrostatic double layer interaction (Ved);VTmax, maximum of the total XDLVO
energy. NPeNP and NPeSand, the interaction between two nanoparticles and between nanoparticle and grain surface, respectively.

2577

w a t e r r e s e a r c h 4 7 ( 2 0 1 3 ) 2 5 7 2 e2 5 8 2Lower normalized effluent concentrations occur in the
BTCs with decreasing sand size (Fig. 1a), especially between the
350 and 240 mm sands. The amount of AgNP retention shown in
the RPs (Fig. 1b) was inversely related to their BTCs. Consistent
with these observations, fitted values of k1 increased withdecreasing grain size (Table 3). Filtration theory provides an
explanation because it predicts that the mass transfer rate to
the grain surface increases with decreasing grain size (Yaoet al., 1971). However, the shape of the BTCs and RPs was also
sensitive to the grain size. This can be explained in part bytime-dependent retention processes associated with blocking.
Fitted values of Smax/Co increased with decreasing grain size
(Table 3) because of its larger surface area. The retention rate
declines as retention sites are increasingly occupied by AgNPs(Equation (3)). Consequently, smaller values of Smax/Co in thecoarser sands (350 and 607 mm) produce more asymmetric
BTCs (Fig. 1a) and more uniform RPs (Fig. 1b) because ofstronger blocking (Bradford et al., 2006). The smallest 240 mm
sand produced a hyperexponential RP when blocking is lessimportant. In this case, straining is unlikely because theextremely small ratio of dp/d50 is much lower than the reported
threshold for straining (Bradford et al., 2002; Li et al., 2004).
Alternatively, variations in the pore-scale velocity can provide
a viable explanation for this behavior (Bradford et al., 2011a,
2011b). Specifically, hyperexponential RPs occur when the flux
adjacent to the solid surface at the column inlet is the dominant mass transfer mechanism of NPs to the grain surface(Bradford et al., 2011a). Pore-scale water flow simulations
demonstrate that this flux increases as the grain size decreases(Bradford et al., 2011a).

3.3.

Input concentration

Additional transport experiments were conducted to demonstrate the influence of time-dependent retention on AgNP
BTCs and RPs. Fig. 2 presents observed and simulated BTCs
and RPs for AgNPs in 607 mm sand when the IS ¼ 1 mM KNO3,

q ¼ 0.7 cm min1, and Co ¼ 1, 5, and 10 mg L1. The normalized
BTCs were sensitive to the value of Co. Higher Co produced
greater recovery of AgNPs in the effluent and more asymmetric BTCs. The amount of AgNPs in the RPs was inversely
related to the BTCs, and the shape of the RPs systematicallyvaried with Co. In particular, the RP was hyperexponential
when Co ¼ 1 mg L1, was nonmonotonic when Co ¼ 5 mg L1,and approached a uniform distribution when Co ¼ 10 mg L1.
The model was able to simulate well the BTCs and most of
the RPs in Fig. 2 (R2 > 0.96). The value of k1 increased withdecreasing Co (Table 3). Consequently, this observation suggests that repulsive AgNPeAgNP interactions between mobile
and retained NPs hampered AgNP retention on the grainsurface at higher values of Co. Note that the number of
AgNPeAgNP collisions is expected to be proportional to Co.
The hyperexponential RP shape at lower values of
Co ¼ 1 mg L1 is attributed to pore-scale hydrodynamic at thecolumn inlet as discussed in the previous section. Observed
differences in BTCs and RPs with Co indicate that timedependent blocking diminished retention with time
(Bradford et al., 2009). Furthermore, values of Smax/Co also
increased with decreasing Co (Table 3). In particular, the BTCs
were more asymmetric (Fig. 2a) and the RPs were more uniform (Fig. 2b) at a higher Co because Smax filled more rapidly.
The observed transition in RP shape from hyperexponential,
to nonmonotonic, and to uniform with increases in Co indicates that blocking contributed to the development of nonmonotonic RPs. However, the model did not capture thenonmonotonic RP shape for the Co ¼ 5 mg L1 data. Consequently, other factors were also involved in the developmentof the nonmonotonic RP shape. Potential explanations forthese observations will be explored below.

3.4.

Ionic strength

Transport experiments were conducted at several IS to better
understand the nature of the adhesive interaction influencing

Table 3 e Fitted values (k1 and Smax/Co) of AgNP transport and retention under various experimental conditions.
d50 mm Co mg L1 IS mM q cm min1 k1 min1 Standard error k1 Smax/Co cm3 g1 Standard error Smax/Co
Fig. 1

Fig. 2

Fig. 3Fig. S3

Fig. 4

240
350
607
607
607
607
607
607
607
350
350
350
607
607
607
607

10
10
10
10
5
1
10
10
10
10
10
10
10
10
10
10

1
1
1
1
1
1
5
2.5
1
5
2.5
1
1
1
1
1

0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.03
0.14
0.35
0.7

3.417
1.182
0.855
0.855
1.018
1.683
3.038
1.422
0.855
3.378
2.859
1.182
0.058
0.356
0.390
0.855

0.191
0.020
0.029
0.029
0.047
0.087
0.095
0.033
0.029
0.222
0.102
0.020
0.001
0.013
0.021
0.029

5.320
0.754
0.732
0.732
1.679
6.949
4.439
3.593
0.732
23.012
3.482
0.754
1.532
1.095
2.017
0.732

1.266
0.023
0.027
0.027
0.219
2.524
0.816
0.749
0.027
6.456
0.751
0.023
0.067
0.048
0.411
0.027

R2
0.979
0.969
0.963
0.963
0.975
0.979
0.969
0.962
0.963
0.982
0.970
0.969
0.952
0.970
0.956
0.963

Fig. 1: grain size effect; Fig. 2: concentration effect; Fig. 3 and Fig. S3: ionic strength effect; Fig. 4: flow rate effect. d50, grain size; Co, AgNP input
concentration; IS, ionic strength; q, Darcy velocity; k1, the first-order retention coefficient; Smax/Co, normalized maximum solid phase concentration of deposited NPs; R2, Pearson’s correlation coefficient.

2578

w a t e r r e s e a r c h 4 7 ( 2 0 1 3 ) 2 5 7 2 e2 5 8 2

1.0

0.6

C/Co

C/Co

1 mM
fit - 1 mM
2.5 mMfit - 2.5 mM
5 mM
fit - 5 mM

0.8

10 mg/L
fit - 10 mg/L
5 mg/Lfit - 5 mg/L
1 mg/Lfit - 1 mg/L

0.4

0.2

0.0
0

1

2

Pore volume
1

2

3

4

0

5

0

0

-2

-2

-4

-4

-6
10 mg/L
fit - 10 mg/L
5 mg/Lfit - 5 mg/L
1 mg/Lfit - 1 mg/L

-8

-10

-12

S/Co [cm3 g-1]
Fig. 2 e Concentration effect: Observed and fitted
breakthrough curves (a) and retention profiles (b) of AgNPs
under AgNP input concentrations of 1, 5, and 10 mg LL1,
respectively. Other experimental conditions were thesame: Grain size, 607 mm; electrolyte, 1 mM KNO3; Darcy
velocity, 0.7 cm/min.

AgNP retention. Fig. 3 presents observed and simulated BTCs
and RPs of AgNPs in 607 mm quartz sand whenq ¼ 0.7 cm min1, Co ¼ 10 mg L1, and IS ¼ 1, 2.5, and 5 mM
KNO3. Figure S3 provides similar information but in 350 mm
quartz sand. For a given sand size, increases in IS produced asignificant decline of AgNPs transport and a corresponding
increase in retention. This observed trend is consistent withcompression of the electrostatic double layer surrounding thecollectors at higher IS as reported in previous studies (Jaisi
et al., 2008; Lin et al., 2011). The BTCs were well describedusing the model (R2 > 0.96), whereas the nonmonotonic RP
was not accurately simulated.
The sensitivity of BTCs and RPs to IS provides further
insight on AgNP retention. BTCs were more asymmetric at the

Depth [cm]Depth [cm]

0

3

4

5

6

Pore volume
1

2

3

4

5

-6
1 mM
fit - 1 mM

-8

2.5 mMfit - 2.5 mM

-10

5 mM
fit - 5 mM

-12

S/Co [cm3 g-1]
Fig. 3 e Ionic strength effect: Observed and fitted
breakthrough curves (a) and retention profiles (b) of AgNPs
under 1, 2.5 and 5 mM KNO3, respectively. Other
experimental conditions were the same: Grain size,
607 mm; input concentration, 10 mg LL1 AgNPs; Darcy
velocity, 0.7 cm/min.

lowest IS ¼ 1 mM and RPs were nonmonotonic when the IS
equaled 2.5 or 5 mM. The increase of IS from 1 to 5 mM
resulted in an increase of nearly 4-fold on k1 and around 6-foldon Smax (Table 3). Consequently, BTCs were asymmetric andRPs were uniform with depth when IS ¼ 1 mM because thesmaller Smax was filled rapidly. Conversely, when the IS ¼ 2.5
and 5 mM, values of Smax were larger, and the RPs were nonmonotonic because it took longer to fill these regions of thesolid surface. Similar behavior occurred at higher IS and in thefiner textured 350 mm sand, but in this case the value of Smax
and the filling times were even larger. The nonmonotonic RPs
were therefore more pronounced in the finer sand and at
higher IS (2.5 and 5 mM) conditions.
XDLVO calculations predict a shallow secondary minima
and significant energy barriers to attachment under these IS

2579

w a t e r r e s e a r c h 4 7 ( 2 0 1 3 ) 2 5 7 2 e2 5 8 2

0.7 cm/min

0.8

fit - 0.7 cm/min
0.35 cm/min

0.6

fit - 0.35 cm/min
0.14 cm/min
fit - 0.14 cm/min

0.4

0.03 cm/min
fit - 0.03 cm/min

0.2

0.0
0

1

2

3

4

5

6

Pore volume
0

1

2

3

0

-2

Flow velocity

A series of experiments were performed under various flow
velocities in 607 mm sand at an IS ¼ 1 mM KNO3 to further
investigate the influence of system hydrodynamics on AgNP
fate. Our results demonstrated an increased mass removal of
AgNPs with decreasing flow rate (Fig. 4). Higher effluent concentrations and significantly less retention occurred when theflow velocity increased from 0.03 to 0.7 cm min1. The RPs
were nonmonotonic when Darcy velocities were 0.03, 0.14,
and 0.35 cm min1.
Hydrodynamics may affect AgNPs transport and retention
in 3 ways by influencing: (i) the rate of mass transfer from thebulk phase to the solid phase (Yao et al., 1971); (ii) the value of
Smax that is associated with a secondary minimum (Li et al.,2005; Torkzaban et al., 2008); and (iii) the migration rate ofparticles adjacent to the solid phase (Ko and Elimelech, 2000;
Bradford et al., 2011a, 2011b). Each of these factors will bebriefly discussed below. In filtration theory, physicoechemicalinteractions are assumed to control the rate of AgNP masstransfer to the solid surface. The values of k1obtained from themodel all increased with increasing velocity (Table 3),
consistent with expected trends for the bulk mass transfer
rate predicted by filtration theory (Yao et al., 1971; Logan et al.,1995). Conversely, a systematic decrease in Smax withincreasing velocity was not observed (Smax/Co ranged from0.732 to 2.017 cm3 g1) as would be predicted from torquebalance considerations for AgNPs interacting by a secondary
minimum. However, Smax was found to be lowest at thehighest velocity. These observations highlight the relatively
insignificant role of secondary minimum interactions for
AgNPs and the importance of primary minimum interactions
as a result of physical and/or chemical heterogeneity.
Furthermore, systematic trends in the RP shape with velocity
were not very apparent in Fig. 4b as would be expected if solid
phase AgNP migration occurred over long distances. Consequently, the amount of retention in the RPs (Fig. 4b) was

Depth [cm]

3.5.

1.0

C/Co

conditions (Table 2). An additional experiment at IS ¼ 5 mM
was conducted to verify the role of the secondary minima.
After application of AgNPs and rinsing with backgroundparticle-free electrolyte solution (around 8 pore volumes intotal), the column was flushed with several pore volumes ofMilli Q water to expand the double layer thickness and toeliminate the secondary minimum (Figure S4). A pulse of
AgNPs was released into the effluent (C/Co was approximately
0.4) when the secondary minimum was eliminated. However,integration of this pulse indicated that it only accounted foraround 7% of the retained mass. This observation suggeststhat most of the retained AgNP mass was interacting in a
primary minimum as a result of physical and/or chemical
heterogeneity. Values of k1 and Smax mainly determine theamount of AgNP retention, and both these parameters were
lowest at IS of 1 mM. The effect of microscopic heterogeneity
on k1 and Smax has been reported to increase with IS becauseof a smaller zone of electrostatic influence when the double
layer is compressed (Torkzaban et al., 2008). Consequently,
increasing values of k1 and Smax with IS (Table 3) are therefore
expected to largely reflect the effects of microscopic
heterogeneity.

-4
0.7 cm/min-6

fit - 0.7 cm/min
0.35 cm/min-8

fit - 0.35 cm/min
0.14 cm/min
Fit - 0.14 cm/min-100.03 cm/min
fit - 0.03 cm/min-12

S/Co [cm3 g-1]
Fig. 4 e Flow rate effect: Observed and fitted breakthroughcurves (a) and retention profiles (b) of AgNPs under Darcy
velocities of 0.03, 0.14, 0.35, and 0.7 cm/min, respectively.
Other conditions were the same: Grain size, 607 mm;
electrolyte, 1 mM KNO3; input concentration, 10 mg LL1
AgNPs.

primarily determined by differences in the bulk phase masstransfer rate, and the shape of the RPs was mainly influencedby blocking of Smax arising from microscopic heterogeneities.

3.6.

Nonmonotonic RPs

The above information indicated that the nonmonotonic RPs
were strongly linked to blocking behavior. One potential
explanation is due to the free surfactants (previously exist ordesorption from the stabilized AgNPs) that subsequently
adsorbed onto porous media. The RP shape may be altered bysurfactant and NP competition for attachment sites that diminishes Smax (Lin et al., 2012; Wang et al., 2012). Additionaltransport experiments with AgNPs in the presence of 10 and2580

w a t e r r e s e a r c h 4 7 ( 2 0 1 3 ) 2 5 7 2 e2 5 8 230 mg L1 of surfactants were therefore conducted to test thishypothesis. Other experimental conditions were a grain sizeof 607 mm, an IS of 5 mM KNO3, a Darcy velocity equal to
0.7 cm min1, and Co ¼ 10 mg L1 AgNPs. The BTCs and RPs areshown in Fig. 5. Note that increasing the surfactant concentration had a relatively minor influence on the BTCs andproduced nearly the same recoveries from effluent (Table 3),
but a large effect on the nonmonotonic RPs. In particular,
higher surfactant concentrations were associated with nonmonotonic RPs having peak concentrations at greater distances. The decreasing amount of AgNPs retention near thecolumn inlet with increasing surfactant concentration istherefore likely a result of surfactant adsorption onto thegrain that fills the retention sites and then decreases Smax at

0.3

without additional surfactant
10 mg/L surfactant

0.2

C/Co

30 mg/L surfactant

0.1

0.0
0

1

2

3

4

5

6

4

5

Pore volume
0

1

2

3

0

this location. Consequently, the nonmonotonic RPs were
determined by the combined influence of time- and depthdependent retention (described by k1 and Smax), and the effect of surfactant adsorption on Smax. The surfactant effectalso provides a good explanation for the RPs showed in Fig. 2b.
The amount of surfactant in the solution is expected to be
proportional to Co, so the effects of surfactants on AgNP
retention are expected to increase with Co because blocking ismore important. In the case of nonmonotonic RPs in Figs. 3band 4b, the competitive retention of AgNPs and surfactants
are also likely to play an important role.

4.

Conclusions

The transport of AgNPs was enhanced with an increase inwater velocity, sand grain size, and AgNP input concentration,
and a decrease in solution ionic strength. AgNPs were found to
mainly interact in irreversible primary minimum as a result of
microscopic heterogeneity and partially in a reversible secondary minimum at higher IS. Consequently, the effects offlow velocity on AgNP retention were primarily controlled by
the rate of mass transfer to the solid surface. Significantly, theRPs were found to transition from hyperexponential, to nonmonotonic, and to uniform with depth due to the combined
effects of hydrodynamics near the column inlet and timedependent blocking of Smax that was influenced by theamounts of surfactants. In particular, the high sensitivity of
AgNP retention to the stabilizing agent indicates that artificial
stabilizers or natural occurring organic matter will likely
facilitate NP transport deeper into the subsurface environment and increase the potential risk of groundwatercontamination. Highly idealized systems (clean quartz sandand monovalent electrolyte solutions) were employed in thisresearch to better understand mechanisms of AgNP retention.
Additional research is warranted to fully address the transportcomplexities of natural environments.

-2

Acknowledgments

Depth [cm]

-4

-6

-8
without additional surfactant-10

10 mg/L surfactant
30 mg/L surfactant-12

S/Co [cm3 g-1]
Fig. 5 e Surfactant effect: Observed breakthrough curves (a)and retention profiles (b) of AgNPs without additionalsurfactant and with adding 10 or 30 mg LL1 surfactant in
the injected AgNPs suspension, respectively. Other
experimental conditions were the same: Grain size,
607 mm; electrolyte, 5 mM KNO3; input concentration,
10 mg LL1 AgNPs; Darcy velocity, 0.7 cm/min.

The first author thanks to the China Scholarship Council (CSC)
for financial support. This research was funded by the NanoFlow project supported by German Federal Ministry of Education and Research (BMBF). The authors would like toacknowledge the assistance of Astrid Zimmermann (ZCH,
Forschungszentrum Jülich GmbH, Germany) for ICP-MS measurement and Marc Heggen (PGI-5, and ER-C, Forschungszentrum Jülich GmbH, Jülich, Germany) for TEM
measurement. Thanks to Herbert Philipp for the technicalassistance and Stephan Köppchen for analysis of D2O samples. The authors also thank Dr. Roy Kasteel for his valuablesuggestions for modeling.

Appendix A. Supplementary data
Supplementary data related to this article can be found at
http://dx.doi.org/10.1016/j.watres.2013.02.025.

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