Measuring Nanoparticle Attachment Efficiency in Complex Systems

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Measuring nanoparticle attachment efficiency in complex systems Nicholas K. Geitner, Niall Joseph O'Brien, Amalia Turner, Enda Cummins, and Mark R. Wiesner Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b04612 • Publication Date (Web): 18 Oct 2017 Downloaded from http://pubs.acs.org on October 18, 2017

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Environmental Science & Technology

Measuring nanoparticle attachment efficiency in complex systems

Nicholas K. Geitner,1,3 Niall J. O’Brien,1,2 Amalia A. Turner,1,3 Enda J. Cummins,2 Mark R. Wiesner1,3* * Corresponding author ([email protected]) 1. Department of Civil and Environmental Engineering, Duke University, Durham, North Carolina 27708, United States 2. UCD School of Biosystems and Food Engineering, University College Dublin, Belfield, Dublin 4, Ireland 3. Center for the Environmental Implications of NanoTechnology (CEINT), Duke University, Durham, North Carolina 27708, United States

ACS Paragon Plus Environment


1

Abstract

2 3

As process-based environmental fate and transport models for engineered nanoparticles are

4

developed, there is a need for relevant and reliable measures of nanoparticle behaviour. The affinity

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of nanoparticles for various surfaces (α) is one such measure. Measurements of the affinity of

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nanoparticles obtained by flowing particles through a porous medium are constrained by the types

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of materials or exposure scenarios that can be configured into such column studies. Utilizing glass

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beads and kaolinite as model collector surfaces, we evaluate a previously developed mixing method

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for measuring nanoparticle attachment to environmental surfaces, and validate this method with an

10

equivalent static column system over a range of organic matter concentrations and ionic strengths.

11

We found that, while both impacted heteroaggregation rates in a predictable manner when varied

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individually, neither dominated when both parameters were varied. The theory behind observed

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nanoparticle heteroaggregation rates (αβB) to background particles in mixed systems is also

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experimentally validated, demonstrating both collision frequency (β) and background particle

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concentration (B) to be independent for use in fate modelling. We further examined the effects of

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collector particle composition (kaolinite vs glass beads) and nanoparticle surface chemistry (PVP,

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citrate, or humic acid) on α, and found a strong dependence on both.


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Introduction

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Assessing and understanding engineered nanomaterial exposure to dynamic environmental systems

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has been a major driver in the move from material flow analysis of nanomaterials1, 2 to process-

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based fate and transport models.3-5 Experimental assays are required to identify fundamental

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nanoparticle and environmental properties that impact nanoparticle fate and transport in the

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environment and quantify the reaction rates (dissolution, oxidation, sulfidation, heteroaggregation

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etc.) dependent on these properties.6

25

Nanoparticles may not reach equilibrium partitioning in the same manner or over the same time-

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scales as dissolved chemicals, and therefore standard tests for determining partitioning or

27

adsorption coefficients (e.g. Kow 7 and Kd 8) require special consideration for potential departure from

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thermodynamic expectations. 9, 10 A kinetic consideration of particle attachment to other particles

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(heteroaggregation)11 or to immobile surfaces (deposition), as has been developed in the colloid

30

science literature,12-18 can be adapted to describe the fate of nanomaterials in natural and

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engineered systems.11, 19 The attachment efficiency (α) of nanoparticles to another particle or

32

surface (collector), along with the nanoparticle-collector collision frequency (ß) and collector

33

concentration (B) describes key elements of nanoparticle characteristics and the system in which

34

nanoparticles are present. The attachment efficiency is therefore a functional or aggregate property,

35

not just of the nanoparticle in question, but of the entire system, including the nanoparticle, its

36

surface chemistry, the surfaces nanoparticles encounter, and the chemistry of the dispersing

37

medium.

38

Nanoparticle attachment efficiencies have been determined in column studies for collector media

39

such as silica (glass beads) and soil.19-21 In these systems, fluids containing nanoparticles pass

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through the column, contacting surfaces of the porous medium (collectors) with an efficiency (η0)

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over the column length (L).22 The range of background collector media that can be studied in this

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arrangement is limited, however, to solid phases that can be packed into a column and provide



43

sufficient porosity for flow to occur. The breakthrough curves obtained in these experiments provide

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information on the transport and attachment of nanomaterials through the packed column.

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Attachment efficiencies are calculated from these breakthrough curves in one of two ways: , (1)

46

applying theory that describes the physics of transport within the column, thereby isolating the

47

chemical effects expressed by α or (2) normalizing experimental results to a scenario in which every

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collision results in attachment, such that the factors affecting transport are cancelled out. Other

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methods for measuring the affinity of particles for an immobile surface, such as the quartz crystal

50

microbalance, 23 where the background of interest is present as a thin layer, as well as

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complementary confirmatory methods such as SEM and AFM24 are also largely limited to rigid

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materials and suffer from limitations in throughput.

53

Instances where the collector surfaces are mobile (heteroaggregation), highly complex, and/or

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otherwise incompatible with column studies, are frequently encountered in the context of complex

55

dynamic backgrounds such as wastewater sludge, suspended solids in surface waters, and soil

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particles. Multiple surfaces (e.g. bacteria, algae and clays) may also compete as sites for nanoparticle

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attachment. Theory for colloid aggregation provides us with a framework for considering

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nanoparticle interactions with such “background” particles where an overall attachment (or

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heteroaggregation) rate is determined by the product (αβB), consisting of the attachment efficiency

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(α), which largely describes the chemistry of attachment; the collision rate constant (β), which

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defines the physics of how particles approach collectors; and the collector particle concentration

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(B).25, 26 Each environment (e.g. river, lake, settling tank, etc.) is characterized in part by a value of

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βTOTAL, itself comprised of three principle collision mechanisms: Brownian diffusion (βBROWNIAN ),

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differential settling (βDIFFERENTIAL) and shear-induced collisions (βSHEAR).27

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The importance of heteroaggregation for nanoparticles in environmental systems has been

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repeatedly signalled in the literature.11, 28, 29 The complexity of heteroaggregation in environmental

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systems, which had impeded measurement of the attachment efficiency, has been addressed using a


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mixing method for measuring the “surface affinities” in previous work.11, 30 In the current work we

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validate the mixing method by comparing trends in attachment efficiencies to those obtained by the

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column method. We present measurements of α for PVP-coated silver (PVP-Ag) nanoparticles

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attaching to a mixed suspension of glass beads for a range of environmentally relevant conditions of

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ionic strength (IS) and organic matter (OM) and compare these values to those determined through

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standard column tests with glass beads under equivalent conditions of solution chemistry. We tested

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the influence of βTOTAL and B (as predicted by colloid theory) and their use as separate quantities that

75

can be calculated from theory. The mixing method is then applied to a matrix of IS and OM

76

conditions to further investigate the relative influence of these two parameters on attachment

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efficiency. Finally, we probe mechanisms of attachment by examining the influence of collector

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particle composition and nanoparticle surface chemistry on attachment efficiency.

79 80

Methods and materials

81

Materials

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The nanoparticles used in this study were polyvinylpyrrolidone-functionalised silver (PVP-Ag)

83

(diameter = 44.3 nm from TEM measurements) synthesized in-house at the Center for the

84

Environmental Implications of NanoTechnology (CEINT, Duke University, Durham, NC), with the

85

synthesis and characterisation of these particles previously described.31, 32 Humic acid (HA) was

86

Suwanee river humic acid, obtained from the International Humic Substance Society.

87

Similar collector surfaces in both experiments were selected to allow comparison of mixing and

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column methods under well-controlled conditions. Four size classes of spherical borosilicate glass

89

beads (GBs) were used as background or porous material in this study (all obtained from Potters

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Industries Inc. Berwyn, PA) with mean diameters of 2.2 ± 0.5 µm, 8 ± 2 µm, and 40 ± 10 µm for

91

mixing experiments and 360 µm for column experiments. To ensure comparable surfaces for



92

nanoparticle attachment, all glass beads were subjected to the same cleaning procedure in the same

93

background electrolyte as the subsequent mixing studies,33 in which GBs were washed in the

94

exposure medium and allowed to settle. The GBs were then decanted, and this washing procedure

95

was repeated at least 5 times.

96 97

α: Validating mixing method with column method

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Ionic strength

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To investigate the influence of ionic strength on PVP-Ag nanoparticle-GB heteroaggregation rate, 40

100

mL PVP-Ag nanoparticles (2 mg/L) were mixed in glass vials with 40 ± 10 µm GBs (22.4 g/L), as a

101

representative background particle, and 0.002 mg HA/mg Ag at 350 RPM (time averaged shear rate

102

23.7 s-1, calculated from Croughan et al;34 see Supporting Information) in ultra-pure water adjusted

103

to pH 7.8 with 0.1 N NaOH. The pH adjustment was necessary to avoid pH shifts during the

104

experiment in the presence of bare glass beads in the single-electrolyte system. The initial

105

concentrations of both PVP-Ag nanoparticles and 40-µm GBs were selected for ease of detection,

106

while still maintaining nanoparticle concentrations well below the concentration of background

107

particles. The attachment efficiency of nanoparticles onto background particles is independent of

108

these initial concentrations, so long as the background particle concentration (and associated

109

available surface area) is significantly greater than that of the nanoparticles. In this medium, PVP-Ag

110

nanoparticles possessed a zeta potential of –8.8 ± 2.3 mV (Malvern Instruments, ZetaSizer Nano ZS),

111

indicating a nearly neutrally charged surface coating. A time-series of 12 aliquots were taken (350 µl;

112

30 s – 1 hr) and the phases allowed to separate in small plastic vials for 30 s, over which time the GBs

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completely settled out of suspension along with all associated nanoparticles. The supernatant (200

114

µl) PVP-Ag nanoparticle concentration for each time point was then measured by UV spectrometry

115

(Thermo Multiskan MMC) at 400 nm. The lower detection limit of PVP-Ag nanoparticle


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concentration was determined to be 0.07 mg/L, with a resolution of 0.01 mg/L. Control supernatants

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in the absence of PVP-Ag nanoparticles were also measured, which displayed absorbance identical to

118

that of control water samples. The residual concentration of the PVP-Ag nanoparticles over time can

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be plotted such that log transformed data obtained from the initial phase of attachment can be

120

linearized and used to calculate the rate of nanoparticle removal due to attachment, αβB:11, 30

ln = . #1 121

The linear phase was identified as starting from 30 seconds after nanoparticle dosing and was cut off

122

based on optimization of the linear regression correlation coefficient. At least 5 time points were

123

used in each final fit.

124

To investigate the effect of ionic strength on attachment efficiency, a potassium nitrate (KNO3)

125

solution was added to the mixing vials. KNO3 is generally considered to be an indifferent electrolyte,

126

thereby avoiding confounding surface interactions with minerals and other collector particles

127

surfaces. The experiment was repeated with increasing levels of ionic strength of the mixture

128

(ranging from 0 to 10 mM) until the measured αβB did not increase further (i.e. α approached 1, or

129

all collisions resulted in attachment). Clean GBs in new glass vials were used for each replicate at

130

each of the ionic strengths tested, and each mixing study was performed in triplicate. Control

131

experiments without GBs were also performed to account for any reduction in suspended Ag

132

absorption (at 400 nm) due to homoäggregation, dissolution or sorption to mixing vessel. We further

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verified that any homoäggregation present in a given system was negligible compared to

134

heteroaggregation in the linear attachment phase for each condition, as done previously.30 Also note

135

from Eqn. 1 that the attachment efficiency is predicted to be independent of the nanoparticle initial

136

concentration.

137

The values of α obtained from the mixing method at various ionic strengths were then compared

138

with those obtained from column experiments using PVP-Ag nanoparticles (2 mg/L) and 360 ± 20 µm



139

GBs performed under equivalent conditions (see Supporting Information for details on column set-

140

up). The concentrations of PVP-Ag nanoparticles leaving the column were also measured by UV

141

spectrometry (Hitachi 2810) at 400 nm.

142

The fraction of nanoparticles leaving the column between approximately one and three pore

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volumes typically yields a plateau value, referred to as the clean bed removal, that is related to the

144

collector efficiency of a single GB (η0), the attachment efficiency (α), and the length of the column

145

(L), as described by equation 2:22

= − . #2

146

147

where dc is the collector diameter, ε is the column total porosity, and C/C0 is the column outlet

148

normalized particle concentration of the nanoparticle breakthrough curve, measured in this case

149

after two pore volumes. This experiment was repeated and the ionic strength of the mixture again

150

increased using KNO3 (0 → 10 mM) until the value of C/Co (or equivalently did not change

151

with further increases in ionic strength. Each column study was performed in triplicate, and each

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replicate performed with a fresh column initially free of Ag nanoparticles.

153

By assuming α equals 1 at the maximum observed value of αβB in the mixing studies and αη0L in the

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column experiments, the attachment efficiency (α) can be calculated in each case by dividing the

155

observed value of αβB or αη0L by the values of these quantities obtained when attachment

156

conditions are favourable (α=1, or every collision results in attachment), thereby eliminating the

157

need to calculate or measure B independently:

=

!"#$!%&'

=

!"#$!%&'

#3

158 159

Organic matter


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160

The influence of organic matter on the attachment rate of PVP-Ag nanoparticles to GBs was

161

investigated in experiments where the PVP-Ag nanoparticles were pre-mixed with HA for 5 minutes

162

before addition to the GB suspension. The ionic strength of the suspension was held at 5 mM KNO3

163

(i.e. α~1 for low HA concentrations) and the HA concentration was increased from zero to 0.4

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mg/mg Ag, the latter concentration observed to yield an attachment efficiency of approximately

165

zero in this system. HA concentrations were based on the weight of dry HA salt added as a fully

166

dissolved stock solution.

167

To validate the attachment efficiencies measured using the mixing method at increasing HA

168

concentration, column experiments using PVP-Ag nanoparticles (2 mg/L) and 360 ± 20 µm GBs were

169

performed under equivalent conditions as described previously. The HA concentration in the PVP-Ag

170

nanoparticle stock was again increased from zero to 0.4 mg/mg Ag, the latter concentration

171

observed to yield an attachment efficiency of approximately zero.

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Attachment efficiency was calculated at each HA concentration for both mixing and column

173

experiments as described for the ionic strength experiments.

174 175

β: Influence of mixing rate & comparison to modelled values

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PVP-Ag nanoparticles (2 mg/L) and GBs (22.4 g/L 40 ± 10 µm; 1 g/L 8 ± 2 µm; 0.1 g/L 2.2 ± 0.5 μm)

177

were mixed at varying mixing rates (60-480 RPM; shear rate 0.7 – 21.9 s-1) in independent mixing

178

studies to observe the effect of shear forces on the overall attachment rate (αβB). Experiments

179

were performed with three GB sizes to explore the effect of increasing shear force at different

180

background particle sizes. Concentrations of GBs were varied to ensure equal available surface area

181

across GB sizes. The ionic strength was held at 5mM KNO3 and [HA] at 0.002 mg/mg Ag (i.e. α~1) so

182

any variation in the attachment rate would be solely due to the collision rate constant (βTOTAL). A

183

time series of 12 aliquots (350 µl; 30 s – 1 hr) were taken, the phases separated either by



184

centrifugation (2.2-µm GBs – 15 s, 200 × *; 8-µm GBs – 15 s, 40 × *) or settling (40-µm GBs – 30 s)

185

in 1.5 mL centrifuge tubes and analysed by UV spectrophotometery as described previously. βTOTAL

186

was extracted from αβB by assuming a constant αB across all experiments. Values for βTOTAL were

187

also calculated for each mixing speed (shear rate) and background particle size (GBs) using the

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rectilinear model27 and compared to the measured values. The rectilinear model makes the

189

assumption that particles move in straight lines until a collision occurs.35 This model is frequently

190

selected for its computational efficiency and qualitative representation of overall trends, but it lacks

191

in quantitative accuracy. Because of the large number of assumptions that would be required to

192

produce more accurate calculations, the rectilinear model was considered satisfactory for the

193

purpose of the current work, which is primarily to compare trends. Further details are provided in

194

the Supporting Information.

195 196

B: Role of background concentration

197

To observe the effect of collector particle concentration (B) on measured attachment rate (αβB), the

198

40-µm GB concentration was varied (2.5, 5, 10, 20, 40, 80 g/L), while holding constant the

199

suspension ionic strength (5mM KNO3) and mixing speed (350 RPM; shear rate 14.4 s-1). Samples

200

were taken and analysed as described for the ionic strength experiments.

201 202

Roles of Collector Particle Composition and Nanoparticle Surface Chemistry

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To reveal the roles of collector particle composition and nanoparticle surface chemistry, the same 2

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mg/L PVP-Ag nanoparticles were introduced to 1 g/L kaolinite clay in ultra-pure water, pH 7.8. The

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mixing study solution contained 0.5 mM KNO3 and no additional HA. Further, we performed mixing

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studies on both 40 µm GBs and the above kaolinite clay together with 2 mg/L Ag nanoparticles with

207

citrate (Cit-Ag nanoparticles) and HA (HA-Ag nanoparticles) surface coatings. These particles were


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prepared using the same initial core material as used for PVP-Ag nanoparticles, but with the surface

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coating reducing agent replaced by sodium citrate. HA-Ag nanoparticles were prepared from Cit-Ag

210

nanoparticles by pre-incubating 20 mg/L nanoparticle solutions with 10 mg/L HA. In the above

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mixing solution buffer, the zeta potentials for Cit-Ag and HA-Ag nanoparticles were -38 ±4 and -32 ±6

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mV, respectively. Attachment efficiencies were derived as described above, with α=1 measurements

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made independently for GBs and kaolinite. Phase separation of GBs from free nanoparticles was

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performed as described above. Phase separation of kaolinite clays from a 750 µL aliquot was

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accomplished by 30 seconds of ultracentrifugation at 7000 × * in 1.5 mL centrifuge tubes.

216 217

Application: α heat-map

218

The combined effect of IS and OM on attachment efficiency was evaluated by measuring αβB using

219

the mixing method (2 mg/L PVP-Ag nanoparticles; 22.4 g/L 40 µm GB; shear rate 14.2 s-1) over an

220

environmentally relevant range of IS and humic acid (0.5-10 mM KNO3; 0.1-1.6 mg HA/mg Ag). α was

221

then calculated by dividing by the value of βB experimentally determined earlier under conditions

222

of favourable attachment ( → 1.

223 224 225

Results and Discussion α: Comparison between mixing with column methods

226

The attachment efficiencies (α) of PVP-Ag nanoparticles to GBs measured by the mixing method

227

closely followed the trends obtained from column experiments in equivalent conditions (Figure 1 a,

228

b), with a Pearson correlation coefficient of 0.989 between the two sets of results. The transition

229

from unfavourable (i.e. α=0, or no collisions result in attachment) to favourable heteroaggregation

230

regimes (i.e. α approaching 1, or every collision results in attachment) occurred over a relatively



231

narrow range of electrolyte concentration (critical coagulation concentration (CCC) ~ 5mM KNO3)

232

(Figure 1 a), while the transition from favourable (at the CCC) to unfavourable heteroaggregation

233

with increasing humic acid concentration also occurred over a relatively narrow HA concentration

234

range (0 → 0.4 mg/mg Ag) (Figure 1 b). The observed increase in PVP-Ag attachment at higher IS has

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been previously ascribed to a compression of the electric double layer of the particle and the only

236

slightly charged PVP coating, thereby enhancing polymer coating interactions with the uncoated

237

collector surface36 The decrease in attachment with increasing HA concentration has been explained

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by a mitigation of PVP attachment to GB surfaces through active site reduction and additional

239

electrosteric stabilization of the nanoparticles by HA. The lower values of attachment efficiency

240

determined by the mixing method relative the column studies at higher humic acid concentrations

241

(Figure 1b) are attributed to a greater extent of these HA interactions with nanoparticle surfaces.

242

Specifically, the lower relative surface area of GBs in mixing studies increases the probability of

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direct adsorption of HA onto Ag nanoparticle, as opposed to the more likely adsorption of HA onto

244

glass bead surfaces in column studies, which in many cases will be onto empty sites with no

245

nanoparticle adsorption. These HA interactions with nanoparticles are expected to lower attachment

246

efficiencies onto glass beads by providing additional electrosteric stabilization; this hypothesis is

247

examined further below.


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Attachment efficiency (α)

1.2

(a)

Mixing Method

Column Method

1.0 0.8 0.6 0.4 0.2 0.0 0.1

1

10

Ionic Strength (mM KNO 3 ) Attachment efficiency (α)

1.2

(b)

1.0 0.8 0.6 0.4 0.2 0.0 0.001

0.01

0.1

1

[Humic Acid] (mg/mg Ag)

248 249

Figure 1: (a) Mixing & Column α vs IS (HA concentration = 0.002) (b) Mixing & Column α vs HA (IS = 5 mM). The

250

shown error bars stand for standard deviations from three replicates.

251 252

Agreement in the trends and numerical values of attachment efficiency determined by the mixing

253

and column methods validates the use of these mixing studies across a range of system parameters.

254

The viability of this batch mixing method could allow the range of materials for which attachment

255

measurements are currently possible through column studies to be expanded to more complex and

256

dynamic systems (e.g. soil, wastewater sludge, biological systems), as well as systems with sparse

257

background particle concentrations. 30

258 259

β: Influence of mixing rate



260

For a known value of attachment efficiency (α) and collector particle concentration (B), the mixing

261

method can be used to determine the value of the collision rate (βTOTAL) under given conditions. As

262

predicted by the rectilinear model for colloidal suspensions (Supporting Information), the collision

263

rate increased with mixing rate (shear rate) for PVP-Ag nanoparticles with 8 µm GB background

264

particles, with reduced impact of mixing speed for 2.2 µm and 40 µm GBs (Figure 2). In Figure 2, the

265

collision rate βTOTAL for a range of mixing speeds is plotted relative to the β obtained at 60 rpm in

266

order to highlight the relative impact of mixing speed on collision rates. These findings for varied

267

background particle size qualitatively agree with trends in βTOTAL as a function of mixing speed as

268

predicted by the rectilinear collision model, where shear rate has a significant influence on βTOTAL

269

only over a limited background particle size range of approximately 1-10 µm (Figure 2, inset). Also

270

qualitatively consistent with theory, the smallest background particle size (2.2 µm) yielded small

271

values for the collision rate kernel that were relatively insensitive to mixing speed.

272

273 274

Figure 2: Modelled and measured values for collision rate (βTOTAL) vs background particle size. Normalized

275

collision rate was calculated for a range of system mixing speeds and plotted relative to results at 60 rpm.

276

Inset: Relative contributions of βBROWNIAN, βSHEAR and βDIFFERENTIAL to βTOTAL at 480 rpm

277


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This experimental determination of β and its qualitative agreement with modelled trends only for

279

collector particles ≥ 50 µm in diameter highlights the utility of β as a separate and calculable

280

parameter in modelling attachment of nanoparticles to these larger collectors. However, the model

281

appears limited to general trends for collector particles of 5-20 µm, and is somewhat lacking in

282

predictive power for the smallest collector particles. These results highlight the utility of the present

283

approach, in which values of β are essentially eliminated from consideration by holding βB constant

284

while measuring α under varying conditions that could affect collision rates in ways otherwise

285

difficult to fully address.

286 287

B: Role of background concentration

288

The influence of background particle (GB) concentration was found to follow heteroaggregation

289

theory (Equation 1), with overall attachment rates (αβB) varying proportionally with collector

290

concentration. In these tests, the exposure medium, nanoparticles, and mixing speeds were all held

291

constant while varying collector particle concentration, B. The collector concentration ratio

292

(1:2:4:8:16:32) (equivalent on mass, # and SA bases) closely matched the measured αβB (Figure 3).

293



294

Figure 3: Influence of B on total removal rate due to attachment, αβB, for 40 µm GBs. [KNO3] = 5 mM, mixing

295

speed = 350 RPM; shear rate 14.4 s-1

296 297

The agreement of experimental B findings with theory confirms that total collision frequency rates

298

(βB) measured at one B condition may quite easily be adapted and applied to another B condition

299

based on particle collision theory. This holds true as long as β is held constant across all values of B

300

tested and high concentrations of collector particles favoring significant particle-particle aggregation

301

and subsequent breakup are avoided.

302

This empirical method of determining β, B and the combined collision frequency term βB (or its

303

equivalent η0L in column studies) removes the need to calculate collision rates from theory based on

304

collision frequencies in mixing conditions11, 26 or collector efficiencies in column studies,22 as well as

305

reducing dependence on potentially sensitive unknowns such as collector size or flow rate.37, 38

306

Indeed, the presence of multiple collector class sizes and material densities in complex backgrounds

307

make the calculation of collision frequencies and collector efficiencies, at best, complex and reliant

308

on multiple assumptions or, at worst, impossible.

309

Calculating the collision rate, β, is further complicated when βSHEAR changes dramatically within the

310

system, which can occur under conditions of irregular or complex geometries or for a distribution of

311

mixing regimes (e.g. containers with baffles, end-over-end mixers). In any event, average mixing

312

conditions are likely to misrepresent the complex nature of particle passage through local mixing

313

environments that are likely to determine the overall rates of aggregation. Biological systems such

314

as bacteria and algae30 may be subject to limited mixing so as not to compromise the integrity of the

315

sample, while systems with mobile organisms such as invertebrates and fish would be subject to

316

collector-driven mixing. In these cases, empirically-derived collision frequencies with a nanoparticle

317

of known α are also preferable to the calculation method.


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318 319

Roles of Collector Particle Composition and Nanoparticle Surface Chemistry

320

Additional mixing studies were performed, varying both the collector particles (GBs and kaolinite)

321

and nanoparticle surface chemistry (Cit-, PVP-, and HA-Ag nanoparticles). The goal of these

322

experiments was to determine if trends in one set of alphas determined for a reference surface such

323

as glass beads, might be predictive of trends in alphas for the same nanoparticles attaching to more

324

complex surfaces. The resulting α values are provided in Figure 4. The trends for these three types of

325

nanoparticle surface treatments are similar, but not identical. For the PVP-Ag nanoparticles utilized

326

in earlier portions of this study, attachment to GBs was slightly more efficient than that onto

327

suspended kaolinite surfaces (α= 0.92 ± 0.09 vs 0.75 ± 0.05). Attachment efficiencies for both Cit-

328

and HA-Ag nanoparticles were markedly lower than those of PVP-Ag nanoparticles for both collector

329

particle surfaces. However, the relative affinities for GBs and kaolinite was reversed in these cases,

330

with α significantly higher for nanoparticles onto kaolinite than on GBs, particularly for HA-Ag. We

331

hypothesize that these differences are due to differences in electrostatic and steric stabilization

332

against heteroaggregation. The more highly charged Cit-Ag nanoparticles are less likely to adhere to

333

GBs, which are more significantly and uniformly negatively charged than kaolinite, which presents

334

multiple surfaces of different energies.39 Additionally, the electrosteric stabilization of HA-Ag

335

nanoparticles further increased the stability of these particles against heteroaggregation as

336

compared to Cit-Ag nanoparticles, which possess much less steric stabilization. However, more

337

specific interactions could have arisen between the organic HA and kaolinite surfaces, resulting in

338

higher attachment efficiencies. Further, while PVP may provide some steric stabilization against

339

heteroaggregation, this is expected to be less important than for GA. This is due to the uniform and

340

linear structure of PVP compared with the large and highly branched structure of GA. These

341

comparisons display an overall trend of αPVP >> αCit ≥ αHA for both GBs and Kaolinite, but more subtle

342

differences in attachment efficiency occurred due to collector particle composition. Therefore,



343

detailed knowledge of the nanoparticle core and surface composition in addition to knowledge of

344

the local water chemistry is critical in studies of attachment efficiencies between nanoparticles for

345

fate and transport model development.

GBs

HA

Kaolinite

Citrate PVP 0.0

0.2

0.4

0.6

0.8

1.0

α

346 347

Figure 4: Values of attachment efficiency (α) for three different silver nanoparticle surface chemistries (humic

348

acid[HA], citrate, and polyvinylpyrrolidone [PVP]) and two collector particles (glass beads [GBs] and kaolinite

349

clay).

350 351

Application: α heat-map

352

The full range of possible values for α (i.e. 0 → 1) were observed when ionic strength (KNO3) and

353

organic matter (humic acid) concentration were varied within realistic environmental bounds.

354

Neither IS nor HA concentration dominated attachment efficiency determination over the

355

environmental range tested; rather, the IS:HA ratio drove α (Figure 5). The interplay between these

356

two characteristics of the system, ad their effect on attachment efficiency, suggests that all particle

357

and media characteristics that influence particle-surface interactions (e.g., van der Waals (vDW),

358

hydrophobic, steric) are of critical importance in the investigation of what drives attachment

359

efficiency in complex systems.40, 41 Extending the α-mapping presented here across multiple media

360

with differing characteristics (e.g. pH, different salts, different classes of organic matters) and across


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particles with different characteristics (size, morphology and functionalisation) will help build

362

predictive models for nanoparticle attachment.6

Humic Acid (mg/mg Ag)

361

0.5 0.002 0.01 0.05 0.1 0.2 0.4 0.8 1.6

Ionic Strength (mM KNO3) 1 2.5 5

10

0.061

0.180

0.777

0.918

1.000

0.045

0.031

0.256

0.789

0.939

0.026

0.036

0.069

0.269

0.792

0.026

0.026

0.064

0.152

0.640

-

0.000

0.025

0.048

0.382

-

-

0.012

0.020

0.159

-

-

-

0.017

0.076

-

-

-

-

0.000

Alpha (α) > 0.5 0.1 - 0.5 0.02 - 0.1 < 0.02

363 364

Figure 5: Heat-map summary of alpha (α) results as a function of both Humic Acid concentration and solution

365

ionic strength. “-“ indicate values of α below detection limits.

366

The mixing method investigated here proved to be effective for measuring nanoparticle attachment

367

to glass beads in mixed systems with widely varying physicochemical properties, as well as in a

368

model suspended clay system. The predicted attachment efficiencies for PVP-coated Ag

369

nanoparticles closely matched those measured in standard column studies, while extending the

370

utility of attachment measurement to diverse, non-static systems.

371

This work demonstrates that attachment efficiencies for nanoparticle heteroäggregtion can be easily

372

obtained from simple laboratory experiments without complex instrumentation or the need for

373

direct calculation of collision frequencies or total collector surface areas. This functional assay42

374

opens the door for parameterization of models for heteroaggregation and deposition, while also

375

suggesting a means of characterizing physical regimes for mixing and flow in complex environmental

376

systems. Therefore, with careful characterization of water chemistry parameters such as ionic

377

strength, pH, and organic content, as well as nanoparticle core composition and surface chemistry,

378

glass bead studies may prove effective in measuring approximate comparisons in attachment

379

efficiencies between nanoparticles for fate and transport model development.



380

Acknowledgements

381 382 383 384 385 386 387

This material is based upon work supported by the National Science Foundation (NSF) and the Environmental Protection Agency (EPA) under NSF Cooperative Agreement EF-0830093 and DBI1266252, Center for the Environmental Implications of NanoTechnology (CEINT). Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF or the EPA. This work has not been subjected to EPA review and no official endorsement should be inferred. NO’B was funded by the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement n°329903.

388 389

Supporting Information is Available

390

Supporting information is available. Column experimental setup, shear rate calculations, rectilinear

391

model equations, sample raw data from mixing studies


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42. Hendren, C. O.; Lowry, G. V.; Unrine, J. M.; Wiesner, M. R., A functional assay-based strategy for nanomaterial risk forecasting. Science of the Total Environment 2015, 536, 1029-1037.


Measuring Nanoparticle Attachment Efficiency in Complex Systems

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