Nanoparticle Surface Affinity as a Predictor of Trophic Transfer

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Nanoparticle Surface Affinity as a Predictor of Trophic Transfer Nicholas K. Geitner, Stella M. Marinakos, Charles Guo, Niall O\'Brien, and Mark R. Wiesner Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b00056 • Publication Date (Web): 01 Jun 2016 Downloaded from http://pubs.acs.org on June 4, 2016

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Environmental Science & Technology

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Nanoparticle Surface Affinity as a Predictor of Trophic Transfer

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Nicholas K Geitner†§, Stella M Marinakos§, Charles Guo†, Niall O’Brien†, Mark R Wiesner*†§

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† Department of Civil and Environmental Engineering, Duke University, Durham, NC

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§ Center for the Environmental Implications of NanoTechnology (CEINT), Duke University

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*Corresponding Author

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Duke University, Box 90287

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Durham, NC, USA 27708

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Phone: 919-660-5292

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e-mail: [email protected]

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ACS Paragon Plus Environment


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ABSTRACT

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Nano-scale materials, whether natural, engineered, or incidental, are increasingly

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acknowledged as important components in large, environmental systems with potential

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implications for environmental impact and human health. Mathematical models are a

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useful tool to handle the rapidly increasing complexity and diversity of these materials and

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their exposure routes. Presented here is a mathematical model of trophic transfer driven

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by nanomaterial surface affinity for environmental and biological surfaces, developed in

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tandem with an experimental functional assay for determining these surface affinities. We

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found that nanoparticle surface affinity is a strong predictor of uptake through predation in

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a simple food web consisting of the algae Chlorella vulgaris and daphnid Daphnia magna.

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The mass of nanoparticles internalized by D. magna through consuming nanomaterial-

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contaminated algae varied linearly with surface attachment efficiency. Internalized

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quantities of gold nanoparticles in D. magna ranged from 8.3 to 23.6 ng/mg for

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nanoparticle preparations with surface attachment efficiencies ranging from 0.07 to 1. This

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model coupled with functional assay approach may provide a useful screening tool for

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existing materials as well as a predictive model for their development.

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INTRODUCTION

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Advances in analytical capabilities have allowed for the study of how nano-scale objects

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transit through ecosystems and interact with organisms. There is a growing awareness of

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the role that nano-scale objects play in large-scale systems, through processes that include

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nutrient and contaminant transport, signaling between microbial communities, and gene

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transfer. Much of this work has been motivated by concern of potential effects on human

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health and the environment from engineered nanomaterials. Indeed, the number and


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diversity of nano-enabled consumer and industrial products have been rapidly

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increasing.1–3 Whether through wastewater treatment, terrestrial runoff, landfill

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deposition, direct leaching, or other processes, growing quantities of nanomaterials from

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such products are expected to find their way into aquatic systems.2–4 A large number of

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materials, products, and exposure scenarios prohibits evaluation of each individual

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nanomaterial in the lab for toxicity and in the field for transport and environmental effects.

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Models for nanoparticle transport and hazard are one possible tool to be used in evaluating

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the risk of new materials. Transport models range in scale and complexity and include

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models for transport in porous media,5,6 surface waters,7–9 and even within tumors.10

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However, to date, there has been no conceptual modeling of nanomaterial trophic transfer,

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a mechanism that will be critical in understanding both transport and impacts of

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nanomaterials as they move through food webs. Such models are important not only for

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assessing the impact of current and emerging materials and for informed design of new

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materials, but also for describing how nano-scale objects of natural, incidental, and

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engineered origin transit through ecosystems.

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The power of any model is tied directly to the parameters on which calculations depend.

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However, because of the complexity of the environmental systems of interest and the

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widely varying properties of nanomaterials, the parameter space for a highly predictive

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model tends to grow very quickly. Therefore, it is also desirable to identify a finite number

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of functional assays11 that are capable of rapidly and simply describing key parameters

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that meaningfully describe nanomaterial properties and minimize the necessary

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experimental characterization of a new nanomaterial. Towards this end, we applied a



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functional assay for surface affinity12 to parameterize a model developed to describe the

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trophic transfer of nanoparticles in aquatic ecosystems. The functional assay used here

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measures the attachment efficiency, α , of a nanoparticle for a given surface, which can be

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interpreted as the probability of attachment (between 0 and 1) when a nanoparticle is

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brought in contact with that surface. The attachment efficiency is an aggregate property of

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a nanomaterial that depends on the physical and chemical properties of the material, the

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surfaces that nanomaterials may adhere to, and the surrounding environment. We

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hypothesized that surface attachment to aquatic organisms will be a critical route of

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nanomaterial introduction to the local ecosystem, as has been observed in previous

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laboratory studies using gold nanorods and TiO2 nanoparticles.13,14 We examine three

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different nanoparticle surface functionalities, namely citrate, poly(allylamine

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hydrochloride), and Suwanee River humic acid. These represent negatively and positively

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charged particles as well as an environmentally relevant negatively charged nanoparticle

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with greater hydrophobicity, respectively. We selected 40 nm nanoparticles for this study

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as they are near the midpoint of the widely accepted nano size range, 1-100 nm.

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In this work we formulate a model for nanoparticle uptake and trophic transfer that

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depends on the surface affinity of nanoparticles for individual organisms and tissues. We

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compare laboratory scale trophic transfer of gold nanoparticles of varying surface

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chemistry to trends predicted by model calculations.

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METHODS

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Gold Nanoparticle Preparation


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Gold nanoparticles were synthesized by a stepwise growth from a smaller gold seed.

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Reverse osmosis-purified water (Barnstead Nanopure, 18 MΩ-cm) was used for all

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experiments, unless otherwise specified.

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11 nm gold nanoparticle seed. First, 11 nm gold nanoparticles were synthesized using

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published procedures.15,16 In a round-bottom flask equipped with a condenser, 1 L of 1 mM

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hydrogen tetrachloroaurate trihydrate (Sigma) was brought to reflux while stirring. All at

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once, 4 mL of 1 M sodium citrate dihydrate (VWR) was added, and heating was continued.

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After 10 min., the suspension was removed from heat and stirred until cool, then stored

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covered at 4°C until further use.

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40 nm gold nanoparticles. To synthesize the 40 nm gold nanoparticles, 1 mL of 11 nm gold

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nanoparticles and 500 μL of 40 mM sodium citrate were added to 98 mL of water, and the

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mixture was stirred and heated to reflux. A total of 500 μL of 0.1 M hydrogen

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tetrachloroaurate trihydrate was added in 50 μL aliquots every 15 min. After the final

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aliquot was added, heating was continued for 15 min., then the suspension was removed

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from heat and stirred until cool. The resulting gold nanoparticle suspension, labeled

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AuNP·Cit, was stored at 4°C.

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Modification of 40 nm gold nanoparticles with poly(allylamine hydrochloride).

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Poly(allylamine hydrochloride) (PAH, MW 70,000; Sigma) was added to AuNP·Cit at a

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concentration of 1 mg/mL of particle suspension. The mixture was stirred overnight, then



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centrifuged at 5000 × g for 20 min., and resuspended in water. The resulting stock

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suspension was labeled AuNP·PAH and stored at 4 °C.

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Modification of gold nanoparticles with humic acid. AuNP·Cit was diluted to 50 ppm in

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deionized water and the pH was adjusted to 7.8 using 1 M NaOH. The particles were

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subsequently mixed with 5 ppm Suwannee River humic acid on a magnetic stir plate

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overnight. The resulting stock suspension was labeled AuNP·HA and stored at 4 ºC.

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Particle characterization. Particle core size was characterized by transmission electron

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microscopy (TEM) (FEI Tecnai G2Twin). To prepare the samples, gold nanoparticle

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suspensions were diluted 1:10 in water, and a few drops were applied to a Formvar-coated

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Cu grid (Ted Pella) while wicking away the liquid with filter paper. TEM size distribution

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analysis revealed that the particles were 38.7±5.1 nm. Hydrodynamic diameter and

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electrophoretic mobility were measured with a Malvern Zetasizer Nano ZS. The zeta

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potentials in algal medium (calculated by the Malvern software using the Smoluchowski

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approximation) of AuNP·Cit, HA, and PAH were -39.4±1.2, -35.5±1.5, and +19.0±1.5 mV,

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respectively.

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Algal Culture

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Cultures of Chlorella vulgaris were maintained in AlgGro medium (Carolina Biosciences),

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with an ionic strength of 1 mM and low organic enrichment. Medium concentrate was

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diluted to 1 mM ionic strength and the pH was adjusted to 7.8 using 1 M NaOH. Culture

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vessels were then autoclaved at 15 psi pressure for 30 min and allowed to cool to room


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temperature before inoculation with live algal cells. Cultures were kept under a 16:8 h

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light:dark cycle and were sub-cultured at least every 2 weeks. Algal populations were

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allowed to grow to saturation,17,18 0.11 g/L dry weight algae, before use in all cases.

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Daphnia Culture

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Live juvenile Daphnia magna were obtained from Carolina Bioscience and cultured in a 10

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L container with light aeration. The culture medium contained final salt concentrations of

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2.2 mM NaHCO3, 0.7 mM CaSO4, 0.1 mM MgSO4, and 0.1 mM KCl and a pH of 8.0. D. magna

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were fed live C. vulgaris daily, cultured as described above. Waste in the tank was cleaned

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daily and 50% of the culture medium refreshed weekly.

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Alpha Measurements

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The surface affinity, αAn, of each nanoparticle for algae was determined through mixing

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studies following the method of Barton et al.12 Washed algae, at a 2.5x concentration from

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stock, containing a final concentration of 7 ppm AuNP, were stirred with magnetic stir bars

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at 700 rpm in AlgGro medium. Although 7 ppm is above expected environmental exposure,

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use of this concentration enabled accurate measurements of αAn, which could then be

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applied to environmental exposures across a wide range of concentrations. Aliquots of 700

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µL were removed at designated time points, centrifuged at 2000 × g for 1 min to remove

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algae and associated nanoparticles from the suspension, and 300 µL of supernatant was

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dispensed into a 96-well plate. After all samples were collected, the concentration of free,

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unbound AuNP was determined by UV-VIS spectrophotometry (Thermo Multiskan MMC) at

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540 nm. Losses due to nanoparticle homoaggregation in the medium or to centrifugation



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were determined by conducting control studies in which no algae were present (Figures

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S1-S4), and the concentration measurements of free AuNPs were normalized by these

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values. For a suspension of nanoparticles aggregating with “background” particles (in this

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case algae) of concentration B, the equations for aggregation can be simplified12 to yield an

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expression for the initial stages of aggregation when break-up can be ignored:

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⎛n ⎞ ln ⎜ 0 ⎟ = αβ Bt ⎝ n⎠

(1)

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where n0 is the initial nanoparticle number concentration, n the number concentration at

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time t, 𝛼 is the attachment efficiency, 𝛽 the collision frequency between the nanoparticle

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and background particles, and B the concentration of background particles. By using

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Equation (1), a plot of the inverse of nanoparticle concentration remaining in suspension as

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a function of mixing time should yield a linear relationship, the slope of which is

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proportional to the attachment coefficient. Fitting the data to a linear function, the slope of

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the initial linear attachment phase was used to calculate αβ B . In the case of AuNP·PAH,

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where opposite charges lead to highly attractive potential energies of interaction, it is likely

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that all nanoparticle-background particle collisions yield an attachment (𝛼$% = 1). Under

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this assumption, the attachment efficiency of AuNP·PAH could be used to normalize all

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other measurements of αβ B , yielding the remaining relative values of 𝛼$% .

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Trophic Transfer Study

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Nanoparticles were placed in contact with algal cultures using 0.2 g/L algae in AlgGro

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medium. Three such algal cultures were incubated with 1ppm of AuNP·Cit, AuNP·HA, or


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AuNP·PAH for 6 hours under gentle mixing to prevent settling of algal cells. Each culture

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was then centrifuged at 1000 × g for 3 min and resuspended to the original algae

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concentration in AlgGro medium. The supernatant, consisting of any remaining unbound

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nanoparticles, was collected for further analysis.

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For each of the three nanoparticle surface functionalities, 6 vials were prepared, each

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containing 10 mL of daphnia culture medium, 10 mL of nanoparticle-contaminated algae

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prepared as described above, and 5 randomly selected live juvenile daphnia. Juvenile is

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defined here as being at least 3 days old but have not yet reached the final adult stage in

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their life cycle, as evidenced by size and visible egg production. The random selection of 5

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organisms for each trial replicate was intended to negate the inherent and age-related

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differences between organisms such as feeding and growth rates. After 24 hours of feeding,

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each vial of daphnia was washed individually by pipette transfer to a 1 L container of fresh

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medium in order to remove unconsumed algae as well as any remaining free AuNP

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potentially released from the algae surface. Organisms were allowed to swim freely for 1

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minute and then removed either for analysis or depuration, each of which were allocated 3

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vials per preparation. Depuration took place in fresh medium for 48 hours, during which

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daphnia were fed 5 mL clean algae twice daily. Any offspring produced during the

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depuration period were segregated for analysis independent of the parents.

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All daphnia were euthanized by freezing at -4°C and subsequently dried in an oven

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overnight at 80°C. Digestion was carried out overnight in aqua regia, a 1:3 solution of

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concentrated HNO3 and HCl, respectively. Samples were diluted in 2% HNO3, 0.5% HCl and

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analyzed by inductively coupled plasma mass spectroscopy (ICP-MS) for gold content.

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Statistical analyses were carried out using linear regression t-tests.



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Modeling

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Nanoparticles are assumed to be initially introduced to a trophic web through direct

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interactions between the nanoparticles and the surfaces of organisms. Once initial

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attachment occurs, nanomaterials transit through the food web as a function of feeding,

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predation, and depuration rates. For the system addressed in the current study,

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nanoparticles concentrations are considered in the water column (n), algae (A), daphnia,

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(PD+D) and fish (PF+ F) compartments. The flow of nanoparticles into and out of each

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compartment was defined by a set of coupled linear, first order differential equations.

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dn B = S − ∑ α in βin i n dt mi i

(2)

dA B = α An β An A n − k fDA BD A − k fFA BF A dt mA

dPD = k fDA BD A − kdD PD − k fFD BF PD dt

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dD B = α Dn β Dn D n − k fFD BF D dt mD

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dP2 = k fFD BF ( PD + D ) + k fFA BF − kdF PF dt

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dF B = α Fn β Fn F n dt mF

(7)


(3)

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In Equations 2-7, n is the number concentration of free nanoparticles, S is the source of

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nanoparticles to the system expressed as a number concentration per time, A is the number

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concentration of nanoparticles in the system attached to algae, kfxy is the feeding rate of

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predator x on prey y, Px the number concentration of nanoparticles in predator x due to

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ingestion, kdx is the (alpha-independent) depuration rate of ingested nanoparticles for each

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predator x, D and F are the nanoparticle number concentrations attached to the surface of

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daphnia or fish, respectively, βin is the collision rate between nanoparticles and collecting

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surface i (eg. algae, daphnia, suspended solids, etc) per mass of collector surface, Bi is the

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mass concentration of collector surface i , and α in is the attachment efficiency of

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nanoparticles (n) to surface i . Values of βin were calculated using the curvilinear collision

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model including Brownian motion, mixing, and differential settling terms.9,19

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This model assumes ecological equilibrium, i.e. that all organism populations are, on

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average, constant with time. Nanoparticles lost from any model compartment due to

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depuration were assumed to leave the current system to the local sediment, independent of

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the current model. Suspended solids in the model formulation were defined as having a

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particle density of 1.3 g/cm3 and a number-weighted mean particle diameter of 10 µm.20

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Steady state solutions of the above system of equations were solved analytically and

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analyzed using Wolfram Mathematica.

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RESULTS AND DISCUSSION

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Nanoparticle Attachment to Algae

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Figure 1 shows the values of α An , or attachment efficiency, for the attachment of each

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particle preparation onto live algal cells, calculated using Equation 1. Citrate-stabilized

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particles possessed the lowest attachment efficiency (0.07), while PAH particles had an α

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assumed to be near 1 due to electrostatic attraction between these positively charged

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particle surfaces and the predominantly negatively charged algal cell walls. HA-coated

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particles had α values between these two extremes (0.17). While citrate and HA are both

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negatively charged, the higher surface affinity of HA may be due to hydrophobic moieties

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on humic acid interacting with algal cell walls.21–23 The attachment of nanoparticles to the

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surface of algae was confirmed and visualized using enhanced dark field microscopy,

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shown in the Figure 1 inset for AuNP-Cit, HA, and PAH from left to right. In these images,

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the large dark circles are live algal cells and the small, bright points are individual or small

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clusters of gold nanoparticles. These images confirm that nanoparticles did indeed attach

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to the outer cell wall without penetrating to the interior, as expected for 40 nm

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nanoparticles.24 It is noteworthy that, while HA was used here as an alternate surface

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stabilizing molecule, coating by humic acids is also a realistic environmental exposure

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scenario. The increase of attachment efficiency following nanoparticle interactions with

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humic acids at environmentally relevant concentrations suggests that, while a particle may

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be designed to minimize biological interactions, these properties may rapidly change after

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release, thus affecting the fate and impact of the material.

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Nanoparticle Trophic Transfer

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To understand the impact of differences in α on nanoparticle trophic transfer, we

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conducted a trophic transfer study using cultures of D. magna. The gold content per dry

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weight D. magna after 24 hours of feeding on algae with adsorbed AuNP and after 48 hours

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of depuration is summarized in Figure 2. We found that citrate, HA, and PAH stabilized

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AuNP–contaminated algae resulted in 8.3±0.6, 10.5±0.9, and 23.6±0.4 ng gold/µg daphnia

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dry weight, respectively, before depuration. These ingested gold concentrations were

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strongly and linearly correlated (Pearson coefficient = 0.94, p=0.02) to the observed α An

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values for AuNP attachment to algae (Figure 2, inset), demonstrating that α is a strong

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predictor of this initial nanomaterial trophic transfer.

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After depuration, Cit, HA, and PAH stabilized gold mass ratios in daphnia are 2.5±0.3,

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1.9±0.4, and 3.1±0.5 ng/µg respectively, suggesting both no statistical difference in the

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quantity of gold retained, regardless of nanoparticle surface chemistry. These results also

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suggested that there was no correlation with nanoparticle α (p=0.4), in agreement with

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previous studies that showed no correlation of depuration to initial nanoparticle

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properties.25 This is likely because the processes of algae attachment, ingestion, and

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digestion sufficiently alter the nanoparticle surfaces that they become virtually identical to

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each other. Additionally, offspring were collected during depuration of one culture each of

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D. magna exposed to AuNP·HA and PAH and possessed gold concentrations very close to

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that of the parents (approximately 2.5 µg/ng). While insufficient for a thorough

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investigation, the distribution of nanoparticles into offspring is indicative of true uptake of

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nanoparticles and is in line with previous studies showing both uptake and maternal

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transfer of nanoparticles.26,27 While there was no clear correlation between α and post-

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depuration concentration, both pre- and post-depuration concentrations are critical to



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understanding trophic transfer, as nanoparticles consumed through predation will consist

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of both depurated and non-depurated particles.

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Trophic Transfer Modeling

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Equations 2-7 can be generalized for an arbitrary number of predation interactions. For

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this work we described the experimental system, plus one level of predation (the fish).

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Values for feeding rates and organism populations were obtained from the literature 28–30

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and the depuration rate by D. magna was estimated to be approximately 0.8 day-1, using the

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fraction of gold depurated during the experimental trophic transfer studies presented here.

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In all cases we assumed that α Dn = α Fn = α An . The full table of constants used in model

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calculations is given in Table 1.

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Figure 3 summarizes calculations of the fraction of total nanoparticle concentration as a

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function of α at each trophic level at steady state for multiple possible scenarios. First, in

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Figure 3(a), the baseline scenario includes algae that have grown to 5.5×10, cells/mL (0.05

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g/L) in the water column, a proportional population of daphnia,31 and likewise for fish, all

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in water with no competing particle surfaces such as suspended sediments or clays. It also

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minimizes fish consumption of algae. The next scenario (Figure 3(b)) depicts the same

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biological population, but with suspended solids present in the water at a concentration of

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0.15 g/L, representing a turbid water system. We included suspended solids here in order

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to determine the extent to which the availability of suspended solids as an additional

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attachment surface affects the potential of nanoparticle trophic transfer. The value of α for

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nanoparticles attaching to these solids was assumed to be equal to that for nanoparticles


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on algae, α An . Finally, Figure 3(c) depicts a scenario in which the value of α for attaching

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to suspended solids was held constant at a value of 0.01, which is approximately the value

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at which most nanoparticles were attached to a surface in scenarios (a) and (b). This also

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allows us to observe the differences in trophic transfer potential when 𝛼 is greater or less

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than that on suspended solids, which are not necessarily equal or even expected to always

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follow identical trends. In all cases, conditions were selected to represent a freshwater lake

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dominated by runoff inflow and very little outflow, similar to the natural environment of D.

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magna. All scenarios were also defined as having constant particle source S, of 106 L-1 day-1

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and results are plotted as fractions of the total number of nanoparticles in each

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compartment. The value for S in applications of the model may be estimated from transport

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model calculations or field measurements.

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The baseline scenario highlights some key trends in attachment and trophic transfer. In

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this scenario, nanoparticles may only attach to algae, daphnia, or fish. In this case, the

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fraction of nanoparticles in each trophic level initially increases in a log linear fashion as a

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function of α until approaching a plateau value as 𝛼$% approaches unity. This linear

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dependence of trophic transfer on 𝛼$% is in agreement with the linear correlation observed

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in our experimental studies. At a value near 𝛼$% = 0.003, the fraction of nanoparticles in

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fish overtakes that of free nanoparticles in the water column. The fraction in daphnia is

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considerably less than that attached to algae due to depuration rates. The fraction found in

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fish, however, is nearly 2 orders of magnitude higher due to bioconcentration. For both

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daphnia and fish, the fraction attached to their outer surfaces was several orders of

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magnitude smaller than any other component in the system and is thus hidden from the

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output in Figure 3. As 𝛼$% approaches 1, the fractions found in fish, algae, and daphnia



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respectively approach 55%, 42%, and 2% respectively. In the second scenario, suspended

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solids (e.g., clays) that do not enter into the food chain were assumed to be present and to

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have the same affinity for nanoparticles as the algae. In this case, the addition of suspended

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solids causes only subtle changes in trend, but noticeable changes in quantity (Fig. 3(b)).

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Namely, the fraction of nanoparticles attached to these solids is slightly higher than that on

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algae or within fish. Competition for this attachment surface caused significant decreases in

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the concentrations found in algae, most obvious for large values of 𝛼$% . Additionally, the

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concentration found in daphnia and fish is now lower than in the scenario in Figure 3(a)

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because of attachment competition between suspended solids and algae, daphnia’s food

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source.

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Because one does not expect attachment efficiencies on organic surfaces such as algae and

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environmental colloids to be equal or potentially even trend together, the third scenario

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assumes a fixed value of α on suspended solids in Figure 3(c). Above α An = 0.01 , all

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compartments are nearly identical to those in the baseline scenario without suspended

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solids. Below 0.01, however, the behavior is markedly different. Attachment in that region

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is dominated by the colloidal fraction, resulting in marked decreases in the fractions found

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in all biological compartments of nearly 1 order of magnitude. Low presence in fish and

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algae quickly increases with increasing 𝛼$% to overtake first the free nanoparticle and then

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the colloidal compartments. These trends indicate that understanding any differences in

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attachment to colloidal and biological surfaces will be critical to nanomaterial transport

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and trophic transfer modeling.

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The clear differences between these model scenarios highlight the importance of carefully

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characterizing the environment and ecosystem of interest as well as understanding the


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trends in attachment efficiency. The experimental evidence reported here suggests that

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surface affinity appears to be an important indicator of the propensity of nanoparticles to

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enter the food chain and, in at least some cases, be retained by organisms at higher trophic

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levels. The extent to which the functional assay for surface affinity is predictive of trends

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extending to higher order elements of a food web, and the generality of this measure to

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other ecosystems, remains to be tested. However, these initial results suggest that surface

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affinity for critical biological targets may be a useful screening tool for ecosystem impacts

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of nanomaterials.

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ACKNOWLEDGEMENTS

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The corresponding author gratefully acknowledges the inspiration that Jerald L. Schnoor

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has provided him throughout his career. Professor Schnoor introduced Wiesner to the field

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of environmental engineering and educated him in the modeling perspective that has

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served as the basis for the current work and many previous efforts. This material is based

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upon work supported by the National Science Foundation (NSF) and the Environmental

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Protection Agency (EPA) under NSF Cooperative Agreement EF-0830093 and DBI-

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1266252, Center for the Environmental Implications of NanoTechnology (CEINT). Any

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opinions, findings, conclusions or recommendations expressed in this material are those of

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the author(s) and do not necessarily reflect the views of the NSF or the EPA. This work has

379

not been subjected to EPA review and no official endorsement should be inferred.

380

SUPPORTING INFORMATION

381

Additional information including details regarding controls in mixing studies, nanoparticle

382

homoaggregation, and kinetics of attachment.



383

384

WORKS CITED

385

(1) Project on Emerging Nanotechnologies; http://www.nanotechproject.org/cpi.

386

(2) Gondikas, A. P.; Kammer, F. Von Der; Reed, R. B.; Wagner, S.; Ranville, J. F.; Hofmann,

387

T. Release of TiO2 Nanoparticles from Sunscreens into Surface Waters: A One-Year

388

Survey at the Old Danube Recreational Lake. Environ. Sci. Technol. 2014, 48, 5415–

389

5422.

390

(3) Gantt, B.; Hoque, S.; Willis, R. D.; Fahey, K. M.; Delgado-Saborit, J. M.; Harrison, R. M.;

391

Erdakos, G. B.; Bhave, P. V; Zhang, K. M.; Kovalcik, K.; et al. Near-road modeling and

392

measurement of cerium-containing particles generated by nanoparticle diesel fuel

393

additive use. Environ. Sci. Technol. 2014, 48, 10607–10613.

394

(4) Bernhardt, E. S.; Colman, B. P.; Hochella, M. F.; Cardinale, B. J.; Nisbet, R. M.;

395

Richardson, C. J.; Yin, L. An Ecological Perspective on Nanomaterial Impacts in the

396

Environment. J. Environ. Qual. 2010, 39, 1954.

397

(5) Becker, M. D.; Wang, Y.; Pennell, K. D.; Abriola, L. M. A multi-constituent site blocking

398

model for nanoparticle and stabilizing agent transport in porous media. Environ. Sci.

399

Nano 2015, 2, 155–166.

400 401 402 403

Page 18 of 25

(6) Ju, B.; Fan, T. Experimental study and mathematical model of nanoparticle transport in porous media. Powder Technol. 2009, 192, 195–202. (7) Praetorius, A.; Scheringer, M.; Hungerbühler, K. Development of environmental fate models for engineered nanoparticles--a case study of TiO2 nanoparticles in the Rhine


Page 19 of 25

404


River. Environ. Sci. Technol. 2012, 46, 6705–6713.

405

(8) Dale, A. L.; Casman, E. A.; Lowry, G. V; Lead, J. R.; Viparelli, E.; Baalousha, M. Modeling

406

nanomaterial environmental fate in aquatic systems. Environ. Sci. Technol. 2015, 49,

407

2587–2593.

408 409 410

(9) Li, X.; Zhang, J.; Lee, J. H. W. Modelling particle size distribution dynamics in marine waters. Water Res. 2004, 38, 1305–1317. (10) Gao, Y.; Li, M.; Chen, B.; Shen, Z.; Guo, P.; Wientjes, M. G.; Au, J. L.-S. Predictive models

411

of diffusive nanoparticle transport in 3-dimensional tumor cell spheroids. AAPS J.

412

2013, 15, 816–831.

413 414 415

(11) Hendren, C. O.; Lowry, G. V; Unrine, J. M.; Wiesner, M. R. A functional assay-based strategy for nanomaterial risk forecasting. Sci. Total Environ. 2015, 536, 1029–1037. (12) Barton, L. E.; Therezien, M.; Auffan, M.; Bottero, J.-Y.; Wiesner, M. R. Theory and

416

Methodology for Determining Nanoparticle Affinity for Heteroaggregation in

417

Environmental Matrices Using Batch Measurements. Environ. Eng. Sci. 2014, 31,

418

421–427.

419

(13) Dabrunz, A.; Duester, L.; Prasse, C.; Seitz, F.; Rosenfeldt, R.; Schilde, C.; Schaumann, G.

420

E.; Schulz, R. Biological surface coating and molting inhibition as mechanisms of TiO2

421

nanoparticle toxicity in daphnia magna. PLoS One 2011, 6, 1–7.

422

(14) Ferry, J. L.; Craig, P.; Hexel, C.; Sisco, P.; Frey, R.; Pennington, P. L.; Fulton, M. H.; Scott,

423

I. G.; Decho, A. W.; Kashiwada, S.; et al. Transfer of gold nanoparticles from the water

424

column to the estuarine food web. Nat. Nanotechnol. 2009, 4, 441–444.



425

(15) Turkevich, J.; Stevenson, P. C.; Hillier, J. A study of the nucleation and growth

426

processes in the synthesis of colloidal gold. Discuss. Faraday Soc. 1951, 11, 55.

427

(16) Frens, G. Particle size and sol stability in metal colloids. Kolloid-Zeitschrift und

428 429

Page 20 of 25

Zeitschrift für Polym. 1972, 250, 736–741. (17) Matos, Â. P.; Torres, R. C. de O.; Morioka, L. R. I.; Moecke, E. H. S.; França, K. B.;

430

Sant’Anna, E. S. Growing Chlorella vulgaris in Photobioreactor by Continuous Process

431

Using Concentrated Desalination: Effect of Dilution Rate on Biochemical

432

Composition. Int. J. Chem. Eng. 2014, 2014, 1–6.

433

(18) Chia, M. A; Lombardi, A. T.; Melão, M. D. G. G. Growth and biochemical composition of

434

Chlorella vulgaris in different growth media. An. Acad. Bras. Cienc. 2013, 85, 1427–

435

1438.

436 437 438

(19) Han, M.; Lawler, D. The (Relative) Insignificance of G in Flocculation. Am. Water Work. Assoc. 1992, 84, 79–91. (20) Tiehm, A.; Herwig, V.; Neis, U. Particle size analysis for improved sedimentation and

439

filtration in waste water treatment. Water Sci. Technol. 1999, 39, 99–106.

440

(21) Sirmerova, M.; Prochazkova, G.; Siristova, L.; Kolska, Z.; Branyik, T. Adhesion of

441

Chlorella vulgaris to solid surfaces, as mediated by physicochemical interactions. J.

442

Appl. Phycol. 2013, 25, 1687–1695.

443 444 445

(22) Ke, P. C.; Lamm, M. H. A biophysical perspective of understanding nanoparticles at large. Phys. Chem. Chem. Phys. 2011, 13, 7273–7283. (23) Chen, P.; Powell, B. A.; Mortimer, M.; Ke, P. C. Adaptive interactions between zinc


Page 21 of 25

446 447


oxide nanoparticles and Chlorella sp. Environ. Sci. Technol. 2012, 46, 12178–12185. (24) Lee, W.-M.; Yoon, S.-J.; Shin, Y.-J.; An, Y.-J. Trophic transfer of gold nanoparticles from

448

Euglena gracilis or Chlamydomonas reinhardtii to Daphnia magna. Environ. Pollut.

449

2015, 201, 10–16.

450

(25) Skjolding, L. M.; Kern, K.; Hjorth, R.; Hartmann, N.; Overgaard, S.; Ma, G.; Veinot, J. G.

451

C.; Baun, A. Uptake and depuration of gold nanoparticles in Daphnia magna.

452

Ecotoxicology 2014, 23, 1172–1183.

453

(26) Cedervall, T.; Hansson, L. A.; Lard, M.; Frohm, B.; Linse, S. Food chain transport of

454

nanoparticles affects behaviour and fat metabolism in fish. PLoS One 2012, 7, 1–6.

455

(27) Meyer, J. N.; Lord, C. A.; Yang, X. Y.; Turner, E. A.; Badireddy, A. R.; Marinakos, S. M.;

456

Chilkoti, A.; Wiesner, M. R.; Auffan, M. Intracellular uptake and associated toxicity of

457

silver nanoparticles in Caenorhabditis elegans. Aquat. Toxicol. 2010, 100, 140–150.

458 459

(28) Ebert, D. Chapter 2 Introduction to Daphnia Biology. In Ecology, Epidemiology, and Parasitism in Daphnia; 2005; pp. 1–25.

460

(29) Wilhelm, F. M.; Schindler, D. W.; McNaught, A. S. The influence of experimental scale

461

on estimating the predation rate of Gammarus lacustris (Crustacea: Amphipoda) on

462

Daphnia in an alpine lake. J. Plankton Res. 2000, 22, 1719–1734.

463 464 465 466

(30) Barker, D. M.; Hebert, P. D. N. The role of density in sex determination in Daphnia magna (Crustacea, Cladocera). Freshw. Biol. 1990, 23, 373–377. (31) Kersting, K.; van der Leeuw-Leegwater, C. Effect of food concentration on the respiration of Daphnia magna. Hydrobiologia 1976, 49, 137–142.



Attachment Efficiency,

An

467

1.0 0.8 0.6 0.4 0.2 0.0

Cit

HA

PAH

468

469

Figure 1. Measured value of α for AuNP·Cit, HA, and PAH on the surface of live C. vulgaris. Error bars are

470

standard deviations of triplicate measurements. Inset: enhanced darkfield image of C. vulgaris incubated with

471

AuNP·CIT, HA, and PAH for 2 hours before imaging, 40x magnification.

472

Internalized

Depurated

25

Daphnia Gold Content (ng/µg)

20

20

16 12

15 0

0.2

0.4

0.6

α An

0.8

1.0

10

5

0

473

Cit

HA

PAH

474

Figure 2. The measured mass concentration of gold per dry weight daphnia before and after 48 hours of

475

depuration. Error bars are standard deviation of the mean from independent populations. Inset: correlation

476

plot of D. magna internalized gold concentration vs AuNP α .


Page 22 of 25

Page 23 of 25


477

478

1

2

3

Source

S (L-1 day-1) x106

0.01

0.01

0.01

Defined

β An (cm3 s-1)

7.1

7.1

7.1

Calculated

β Dn (cm3 s-1)

0.45

0.45

0.45

Calculated

β Fn (cm3 s-1)

0.01

0.01

0.01

Calculated

BA (g L-1)

0.05

0.05

0.05

Literature31

BD (g L-1)

0.04

0.04

0.04

Literature31

BF (g L-1)

0.2

0.2

0.2

Calculated

mA (mg)

5x10-7

5x10-7

5x10-7

Calculated

mD (mg)

0.17

0.17

0.17

Measured

mF (mg)

200

200

200

Calculated

ms (mg)

7x10-4

7x10-4

7x10-4

Calculated

BS (g L-1)

0

0.06

0.06

Literature20

α S n

--

α An

0.01

Defined

KfDA (L g-1 day-1)

62.5

62.5

62.5

Literature28

KfFA (L g-1 day-1)

0.01

0.01

0.01

Literature29

KfFD (L g-1 day-1)

1.05

1.05

1.05

Literature29

KdD (day-1)

0.8

0.8

0.8

Measured

KdF (day-1)

0.02

0.02

0.02

Literature26

479

Table 1. The defined parameters for modeling Equations 2-7 for each of the three defined scenarios; 1) no

480

suspended solids, 2) solids where attachment efficiency of nanoparticles is equal to that on algae and 3)

481

solids with a fixed nanoparticle attachment efficiency of 0.01.



Particulate

Algae

Daphnia Fish

0

10

a

-1

10

-2

10

-3

Nanoparticle Fraction of Total

10

-4

10

0

10

b

-1

10

-2

10

-3

10

-4

10

0

10

c

-1

10

-2

10

-3

10

-4

10

-4

10

-3

10

-2

10

αAn

482

-1

10

0

10

483

Figure 3. Calculations of relative nanoparticle concentrations in each compartment from the trophic transfer

484

model for different scenarios of closed ecosystems with a constant source of nanoparticles. The scenarios

485

correspond to a) no competing suspended solids; b) the addition of suspended solids with α Solids,n = α An ; and

486

c) the same concentration of suspended solids with α Solids,n = 0.01 . Compartments are free nanoparticles

487

(black, solid), attached to suspended solids (black, dotted), algae (green, solid), daphnia (blue, dot-dash), and

488

fish (red, dash).


Page 24 of 25

Page 25 of 25

489


490

491

TOC Figure

492


Nanoparticle Surface Affinity as a Predictor of Trophic Transfer

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