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Mechanistic Insights from Discrete Molecular Dynamics Simulations of Pesticide-Nanoparticle Interactions Nicholas K. Geitner, Weilu Zhao, Feng Ding, Wei Chen, and Mark R. Wiesner Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b01674 • Publication Date (Web): 07 Jul 2017 Downloaded from http://pubs.acs.org on July 7, 2017

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Environmental Science & Technology

1

Mechanistic Insights from Discrete Molecular Dynamics Simulations of

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Pesticide− −Nanoparticle Interactions

3

Nicholas K Geitner,a Weilu Zhao,b Feng Ding,c Wei Chen,b Mark R Wiesnera*

4

a

5

Environmental Engineering, Duke University, Durham, NC 27708, USA

6

b

7

China

8

c

9

Corresponding Author:

Center for the Environmental Implications of NanoTechnology, Department of Civil and

College of Environmental Science and Engineering, Nankai University, Tianjin 300350,

Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA

10

Box 90287, Durham, NC 27708

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Phone: 919-660-5292

12

Fax: 919-660-5219

13

E-mail: [email protected]

14 15

ABSTRACT

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Nano-scale particles have the potential to modulate the transport, lifetimes, and ultimate

17

uptake of pesticides that may otherwise be bound to agricultural soils. Engineered

18

nanoparticles provide a unique platform for studying these interactions. In this study, we

19

utilized discrete molecular dynamics (DMD) as a screening tool for examining

20

nanoparticle−pesticide adsorptive interactions. As a proof-of-concept, we selected a library

21

of 15 pesticides common in the United States and 4 nanomaterials with likely natural or 1 ACS Paragon Plus Environment


22

incidental sources, and simulated all possible nanoparticle− −pesticide pairs. The resulting

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adsorption coefficients derived from DMD simulations ranged over several orders of

24

magnitude, and in many cases were significantly stronger than pesticide adsorption on clay

25

surfaces, highlighting the significance of specific nano-scale phases as a preferential media

26

with which pesticides may associate. Binding was found to be significantly enhanced by

27

the capacity to form hydrogen bonds with slightly hydroxylated fullerols, highlighting the

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importance of considering the precise nature of weathered nanomaterials as opposed to

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pristine precursors. Results were compared to experimental adsorption studies using

30

selected pesticides, with a Pearson correlation coefficient of 0.97.

31 32

INTRODUCTION

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The presence and implications of anthropogenic nanomaterials in various natural

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environments is a rapidly growing body of research.1 This attention is due to the rapidly

35

growing applications of a wide variety of nanomaterials in both consumer and industrial

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applications, ranging from silver nanoparticles in textiles to titania particles in sunscreens

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or nano-scale ceria as diesel fuel additives. However, the release of naturally occurring and

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incidental nanomaterials is several orders of magnitude greater than those of intentional

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engineered origin,2 and in many cases, the engineered and natural or incidental material

40

may be virtually indistinguishable.

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From a human health and exposure perspective, agricultural environments are

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clearly of critical importance. Work in this area frequently focuses on the acute toxicity of

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pristine nanomaterials to agricultural plants. Previous work has highlighted carbon

2 ACS Paragon Plus Environment

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nanotubes in soil affecting seed germination3 as well as graphene toxicity towards several

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agricultural plants.4 Others, however, have investigated direct benefits of intentionally

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applying nanomaterials to agricultural soil. For example, carbon nanoparticles from

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biochar (that includes significant quantities of C60 and other fullerene-like materials) were

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found to boost wheat plant growth.5 Potential beneficial effects of fullerols on a wide

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variety of organisms have been reported.6 Still others employ a variety of metal and metal

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oxide nanoparticles for contaminant remediation or the prevention of plant disease.7-8

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Whether intentional or incidental, there are a wide variety and growing number of

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agricultural exposure routes to a diverse population of nanomaterials.1, 5, 7, 9-12

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Many nanomaterials, however, do not appear to be acutely toxic at environmentally

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relevant concentrations and exposures.13-15 Therefore, attention has begun to turn to

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secondary effects such as the trophic transfer (propagation through food webs) of these

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materials.1,

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delivering chemicals to cells

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agricultural soils may alter the transport kinetics and subsequent uptakes of pesticides and

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other agricultural chemicals into plants.21 In order to understand the potential for

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nanomaterials to exhibit such modulation of pesticide transport in an agricultural setting, a

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detailed understanding of interactions between nano-scale particles and agricultural

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chemicals is required. The large number of agricultural chemicals and the diversity of

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nano-scale phases challenge the experimental characterization of all possible interactions.

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A predictive computational approach with high efficiency and accuracy has the potential of

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accelerating the description of relevant interactions, while reducing reliance on expensive

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and labor-intensive experimental studies.

12, 16-18

There have also been suggestions of “Trojan Horse” mechanisms in 19-20

Recent studies have reported that nanomaterials in



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Some computational studies have been done using molecular dynamics (MD) to

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examine small molecule interactions with carbon nanotubes22 as well as polynomial fitting

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based on previous experimental results and a biological surface adsorption index (BSAI).23

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Discrete Molecular Dynamics (DMD) has been previously employed to simulate

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nanoparticle protein corona formation,24 polymeric nanoparticle oil dispersants,25 and the

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formation of stripe-like binary molecules on nanoparticle surfaces.26 Here, we consider

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scenarios of agricultural relevance; the adsorption of commonly used pesticides with

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reference nanomaterials such as fullerene, fullerols and ceria nanoparticles. By simulating

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the interactions between several nanoparticles and a library of relevant pesticides, we aim

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to provide a heuristic for quickly assessing combinations of chemical and nanoparticles of

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interest for possible experimental study. The details provided by DMD such as binding

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configuration, mechanisms, kinetics, and environmental sensitivity may also inform a so-

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called “safer-by-design” approach to risk management for engineered nanoparticles.

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Additionally, the computational efficiency of DMD allows for the simulation of larger,

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more complex systems over longer time scales than traditional MD. To evaluate the

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predictive power of this DMD-based approach, we compare the results with experimentally

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measured binding affinities.

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The nanoscales selected for the experimental portion of this study were a C60

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fullerene, fullerols with either 8 (fullerol-8) or 24 (fullerol-24) hydroxyl groups, and ceria

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nanoparticles. This set of nanoparticles represents carbon-based nanomaterials with

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varying degrees of hydrophobicity as well as a metal oxide nanoparticle. Ceria

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nanoparticles, a member of the broad class of metal oxide nanoparticles, find applications

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is catalysis, silica polishing, and fuel additives, resulting in significant potential for


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environmental expsure.27-33 Inclusion of hydroxylated fullerene derivatives is motivated by

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the systematic range of derivatizations of a C60 cage that these materials provide as well as

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environmental relevance of fullerols and other derivatized or environmentally aged carbon-

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cage nanomaterials34-36 as natural and incidental materials that may be present in soils.

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Such carbon-based nanomaterials are commonly found as a product of carbon-based

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material combustion, such as coal ash or fossil fuel emissions.1, 9-10, 37 Thirteen pesticides

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were selected from the US Environmental Protection Agency list of most commonly used

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pesticides in the United States, discarding some of those with high similarity in chemical

98

structure.

99

dichlorodiphenyldichloroethylene38

100

We

then

added

two

legacy (DDE,

pesticides

of

breakdown

particular product

interest, of

dichlorodiphenyltrichloroethane, DDT) and chlordane.39

101 102

METHODS

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DMD Simulations

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DMD is a subclass of MD approach which employs several improvements in calculation

105

efficiency, including implicit solvent, discretized potential functions, and re-calculation of

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atomic ballistic equations only when atoms have participated in a collision.40 These

107

features yield significant gains in calculation speed and efficiency, allowing for the

108

simulation of larger molecular systems on longer time scales. Interatomic potentials

109

include electrostatic, van der Waals (VDW), hydrogen bond, and solvation interactions.

110

Solvation energy in these implicit solvent simulations was calculated using the

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Lazaridis−Karplus EEF1 (Effective Energy Function 1) model.41 The Debye−Hückel

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approximation was used to model electrostatic screening, with a Debye length of 48 Å, 5 ACS Paragon Plus Environment


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corresponding to a solution ionic strength of approximately 4 mM×e2, as may be expected

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in freshwater and many pore water systems.

115

Small molecule ligands were modeled in all-atom DMD simulations with the

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MedusaScore force field,42 which was parameterized on a large set of small molecule

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ligands and transferrable to different molecular systems. The predictive power of

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MedusaScore has been validated in various benchmark studies, including recent CSAR

119

(community structure-activity resource) blind ligand-receptor docking prediction

120

exercises43-44 where the performance of MedusaScore was among the best approaches in

121

predicting the near-native ligand-binding poses and binding affinities.

122

All nanomaterials and molecules were constructed using the Avogadro molecular

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builder (v 1.1.1),45 including all heavy atoms and hydrogens. Ceria nanoparticles were

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assigned a diameter of 2.7 nm and included 500 total atoms. Atoms buried in the core of

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the particle were omitted to minimize the computational load for ceria nanoparticle

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simulations. For those species with potentially charged moieties, a pH of 7.4 was assumed

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and sodium ions were incorporated into the simulation to balance the system net charge.

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When incorporating cerium into the DMD simulation package, the VDW constants of

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atomic Ce27,

130

angles)48 were obtained from the literature. Specifically, the VDW radii (σVDW) and energy

131

(εVDW) of Ce were assigned 2.4 Å and 0.38 kcal/mol, respectively. The corresponding

132

desolvation energy (εsolv) was chosen as -1.0 kcal/mol (i.e., the nanoparticle is weakly

133

hydrophilic) in the EEF1 calculation. Clay aluminosilicate was similarly parameterized. Si:

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εVDW = 0.30 kcal/mol, σVDW = 1.92 Å, εsolv = -5.0 kcal/mol; Al: εVDW = 0.28 kcal/mol, σVDW =

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1.84 Å, εsolv = -5.0 kcal/mol (i.e., clay is more hydrophilic than CeO2).

46-47

and the hydrophobicity of CeO2 surfaces (from experimental contact


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Simulations were carried out with all possible pairings of a single nanoparticle and

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agricultural agent in a simulation box size of 80 Å3 with periodic boundary conditions.

138

Unless otherwise noted, the temperature for each simulation was 0.6 kcal/(mol·kB), which

139

corresponds approximately to 300 K. Constant temperature was maintained using the

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Anderson thermostat.49 After initialization, energy minimization was carried out for 10,000

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time steps of approximately 500 ps each. Production simulations were subsequently run for

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500,000 time steps (approximately 25 ns). We then extracted time-dependent data over the

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entire simulation for estimating the Gibbs free energy of binding (∆G) and the number of

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atomic contacts between nanoparticles and pesticides. Two atoms were defined as being in

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contact when within 6.5 Å of each other. All simulations were carried out on the Duke

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Compute Cluster, housed at Duke University. Each simulation was carried out on a single

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CPU core and completed in approximately 1.9 hours of computational time.

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To estimate the binding free energies, we first obtained the potential energy (E) of

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the unbound system by calculating the mean and standard deviation values for all time

150

points where the molecule was far from the nanoparticle. The molecule and nanoparticle

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were considered bound when the value of interatomic contacts was non-zero. During all

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such time points, we again calculated the mean and standard deviations of the potential

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energy of the bound state. We then used these two mean values to calculate the Δ of

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binding for each nanoparticle–molecule pair. Assuming that the conformational flexibility

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of a molecule in the unbound states was due to independent rotation of its rotatable bonds

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and that the molecule lost this conformational flexibility upon binding, changes in entropy

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were then estimated as the number of rotatable bonds in each pesticide, NRB, such that



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Δ ≈ − . Subsequently, changes in free energy were calculated as ∆ = ∆ − ∆.

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Log adsorption constants (log Kd) were then calculated per,

160 161

∆ = − ,

(1)

where kB (kcal mol-1 K-1) is the Boltzmann constant and T is the system temperature, 300 K.

162 163 164

Pesticide Adsorption Experiments 1. Materials

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Sublimed fullerene powder (C60, >99.5 %) was purchased from SES Research

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(Houston, TX, USA). Cerium oxide nanopowder (99%), and permethrin (99.5%) were purchased from J&K Chemical (Shanghai,

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China). Stock solutions of the pesticides were prepared in methanol, and were stored at -

171

20°C. The inorganic salts (NaH2PO4·2H2O and Na2HPO4·12H2O) were obtained from

172

VICTOR Co. (Tianjin, China).

173

Glass optical fibers coated with polyacrylate (thickness 35 mm; volume 15.4 µL/m)

174

were purchased from Polymicro Technologies (Phoenix, AZ). The fibers were cut to the

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desired length (generally 1–3 cm, depending on the expected partition coefficient), cleaned

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3 times by shaking in 50:50 methanol/water (v:v), then washed with deionized (DI) water

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to remove the solvent, and stored in DI water until further use.

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2. Preparation of nC60 stock suspension


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An nC60 stock suspension was prepared by magnetically stirring 150 mg C60 in 300

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ml DI water for 30 min, followed by sonicating the mixture (using a sonication probe, 100

181

W) in an ice bath in the dark for 1 h. This step was repeated 3 times. The concentration of

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C60 in the stock suspension was determined using an oxidation−toluene extraction

183

procedure.50

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3. Procedures of negligible depletion-solid-phase microextraction

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To determine the sorption coefficients of the pesticides to the fiber, the sorption

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isotherms of the six pesticides to the fiber were obtained using previously developed

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procedures.51-54 To initiate a fiber sorption experiment, first 20 ml of 10 mM phosphate

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buffer (NaH2PO4/Na2HPO4, pH 7.0) was transferred to each of a series of 20-ml amber

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glass vials. Next, a stock solution of a pesticide (in methanol) was added to each vial, and

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the volume percentage of methanol was kept below 0.1% (v/v) to minimize cosolvent

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effects. Next, a piece of fiber was added to the vial. The vial was capped and tumbled at 8

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rpm at room temperature in the dark for 28 d. The time required to reach sorption

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equilibrium was pre-determined. Then, the fibers were taken out, wiped with wet tissues,53-

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54

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other compounds) to analyze the mass of the pesticides on the fibers. The aqueous

196

solutions were extracted with hexane (and if needed, solvent-exchanged to methanol) to

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analyze the concentration of the pesticides in the dissolved phase. All experiments were

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run in duplicate. The sorption data were fitted with the linear sorption isotherm: Cfiber =

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Kfiber ⋅ CW, where Cfiber (mg/L) and CW (mg/L) are the equilibrium concentrations of a

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pesticide on the fiber and in the solution, respectively; and Kfiber (L/L) is the fiber–water

and extracted with hexane (for tefluthrin, bifenthrin, permethrin) or methanol (for all



201

distribution coefficient. The fiber sorption isotherms are shown in SI Fig. S1, and the fitted

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Kfiber values are summarized in SI Table S1.

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4. Adsorption isotherms of pesticides to nC60 or CeO2

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Prior to initiating an nC60 adsorption experiment, the stock suspension of nC60 was

205

diluted in 10 mM phosphate buffer (NaH2PO4/Na2HPO4, pH 7.0) to obtain an nC60

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suspension of ~30 mg/L. Next, 20 mL the suspension was transferred to a series of 20-ml

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amber glass vials. Then, the suspensions were spiked with a test pesticide (in methanol)

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and tumbled at 8 rpm at room temperature in the dark for 7 d. Afterward, fibers were added

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to the vials and were allowed to equilibrate for 28 d. Finally, the fibers were treated as

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described above to analyze the concentrations of the pesticides on the fibers. The

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concentrations of freely dissolved compounds were calculated based on the concentrations

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on the fibers and the fiber–water distribution coefficients. The concentrations of the

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pesticides on nC60 were calculated based on a mass balance approach.

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For the nCeO2 adsorption experiments, 0.002 g CeO2 was added to 20 mL electrolyte

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solution (10 mM phosphate buffer, NaH2PO4/Na2HPO4, pH 7.0)55, and then spiked with a

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test pesticide (in methanol) and tumbled at 8 rpm at room temperature in the dark for 7 d.

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Afterward, the vials were centrifuged (15,000 g, 10 min) and the supernatants were

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withdrawn and extracted as stated above to analyze the concentrations of pesticides in the

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aqueous solutions.56

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5. Analytical methods

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Chlorpyrifos was analyzed using a Waters high performance liquid chromatography

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system equipped with a symmetry reversed-phase C18 column (4.6 × 150 mm), and was


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detected with a Waters 2489 UV/visible detector at a wavelength of 300 nm; the mobile

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phase was acetonitrile–DI water (90:10, v:v; 1.0 ml/min). Tefluthrin, bifenthrin,

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permethrin, chlordane, and p,p’-DDE were analyzed with a gas chromatograph (GC6890N,

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Agilent Corp., Santa Clara, CA) equipped with an electron capture detector.57-59 No peaks

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were detected in the spectra for potential degraded/transformed products of the compounds

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tested.

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In order to compare experimental adsorption coefficients to DMD simulation results,

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experimental adsorption isotherms were extrapolated to very low adsorbent concentration,

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as done by Zou et al.60 Briefly, the concentration in the adsorbed phase, q (mg/kg), was

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plotted against the aqueous concentration of pesticide, Cw (mg/L). The slope of this curve

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resulted in the theoretical Kd coefficient for a single pesticide–nanomaterial interaction and

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was compared directly to DMD calculations.

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RESULTS AND DISCUSSION

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Figure 1 contains renderings of the 4 simulated nanomaterials and Table 1 summarizes the

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basic physicochemical properties and chemical structure of each pesticide. Counts of

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hydrogen bond acceptors include trifluoromethanes.61 An example plot of system energy

240

as a function of time from a simulation of chlorpyrifos and a ceria nanoparticle is provided

241

in Figure 2. These plots were then used to calculate the free energies of adsorption as

242

described in Methods.



C O

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H Ce

A

B

C

D

243 244

Figure 1. Rendering of the nanoparticles simulated. A: C60 fullerene; B: fullerol-8; C: fullerol-24; D: ceria

245

nanoparticle

246 247

Table 1. Names and basic properties of the agricultural chemicals for DMD simulations. MW

log

Net

Name

NRBb

NHBAc

NHBD Structure d

(g/mol)

KOW

Charge

Aldicarb

190.26

1.13

0

5

4

1

Atrazine

215.69

2.61

0

4

5

2

a


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Bifenthrin

422.87

6.00

0

7

5

0

Carbofuran

221.26

2.32

0

3

4

1

Chlordane

409.76

6.16

0

0

0

0

Chlorpyrifos

350.59

4.96

0

6

4

0

DDE

318.02

6.51

0

2

0

0

Dicamba

221.03

2.21

-1

2

3

1



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Glyphosate

169.07

-3.4

-3

4

6

4

Imidacloprid

255.66

0.57

0

4

7

1

Methyldithiocarbamate

107.19

N/A

-1

1

1

1

Metolachlor

283.80

3.13

0

7

3

0

Permethrin

391.29

6.5

0

7

3

0

Tefluthrin

418.74

6.4

0

6

5

0

Terbufos

288.42

4.48

0

8

2

0

248

a

249

b

250

c

Net charges were calculated at pH 7 NRB: number of rotatable bonds

NHBA: number of hydrogen bond acceptors


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251


d

NHBD: number of hydrogen bond donors

252

253 254

Figure 2. A sample of simulation data output for 500,000 time steps. Green: change in system potential

255

energy relative to initial conditions. Blue: number of inter-atomic contacts between the pesticide and

256

nanoparticle, in this case between chlorpyrifos and nano ceria.

257 258

The resulting log Kd (adsorption coefficients) from DMD simulations are shown in

259

Figure 3 for all nanoparticle–pesticide pairs. Across all nanomaterials, these adsorption

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coefficients cover a wide range, from near 0 (methyldithiocarbamate on highly

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hydroxylated fullerols) to 8.85 (tefluthrin on slightly hydroxylated fullerols). DDE

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exhibited the strongest adsorption to fullerenes of all simulated pesticides. This may be

263

surprising, as it is not the most hydrophobic pesticide in the library, and it contains the

264

same number of aromatic rings as several other species. However, the unique geometry of

265

DDE allows its two chlorobenzene rings to rotate and “cradle” the nanoparticle, resulting 15 ACS Paragon Plus Environment


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in its extremely high binding affinity (Figure 4A). There are some noteworthy trends

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observed between nanomaterials, as well. First, adsorption to fullerol-8 is nearly

268

universally stronger than that to C60 fullerenes. However, further hydroxylation to fullerol-

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24 causes a decrease in adsorption strength for all tested chemicals. This drop is highly

270

dependent on the chemical agent; in some cases, it returns to approximately the same value

271

as fullerene adsorption (i.e. aldicarb), in some cases remains stronger than on fullerenes

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(i.e. atrazine), and in many others becomes significantly weaker. We hypothesize that these

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differences are due to the balance between interaction mechanisms. While the fullerenes

274

rely solely on van der Waals, π–π, and hydrophobic interactions for adsorption, fullerol-8

275

nanoparticles also have the capacity for hydrogen bonding while still allowing access to

276

the hydrophobic carbon surface. A typical example of this phenomenon can be seen in

277

Figure 4B, in which a bifenthrin molecule was observed to simultaneously exhibit both

278

hydrogen bonding (dotted yellow line) and strong hydrophobic interactions with a single

279

fullerol-8 nanoparticle. This mechanism is further evidenced in noting that chlordane, with

280

no capacity for forming hydrogen bonds, does not exhibit enhanced binding to fullerol-8

281

particles. An abundance of hydroxyl groups in fullerol-24 blocks the access to hydrophobic

282

surfaces, thus reducing the potential for hydrophobicity-driven adsorption (Figure 4C).

283

The weakest binding was observed for charged and more water-soluble pesticides, as

284

may be expected. Methyldithiocarbamate exhibited particularly weak binding on highly

285

hydroxylated Fullerols. This is likely due to its high water solubility and limited capacity

286

for hydrogen bonding. Aldicarb and atrazine, while not charged, bound weakly to each

287

nanoparticle due to slight water solubility and a lack of π–π interaction capacity. The

288

strongest binding, in contrast, was generally observed for pesticides with at least 1


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aromatic ring and significant capacity for hydrogen bonding on slightly hydroxylated

290

fullerols, as described above.

291

In most cases, adsorption on ceria nanoparticles is approximately equal to or weaker

292

than that on fullerenes. This is generally due to the lower hydrophobicity of the ceria

293

nanoparticle. Terbufos most significantly exhibited stronger adsorption to ceria

294

nanoparticles than to fullerenes or fullerols. We hypothesize that this is due to its highly

295

flexible “arms” containing oxygen and methyl groups, available to interact with the surface

296

through multiple mechanisms simultaneously (Figure 4D). These mechanisms are more

297

diverse than those available for adsorption to the purely carbon-based fullerenes.

298

Additionally, due to the lack of hydrogen bonding capacity, pristine ceria is still more

299

hydrophobic than either fullerol (see Methods),48 and previous work has illuminated

300

several mechanisms of oxygen-mediated adsorption onto CeO2 surfaces which appears to

301

enhance turbufos binding to the ceria nanoparticle.62 10 9

Fullerene

Fullerol-8

Fullerol-24

Ceria

8 7

log Kd

6 5 4 3 2 1 0

302 303

Figure 3. Predicted log Kd values for each nanoparticle-pesticide pair from DMD simulations



304 305

A

C B O H Ce Cl F S P

C

D

E

306 307

Figure 4. A collection of simulation snapshots highlighting interaction mechanisms. A: DDE interaction with

308

a fullerene; B: bifenthrin interaction with fullerol-8; C: bifenthrin with fullerol-24; D: terbufos with a ceria

309

nanoparticle. Yellow dotted lines indicate the formation of hydrogen bonds; E: the two observed binding

310

modes of permethrin on fullerenes

311 312

The increase in the number of hydroxyl groups from fullerene to fullerol-8 and

313

fullerol-24 results into increased capability to form hydrogen bonds with water molecules,

314

and thus, increasing water solubility. The fact that fullerol-8 interacted most strongly with

315

most chemicals also suggests that the adsorption of those chemicals is not driven solely by

316

their low water solubility. We confirmed this by plotting the predicted adsorption

317

coefficients against each chemical’s log octanol–water partition coefficient, KOW, and 18 ACS Paragon Plus Environment

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performed correlation analysis (SI, Figures S4-7). Pearson correlation coefficients between

319

calculated adsorption coefficients and log KOW for adsorption to fullerenes, fullerol-8,

320

fullerol-24, and ceria nanoparticles are 0.78, 0.75, 0.58, and 0.82, respectively. Thus, while

321

there was a trend seen between adsorption and chemical hydrophobicity in these cases, this

322

property alone is insufficient to precisely predict actual adsorption strength and rank

323

between chemicals. This is because KOW values fail to account for other factors important

324

to molecular adsorption to nanomaterials including molecular flexibility, entropic effects,

325

and specific modes of interaction such as hydrogen bonding and π–π interactions. The

326

many physical factors that must be considered for all species involved make prediction

327

difficult from a single or small subset of simple physical parameters. The use of DMD,

328

however, allows for adsorption coefficient prediction with little prior knowledge of the

329

chemical or nanomaterial in question other than chemical structure, and can also provide

330

additional insight such as the binding mechanisms at work as well as kinetic and

331

configurational information. System parameters such as ionic strength and temperature can

332

also be freely adjusted in DMD, which allows for the facile investigation of a diverse set of

333

scenarios.

334

Importantly, we also experimentally verified the predictive power of our DMD

335

simulations. We selected 2 nanomaterials (fullerenes and ceria nanoparticles) and 6

336

pesticides with which to obtain experimental adsorption isotherms. From these isotherms,

337

we extracted values for Kd and compared them to the simulation results, shown in Figure 5.

338

The dotted line represents the ideal case in which the experimental results exactly equal the

339

simulated predictions. The average absolute difference in experimental and predicted log

340

Kd values is 0.16. A correlation analysis yielded a Pearson correlation coefficient of 0.97.



341

There is one outlier adsorption, permethrin on fullerenes. We hypothesize that this may be

342

due to the long, fairly inflexible structure of permethrin. In examining simulation

343

snapshots, we observed that permethrin exhibited two distinct binding modes on fullerenes

344

(Figure 4E). In one mode, one benzene ring and the hydrophobic triangular tail are bound

345

to the nanoparticles; in the other, both benzene rings are bound to the surface, but the tail is

346

not due to insufficient flexibility of the molecule. This limits the potential binding capacity

347

on a single fullerene particle. However, this limitation is not present on the much larger

348

ceria nanoparticles, and is not expected to be present for a small cluster of fullerenes, as

349

was likely the case during adsorption isotherm experiments. This hypothesis will be tested

350

in future studies. Overall, this experimental verification confirms the validity and high

351

precision of our simulations, which can be performed far more quickly and economically

352

than experimental measures while also providing a rich variety of mechanistic information. 9.5

Fullerenes Ceria

9.0

log Kd - Exprimental

8.5 8.0 7.5 7.0 6.5 6.0 5.5 5.0 4.5 4.5

353

5.5

6.5

7.5

8.5

9.5

log Kd - DMD Simulations

354

Figure 5. Correlation plot comparing log Kd values from DMD simulations and experimental adsorption

355

isotherms for 6 pesticides adsorbing to fullerenes (green squares) and nano ceria (orange triangles).


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356

In using this method to determine which nanoparticle–pesticide pairs may

357

potentially affect pesticide transport and impact through binding, one must also consider

358

competitive adsorption surfaces already present in the environment. To this end, we

359

selected 5 of the strongest interacting pesticides and compared their adsorption coefficients

360

on nanoparticles to those on a clay surface, which is one of several possible such

361

competitive surfaces. For a model clay surface, we constructed a fragment of kaolinite 1

362

aluminosilicate layer thick and approximately 2 × 3.5 nm2 in size, shown in Figure 6a with

363

a tefluthrin molecule for illustration. As shown in Figure 6b, we found that each of these 5

364

pesticides adsorbed much more weakly to the clay surface than to Fullerol-8 nanoparticles,

365

and much more weakly than to fullerenes in all but one case. Because the adsorption onto

366

the nanomaterials is so much stronger than onto clay, these adsorption processes would be

367

expected to be environmentally relevant even at low nanoparticle concentrations.

368

Additionally, the mechanistic insights described above will be particularly important in

369

considering weathered nanomaterials, or those which have previously interacted with

370

organic matter, which as a result are expected to exhibit significantly different binding

371

capacities for organic co-contaminants, further highlighting these findings’ environmental

372

relevance. Mechanistic and quantitative results from this study will form the basis of

373

experimental and targeted computational studies using those nanoparticle-pesticide pairs

374

with the highest binding affinities. These studies should capture the complexity of

375

agricultural soil conditions more completely. Specifically, additional studies on the impact

376

of various forms of organic matter on the transport of the pesticides which most strongly

377

bind with nanoparticles are left for future studies. However, effects are expected to range



378

from an additional sink of pesticide through adsorption and competitive binding with clay

379

and nanoparticle surfaces, depending on the form of organic matter.63

A

10

B

Fullerene

Fullerol-8

Clay

9

8

log Kd

7

6

5

4

3

2

380

Chlordane

Chlorpyrifos

DDE

Imidaclorpid

Tefluthrin

381

Figure 6. A: Rendering of tefluthrin adsorbed to the surface of kaolinite fragment. B: Comparing select

382

pesticide adsorption to fullerene (Green), fullerol-8 (Blue) and kaolinite clay (Brown hashed) surfaces.

383

Acknowledgement

384

This material is based upon work supported by the National Science Foundation (NSF) and

385

the Environmental Protection Agency (EPA) under NSF Cooperative Agreement EF-

386

0830093, Center for the Environmental Implications of NanoTechnology (CEINT). Any

387

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

388

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

389

has not been subjected to EPA review and no official endorsement should be inferred. 22 ACS Paragon Plus Environment

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390


This work was further supported by NSF CAREER CBET-1553945 (Ding) and the

391

Ministry of Science and Technology of China 2014CB932001 (Chen).

392

Supplementary Information

393

Additional data pertaining to experimental adsorption isotherms, including statistical

394

analysis, controls, and method calibration. Also includes correlation plots for KOW vs

395

calculated Kd values.

396 397

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