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Environmental Processes
Investigating and Modeling the Transport of the ‘New-Horizon’ Reduced Graphene Oxide—Metal Oxide Nanohybrids in Water-Saturated Porous Media Dengjun Wang, Yan Jin, Chang Min Park, Jiyong Heo, Xue Bai, Nirupam Aich, and Chunming Su Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b06488 • Publication Date (Web): 27 Mar 2018 Downloaded from http://pubs.acs.org on March 27, 2018
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Environmental Science & Technology
Investigating and Modeling the Transport of the ‘New-Horizon’ Reduced Graphene Oxide—Metal Oxide Nanohybrids in WaterSaturated Porous Media Dengjun Wang,†,* Yan Jin,§ Chang Min Park,∆ Jiyong Heo,¶ Xue Bai,# Nirupam Aich,¥ and Chunming Su‡,* †
National Research Council and ‡Groundwater, Watershed, and Ecosystem Restoration Division, National Risk Management Research Laboratory, Office of Research and Development, United States Environmental Protection Agency, Ada, Oklahoma 74820, United States §
∆
Department of Plant and Soil Sciences, University of Delaware, Newark, Delaware 19716, United States
Department of Environmental Engineering, Kyungpook National University, Buk-gu, Daegu 41566, South Korea
¶
Department of Civil and Environmental Engineering, Korea Army Academy, Young-Cheon, Gyeongbuk 38900, South Korea #
Key Laboratory of Integrated Regulation and Resource Development on Shallow Lake of Ministry of Education, College of Environment, Hohai University, Nanjing 210098, Jiangsu Province, China ¥
Department of Civil, Structural, and Environmental Engineering, University at Buffalo, The State University of New York, Buffalo, New York 14260, United States
*
Corresponding authors: Dengjun Wang E-mail:
[email protected] Phone: (580) 436-8828 Fax: (580) 436-8703 and Chunming Su E-mail:
[email protected] Phone: (580) 436-8638 Fax: (580) 436-8703
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Environmental Science & Technology
ABSTRACT
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Little is known about the fate and transport of the ‘new-horizon’ multifunctional
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nanohybrids in the environment. Saturated sand-packed column experiments (n=66) were
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therefore performed to investigate the transport and retention of reduced graphene oxide
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(RGO)—metal oxide (Fe3O4, TiO2, and ZnO) nanohybrids under environmentally-relevant
9
conditions (mono- and di-valent electrolytes and natural organic matter). Classical colloid
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science principles (Derjaguin-Landau-Verwey-Overbeek (DLVO) theory and colloid filtration
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theory (CFT)) and mathematical models based on the one-dimensional convection-dispersion
12
equation were employed to describe and predict the mobility of RGO-Fe3O4, RGO-TiO2, and
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RGO-ZnO nanohybrids in porous media. Results indicate that the mobility of the three
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nanohybrids under varying experimental conditions is overall explainable by DLVO theory and
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CFT. Numerical simulations suggest that the one-site kinetic retention model (OSKRM)
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considering both time- and depth-dependent retention accurately approximated breakthrough
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curves (BTCs) and retention profiles (RPs) of the nanohybrids concurrently; whereas, others
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(e.g., two-site retention model) failed to capture the BTCs and/or RPs. This is primarily because
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blocking BTCs and exponential/hyperexponential/uniform RPs occurred, which is within the
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framework of OSKRM featuring time- (for kinetic Langmuirian blocking) and depth-dependent
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(for exponential/hyperexponential/uniform) retention kinetics. Employing fitted-parameters
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(maximum solid-phase retention capacity: Smax=0.0406–3.06 cm3/g; and first-order attachment
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rate coefficient: ka=0.133–20.6 min–1) extracted from the OSKRM and environmentally-
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representative physical variables (flow velocity (0.00441–4.41 cm/min), porosity (0.24–0.54),
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and grain size (210–810 µm)) as initial input conditions, the long-distance transport scenarios (in
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500-cm long sand columns) of nanohybrids were predicted via forward simulation. Our findings
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address the existing knowledge gap regarding the impact of physicochemical factors on the
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transport of the next-generation, multifunctional RGO—metal oxide nanohybrids in the
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subsurface.
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INTRODUCTION
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Compared with single-component nanomaterials (NMs), the ‘new-horizon’ nanohybrids
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that are nano-/hierarchical assemblies of multiple NMs hold great promise for addressing issues
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and meeting challenges within water-energy-agriculture-environment nexus,1 due to their
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enhanced properties, optimized multi-functionalities, and maximized performances.2, 3 Among
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others, the reduced graphene oxide (RGO)—metal oxide nanohybrids are the most commonly
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pursued combinations owing to their exceptional and highly-tunable physicochemical (electronic,
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thermal, mechanical, optical, photocatalytic, and magnetic) and biological (bioactive,
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biocompatible, and antimicrobial) properties4 arising from the synergistic interplay of parent NM
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components within the nanoheterostructures.5,
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RGO-titanium dioxide (RGO-TiO2), and RGO-zinc oxide (RGO-ZnO) nanohybrids have
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attracted immense interest for various applications including drug delivery,7 sensors,8
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supercapacitors,9 solar cells,10 biomolecule immobilizer,11 and environmental remediation
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(removing heavy metals, organic contaminants, and pathogens).12 Of considerable interest within
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the framework of environmental remediation is the employment of magnetically-recyclable
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RGO-Fe3O4
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photoredox/photocurrent/photocatalytic degradation of diverse recalcitrant compounds.12, 13 This
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is because nanoheteroconjugating Fe3O4, TiO2, and ZnO NMs with two-dimensional RGO
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nanosheets that can strongly pre-concentrate contaminants via sorption, effectively inhibits the
and
easily-regenerative
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For example, RGO-magnetite (RGO-Fe3O4),
RGO-TiO2
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RGO-ZnO
nanohybrids
for
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aggregation and surface passivation of metal oxide NMs14 and decreases the recombination rate
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of photo-generated electron-hole pairs,12,
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efficiency of contaminants. Increasing production and use of multifunctional RGO—metal oxide
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nanohybrids necessitate fundamental understandings of environmental remediation and potential
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environmental/human health impacts and risks (e.g., unknown toxicity of nanohybrids, or
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detached RGO, TiO2, and ZnO NMs, or dissolved Zn(II) ions)2, 16 due to unintentional release
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during nanohybrids production, usage, and other relevant end-of-life stages17 (e.g., wastewater
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treatment plant18, 19 and land application of sewage sludge20).
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thereby significantly improving degradation
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Aggregation and transport propensities of single-component NMs and multi-component
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nanohybrids dictate their performances in environmental remediation (e.g., in-situ contaminated
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site nanoremediation),21-23 fate,24 transformations,25 and potential environmental/human health
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impacts and risks.26 Over the past decade, substantial efforts have been devoted to unravelling
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the transport of singular NMs in the subsurface, documenting that NMs’ mobility is governed by
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the interplay of physicochemical properties of NMs (e.g., size/shape/coating/surface charge) and
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porous media (e.g., grain size/porosity/surface chemistry), hydrodynamics (e.g., flow velocity),
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and surrounding solution chemistries (e.g., pH/ionic strength (IS)/natural organic matter
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(NOM))27,
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Verwey-Overbeek (DLVO) theory29,
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semi-quantitatively. However, little is known about the transport of nanohybrids in the
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subsurface; and the critical knowledge gaps such as “Will nanohybrids behave/transport
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similarly (or distinctly) as NMs do?” and “Can DLVO theory and CFT qualitatively or semi-
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quantitatively describe the transport behaviors of nanohybrids as well?” need to be addressed
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before mass production and widespread application of multifunctional nanohybrids occur.
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, which can be explained by colloid science principles of Derjaguin-Landau30
and colloid filtration theory (CFT)31 qualitatively or
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Numerical simulations have long-been used to mechanistically describe the transport and
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retention of colloids/NMs in porous media, and model-fitted parameters, in turn, furnish valuable
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insights into understanding the intrinsic mechanisms dominating colloids/NMs’ mobility. For
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instance, upon incorporating a depth-dependent straining term into the one-dimensional (1D)
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convection-dispersion
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hyperexponential deposition of colloids, introducing a new mechanism (physical straining) for
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colloid retention. In addition to the data-fitting functionality (inverse-algorithm), numerical
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simulation (e.g., forward-algorithm) also has enormous potential to forecast possible outcomes of
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colloids/NMs with adjustable input parameters including initial boundary conditions.33 This is of
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practical significance given that laboratory experiments are costly, time-consuming, or even
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impossible to implement due to certain limitations. Specifically, forward simulation can predict
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the long-distance transport of colloids/NMs with varying input conditions (e.g., flow velocity,
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porosity, and grain size)33 commonly encountered in the subsurface, some of which cannot be
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achieved or maintained in laboratory experiments (e.g., one cannot experimentally investigate
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the mobility of colloids/NMs at low subsurface flow velocity scenarios since low-flow-rate
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injection of NMs results in particle clogging in packed-column tubing system due to
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aggregation/agglomeration).
equation
(CDE),
Bradford
et
al.32
accurately
simulated
the
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This research is set forth to bridge the knowledge gap regarding the influence of
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physicochemical factors on the transport of RGO—metal oxide nanohybrids in the subsurface.
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The most influential environmental factors (mono- and di-valent electrolytes and NOM)34
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controlling NMs’ mobility were chosen. Saturated sand-packed column experiments were
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conducted to investigate the transport and retention of RGO-Fe3O4, RGO-TiO2, and RGO-ZnO
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nanohybrids under environmentally-relevant concentrations of NaCl, CaCl2, and NOM. DLVO
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theory, CFT, and numerical simulation were employed to delineate the transport behaviors of
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nanohybrids. Fitted-parameters from the best modelling approach were used in combination with
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the physical variables commonly encountered in the subsurface to predict long-distance transport
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(in 500-cm long sand columns) of nanohybrids via forward-simulation. Coupling laboratory
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experiments with numerical simulations provides a robust venue for accurately describing and
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assessing the mobility of the ‘new-horizon’ nanohybrids in the subsurface.
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MATERIALS AND METHODS
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Preparation of RGO-Fe3O4, RGO-TiO2, and RGO-ZnO Nanohybrid Influent
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Suspensions and Solution Chemistries
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The RGO-Fe3O4 nanohybrid stock suspension (10,000 mg/L) well-dispersed in acetone
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(~80%, v/v) was purchased from Sigma-Aldrich (product #803804). The RGO-TiO2 and RGO-
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ZnO nanohybrid powders were synthesized in-house. Physicochemical properties of the RGO-
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Fe3O4, RGO-TiO2, and RGO-ZnO nanohybrids were characterized using multiple techniques
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including high-resolution transmission electron microscopy (HR-TEM), field-emission scanning
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electron microscopy (FE-SEM), Fourier-transform infrared (FT-IR) spectroscopy, X-ray
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photoelectron spectroscopy (XPS), and ultraviolet-visible (UV-Vis) spectroscopy. Detailed
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procedures of synthesizing the RGO-TiO2 and RGO-ZnO, and physicochemical characterizations
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of the three nanohybrids are provided in the Supporting Information (SI) S1. Environmentally-
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relevant solution chemistries (Table 1) were chosen to examine the mobility of RGO-Fe3O4,
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RGO-TiO2, and RGO-ZnO nanohybrids in water-saturated porous media including mono- (1, 10,
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50, and 100 mM NaCl) and di-valent (0.5, 1, 5, and 10 mM CaCl2) electrolytes, and the presence
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of NOM (0, 1, 5, and 10 mg C/L Suwannee River humic acid (SRHA); procedures for preparing
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SRHA stock suspension are given in SI S2). The nanohybrid influent suspensions at the desired
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solution chemistries (Table 1) were freshly prepared via ultrasonication (100 W and 42 kHz;
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Branson 3510R-DTH sonicator, Danbury, CT) for 30–60 min at 25 °C. The concentration of all
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nanohybrid influents for column experiments was 10 mg/L, and influent pH was unadjusted
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(pH=7.0–7.5). Compared to the RGO-TiO2 and RGO-ZnO nanohybrids suspended in water
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(100%), the RGO-Fe3O4 influent suspension includes ~0.08% (v/v) acetone (10,000 mg/L stock
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suspension was diluted 1,000 times to obtain 10 mg/L influent).
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Column Experiments
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Ottawa sands (U.S. Silica, Berkeley Springs, WV) having an average diameter of 360-µm
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were selected as representative aquifer materials. Prior to use, the sands were cleaned using a
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sequential acid-deionized (DI) water wash procedure.35 Transport experiments were conducted in
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duplicates using glass chromatography columns (1.7-cm i.d. × 10-cm long). The column was
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dry-packed with cleaned sands, purged with CO2 gas for 30 min, and then slowly-saturated with
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DI water. Column porosity was gravimetrically determined to be ~0.334. Following the
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saturation step, a nonreactive tracer (50 mM NaNO3) experiment was performed to determine the
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hydrodynamic properties of the packed-columns, including pore-water velocity and dispersivity,
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which were then used in numerical modeling of the transport of nanohybrids in the column
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experiments.
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Following the completion of tracer experiment, the column was pre-equilibrated with
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desired background electrolyte solution (Table 1) to standardize pore-water solution chemistries.
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A two-step transport experiment was then initiated by injecting 3 pore volumes (PVs) of 10
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mg/L of nanohybrid influents (Table 1) followed by elution with 7 PVs of nanohybrid-free
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background electrolyte solution. Darcy velocity was maintained at 0.44 cm/min35 for all
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experiments. Column effluents were collected continuously via a fraction collector. After
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completion of each breakthrough experiment, the spatial distribution of nanohybrids retained in
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the column was determined (dissection experiment; SI S3). The concentrations of RGO-Fe3O4,
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RGO-TiO2, and RGO-ZnO nanohybrids in the effluents and retentates (collected from dissection
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experiments) were determined spectrophotometrically at their peak wavelengths (264, 252, and
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264 nm, respectively, for RGO-Fe3O4, RGO-TiO2, and RGO-ZnO nanohybrids; SI Figure S4).
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Calibration curves were constructed by diluting 10 mg/L nanohybrid influent suspension, which
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was linear within the concentration range of 0–10 mg/L (R2=1.0; SI Figure S5). Mass balances
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were calculated by comparing the quantities of nanohybrids recovered in the effluents and
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retentates to those injected in the column.
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Electrokinetic Properties and Hydrodynamic Sizes of Nanohybrids and Sand
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Grains
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Electrokinetic properties and hydrodynamic sizes of nanohybrids indicating their
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aggregation and transport propensities36 were determined. Specifically, electrophoretic mobility
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of RGO-Fe3O4, RGO-TiO2, and RGO-ZnO nanohybrids in the influents (10 mg/L) and sand
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grains (pulverized colloidal particles of sands as surrogates)35 at desired solution chemistries
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(Table 1) was determined using the Zetasizer Nano-ZS ZEN3600 analyzer (Malvern Instruments
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Ltd., Malvern, Worcestershire, U.K.) in triplicates at 25 °C, and then converted to zeta (ζ)-
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potential using the Smoluchowski equation.37 The hydrodynamic diameter (DH) of nanohybrids
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in the influents was determined using dynamic light scattering (DLS) on the same Zetasizer
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analyzer in triplicates at 25 °C.35 Prior to measurements, ultrasonication was performed in a
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water bath (25 °C) at 100 W and 42 kHz for 30 min to obtain a homogeneous suspension. The ζ-
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potential and DH values of nanohybrids and sand grains were used to calculate the average
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interaction energy between nanohybrids and sand grains under different experimental conditions
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using DLVO theory. Within the framework of the DLVO theory, the van der Waals and
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electrostatic double layer interaction energies were calculated for the nanohybrid-sand system,
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assuming a sphere-plate configuration (SI S4).
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Numerical Simulations
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The breakthrough curves (BTCs) and retention profiles (RPs) of RGO-Fe3O4, RGO-TiO2,
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and RGO-ZnO nanohybrids under different experimental conditions were simulated using the 1D
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CDE with one- (site 1) or two-site (sites 1 and 2, respectively) kinetic retention. The one-site
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kinetic retention model (OSKRM) is described as follows:40
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+
= −
[1]
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= − [2]
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= 1 −
! "#
[3]
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where θ is volumetric water content [–]; C is nanohybrid concentration in aqueous-phase [NL–3,
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where N and L denote number and length, respectively]; t is time [T, where T denotes time]; ρb is
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bulk density of porous media [ML–3, where M denotes mass]; S is nanohybrid concentration on
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solid-phase [NM–1]; x is the spatial coordinate [L]; D is the hydrodynamic dispersion coefficient
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[–]; q is flow rate [LT–1]; ka and kd are first-order attachment and detachment rate coefficients [T–
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1
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retention; Smax is the maximum solid-phase retention capacity [NM–1]; dc is average diameter of
], respectively; ψ is a dimensionless function considering both time- and depth-dependent
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sand grains; and β is an empirical parameter controlling the shape of RPs [–]. The OSKRM can
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describe time-dependent BTCs (e.g., kinetic Langmuirian blocking)41 and RPs that are uniform,
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exponential, or hyperexponential with depth.40, 42 The first and second terms on the right-hand
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side of equation [3] account for time-dependent Langmuirian attachment,41 and depth-dependent
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retention, respectively. Exponential RPs occur when β=0, consistent with CFT prediction.31
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Conversely, when β>0, hyperexponential RPs occur with greater retention near the column
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inlet.43 Four model formulations (M1–M4) were considered within the framework of OSKRM
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(SI Table S1): M1—CFT when ψ=1; M2—time-dependent Langmuirian attachment when β=0;
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M3—depth-dependent retention when β=0.432;32 and M4—both time- and depth-dependent
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retention when β=0.432 and (1-S/Smax)
