Cotransport of graphene oxide and Cu(II) through saturated porous media

Science of the Total Environment 550 (2016) 717–726

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Cotransport of graphene oxide and Cu(II) through saturated porous media D.D. Zhou a,1, X.H. Jiang a,1, Y. Lu b, W. Fan a,⁎, M.X. Huo a, J.C. Crittenden a,c a b c

School of environment, Northeast Normal University, Changchun 130117, China Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130021, China School of Civil & Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• GO had fairly high mobility and could serve as an effective carrier of Cu. • The GO-facilitated Cu transport increase with increasing concentration of GO and reduce as the IS increased. • The later introduced GO can complex the pre-adsorbed Cu from the sand surface and then mobilize together. • The GO–Cu cotransport behavior can be described by modeling and SEM analysis.

a r t i c l e

i n f o

Article history: Received 12 November 2015 Received in revised form 12 January 2016 Accepted 22 January 2016 Available online 2 February 2016 Editor: Kevin V. Thomas Keywords: Graphene oxide Cu Cotransport Concentration Ion strength Porous media

a b s t r a c t This study examines the cotransport of graphene oxide (GO) and Cu in porous media. The impacts of GO concentration and ion strength (IS) on Cu transport in laboratory packed columns were investigated. The results indicated that GO had fairly high mobility at a IS of 1 mM, and could serve as an effective carrier of Cu(II). The facilitated transport was found to increase with increasing concentration of GO (CGO). The peak effluent concentration (C/C0)max of Cu was 0.57 at CGO of 120 mg/L and IS = 1 mM and 0.13 at 40 mg/L and IS = 1 mM. The Cu appears to be irreversibly adsorbed by the sand because no Cu appeared in the effluent in the absence of GO. However, the GO-facilitated Cu transport was reduced as the IS increased from 1 to 1000 mM. In fact, the facilitated transport was zero percent at an IS of 1000 mM. Particle size analysis, Zeta potential measurements and DLVO calculations demonstrated that higher IS values made the GO became unstable and it flocculated and attached to the sand. We also fed GO into the column pre-equilibrated by Cu as sequential elution experiments and found that the later introduced GO can complex the pre-adsorbed Cu from the sand surface because GO has a higher adsorption affinity for Cu. An advection–dispersion–retention numerical model was able to describe the Cu and GO transport in the column. Our work provides useful insights into fate, transport and risk assessment of heavy metal contaminants in the presence of engineered nanoparticles. © 2016 Elsevier B.V. All rights reserved.

⁎ Corresponding author. E-mail address: [email protected] (W. Fan). 1 D. D. Zhou and X. H. Jiang contributed equally to this manuscript.

http://dx.doi.org/10.1016/j.scitotenv.2016.01.141 0048-9697/© 2016 Elsevier B.V. All rights reserved.

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D.D. Zhou et al. / Science of the Total Environment 550 (2016) 717–726

1. Introduction Understanding the factors affecting transport and fate of heavy metals in porous media is essential for processes such as landfill leachate filtration, land application of sewage sludge and groundwater recharge (Datry et al., 2004; Lim et al., 2015; Wu et al., 2015). Transition metals (such as Cu2+ and Cr6+) may show strong affinity toward the solid matrix and significantly lower the aqueous phase concentration (Grolimund, 2005; Zeng et al., 2015). That could induce considerable retention of these trace metals in natural porous media (Akbour et al., 2002). However, engineered nanoparticles (ENPs) that may be introduced into aquifers may have a strong affinity for heavy metals (Shaker, 2015). Consequently, mobile ENPs with high adsorption capacities may act as rapid carriers of the bound metals, and transport these heavy metals with the groundwater flow (Zhuang et al., 2003; Qi et al., 2014a, b). On the other hand, the ENPs can be removed in small pore channel or by adsorption onto soil particles, and will not facilitate transport in such cases (Syngouna and Chrysikopoulos, 2015). Accordingly, insight into these mechanisms is important to the understanding of the risk that ENPs have on the transport of heavy metals. Graphene oxide (GO) is a new kind of carbonaceous nanomaterial and is widely used in various industries. It contains a large number of hydrophilic groups, such as carboxylic, phenolic and hydroxyl groups. Consequently, GO sheets can be easily dispersed in water and can flocculate in the presence of electrolytes (Konkena and Vasudevan, 2012; Wu et al., 2013). Several earlier works found that metal–GO complex could be attributed to the “edge to edge” and “face to face” complexation. This includes chelation with carboxylate groups at the edges of the GO and intercalation through alkoxide or dative bonds from carbonyl and hydroxyl groups at the basal planes (Wu et al., 2013; Fan et al., 2015a). Previous studies have investigated the adsorption of heavy metals to GO. The maximum adsorption capacity of Cu(II) adsorption on GO at 293 K was measured as 493.69 mg/g (Li et al., 2014), which was much higher than other metal ions, such as Pb (II) (344 mg/g) (Jia and Lu, 2014), Zn(II) (246 mg/g) (Wang et al., 2013) and Cr(III) (92.65 mg/g) (Yang et al., 2014). Thus, it is hypothesized that the transport of GO in porous media could potentially involve GO-associated heavy metal movement, especially for the common ions such as Cu(II). Although there are many studies refer to the interactions between nanoparticles and trace metals, co-transport behavior of GO and Cu in saturated porous media has not been investigated thoroughly. Whether the GO-facilitated Cu transport occurs or not and how the environmental factors impact the process still need to reveal. The transport of metals or GO alone in porous media are generally affected by a number of physical, chemical, and biological factors, including soil grain size and shape, fluid conditions, ionic strength, soil organic matter content, dissolved organic matter and microbial activity, to name a few (Lucia and Xu, 2012; Lanphere et al., 2013; Fan et al., 2015a, b; Kulikowska et al., 2015). When it comes to facilitated transport there are two basic factors that impact metal-GO binding and they are GO concentration and ionic strength. Therefore, the objectives of this work were to explore if GO can facilitate Cu(II) in soils and understand the mechanisms that are involved. We conducted laboratory column tests using granular natural sand with different GO concentrations (40, 80, 120 mg/L) and different ionic strengths (1, 10, 100, 1000 mM NaCl) at pH 6.0 ± 0.2. The potential transportenhancement effect of GO is impacted by the aggregation of GO which depends on the IS, and complexation between GO and Cu(II) and we examined these issues in our study. 2. Materials and methods 2.1. Porous media Natural sand sampled from vadose zone in Changchun (Northeast China) was used as the porous medium. The porous media was sieved

and had a size between 355 and 500 μm, and the approximate average grain diameter was 420 μm that was measured by Bettersizer 2000 intelligent laser particle size analyzer (Better, China). Prior to the batch sorption and transport experiments, the sand was cleaned in tap water until the rinse water was free of suspended impurities, and then rinsed with deionized water until the effluent turbidity was nearly zero. Finally, the sand was air dried and sterilized by ultraviolet. Subsequently, the cleaned sand was pre-eluted by week acid to maintain a pH of 6 before all experiments in our work, which will be helpful to avoid Cu precipitation. The treated sand mainly contained SiO2 (86.96%), (Na + K)2O (6.36%) and some other metallic oxides (e.g. Al2O3, trace amounts of CaO/MgO). The organic matter content was 0.036% and the cation exchange capacity was just 1.404 cmol/kg. Cu was not detected both in soluble salts analysis and scanning electron microscopyenergy dispersive X-ray (SEM–EDX) analysis (SSX-550, Shimadzu Corp., Tokyo), but it was 0.053 mg/kg after strong acid digestion. The background content of Cu was nearly 2–3 orders of magnitude lower than that of deposited Cu in this work. 2.2. Graphene oxide The graphene oxide (purity N 99%) was purchased from the Institute of Coal Chemistry, Chinese academy of Sciences. It was synthesized by a pressurized oxidation method described by Bao et al. (2012). Based on the information provided by the manufacture, the GO particles were comprised of a single layer (~99%); their diameter range was 781.1 ± 502.2 nm; and the thickness was 0.6 ± 1.0 nm. A stock suspension of GO was prepared by adding 1000 mg GO to 1 L deionized (DI) water in a 1000-mL volumetric flask, Subsequently, the suspensions were sonicated with ultra-sonication for 3 h to ensure completely dispersion. Prior to each experiment, the stock suspension was diluted to the desired GO concentration in DI water and sodium chloride (NaCl) was added to achieve the desired IS. Although the average concentration of GO in natural environment is at μg L−1 levels, high GO concentrations may be found in accidental spills from, e.g., a GO production facility. In order to obtain good results with excellent experimental accuracy and examine the case of GO spill, we examined influent GO concentrations of 40, 80, 120 mg/L. We examine the impact of the ionic strengths of 1, 10, 100 and 1000 mM, which represents a range of fresh water to a brackish water, e.g. groundwater in arid region of Northern China (Fan et al., 2013). Assuming that we can validate our model of facilitated transport, we can use it to explore other contamination scenarios. The zeta potential of GO and sand in various suspensions were measured using ZetaSizer Nano ZS90 (Malvern Instruments, UK), and the average hydrodynamic radius of GO was determined by dynamic light scattering (DLS). 2.3. Sorption of Cu on GO and sand Sorption experiments were performed by mixing sand or GO with electrolyte solution containing Cu(NO3)2 solution in 50-mL Teflon centrifuge tubes at room temperature (20 ± 1 °C). Cu–GO binding was examined using different concentrations of GO (20, 40, 60, 80, 100, 120, 150, 180 and 200 mg/L) mixed with 6.4 mg/L Cu at IS = 1 mM. For the sorption of Cu on GO and sand, 25-mL solutions (IS = 1 mM) with various concentrations of Cu (0–32 mg/L) containing 120 mg/L GO or 5 g sand were rotated end-over-end for 24 h. This equilibration time was shown to be enough to achieve equilibrium in preliminary experiments. For both experiments, the initial pH of electrolyte solution was adjusted to 6.0 ± 0.2 with 0.01 M NaOH or 0.01 M HNO3 to avoid the hydrolysis and precipitation of Cu. The Cu(II) loss due to adsorption on tube wall was considered by using the identical conditions but without GO as control. Particles were separated from the water samples using centrifugation at 12,000 rounds per minute for 30 min and then the supernatant was filtered through disposable 0.22-μm membrane filters.

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The concentrations of Cu in the filtrate were analyzed by atomic absorption spectroscopy (AAS, AA-6300). The adsorption percentage (adsorbed % = (C0 − Ce) / C0 × 100%) and sorption isotherms were fit to the Langmuir equations S = KSmCe / (1 + KCe), where S is the amount of Cu adsorbed on GO or sand (mg/g), Ce is the equilibrium concentration of Cu in aqueous solution (mg/L), Sm is the maximum adsorption capacity (mg/g), and K represents the sorption coefficient.

2.4. Column experiments Pretreated sand was wet-packed into an acrylic organic glass column (15 cm length × 3 cm inner diameter) with 0.2 mm stainless mesh screens on both ends. Each column contained approximately 150 g of sand and had an average bed porosity of 0.45 ± 0.01. Solutions were pumped upward through the column at a constant Darcy's velocity of 7.07 × 10−5 m/s which is a representative value for groundwater flow. There are two types of column experiments in our work: the breakthrough column experiments and the sequential elution experiments. The experimental schemes were described as follow. The breakthrough column experiments contain two parts: separated transport experiments and cotransport experiments of GO and Cu in columns. The former were conducted with Cu and GO alone to reveal the transport ability of each material themselves. The later fed preequilibrated Cu–GO mixture into the columns to study the GOfacilitated Cu transport process, which was the focus of this research. All breakthrough column experiments were conducted in 3 steps. Step 1: The packed column was first flushed with more than 10 pore volumes (PV) of the desired electrolyte solution to equilibrate the chemical conditions and to establish steady state saturate flow. Step 2: Then the input solution containing either Cu, GO or a continuously stirred of pre-equilibrated Cu–GO mixture was injected at a constant flow rate for about 3 PVs. Step 3: Then a GO (or Cu)-free background solution was fed to the column until that almost no GO (or Cu) was detected in the effluent. The separated transport experiments were performed in 6 columns according to the 3-step method. First, solution containing only 6.4 mg/L Cu with IS of 1 mM and 1000 mM were fed to two columns in aforementioned order. Second, solutions contain only 120 mg/L GO but with 4 different ISs (1, 10, 100, 1000 mM) were fed to the other 4 columns. The cotransport experiments were conducted using the following steps. First, a solution containing 6.4 mg/L Cu, GO and 1 mM IS were fed to the column. This was repeated for 3 GO concentrations, 40, 80, and 120 mg/L. Next a solution containing 6.4 mg/L Cu, 120 mg/L GO and NaCl was fed to the column. This was repeated for 4 different ISs (1, 10, 100 ,1000 mM). The main seven breakthrough experiments of Cu in the presence of GO are described in Table 1. The pH value of the solution was adjusted to 6.0 ± 0.1 using HNO3 or NaOH (analytical grade). Prior to each experiment, three samples (5 mL for each) were taken from the input mixture to determine the total Cu, the dissolved Cu and the GO concentrations. A conservative tracer (Br−) was fed to the column to determine the hydraulic dispersion coefficient (D) and Peclet number (Pe). In these aforementioned cotransport experiments, the Cu–GO mixture was stirred slowly and equilibrated for 24 h at room temperature before injection. After this sufficient mixing, Cu is easily complexed to GO and the breakthrough is fully controlled by transport of the Cu–GO complexes. However, this fully pre-equilibrated mode only tells part of story in field. In addition to this, the Cu and GO may be introduced into soil matrix sequentially but not simultaneously (as Cu–GO complexes) in some cases. Will the GO-facilitated Cu transport work then? To test this, an additional sequential elution experiment was therefore conducted to check if the GO alter the Cu mobilization without preequilibration. A separated Cu (C0 = 6.4 mg/L) transport experiment was performed, followed by 3PVs GO (C0 = 120 mg/L) elution at a

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Table 1 Conditions and parameters of the main cotransport column experiments. Expt no.

IS (mM)

GO (mg/L)

Cu (mg/L)

φ

Pore water velocity (cm/min)

D (cm2/min)

Pea

1 2 3 4 5 6 7

1 1 1 1 10 100 1000

0 40 80 120 120 120 120

6.4 6.4 6.4 6.4 6.4 6.4 6.4

0.452 0.453 0.457 0.453 0.453 0.455 0.448

0.939 0.937 0.929 0.937 0.937 0.934 0.947

0.183 0.183 0.155 0.145 0.158 0.172 0.163

77.0 76.8 89.9 97.0 89.0 81.4 87.4

a Peclet numbers were in the order of 70 or higher, indicating a convectively dominated flow regime.

constant IS = 1 mM, and then a free background solution was fed to the column until that almost no GO was detected in the effluent. In all column experiments, the column effluent was collected in 15 mL glass tubes at regular time intervals using a fraction collector (Huxi CBS-A 100, China). The collected sample was divided into three aliquots for measuring the concentrations of GO, the dissolved and the total Cu concentrations. GO concentration in the influent and effluent were measured using the UV absorbance at 230 nm (Shimadzu UV2450, Japan). A calibration curve was constructed in the presence of 0.1 mM Cu(NO3)2, GO concentration versus spectrometer response was still linear with a coefficient of determination of R2 = 0.9994 between a GO concentration range of 0–140 mg/L. 5 mL of the effluent was used to measure the dissolved Cu as mentioned in sorption experiments, while another 5 mL aliquot without being centrifuged and filtered was used to determine the total Cu concentration according to the acid digestion method of Wang et al. (2011). The spatial distributions of retained GO in the column was determined right after the co-transport test. The sands were removed in 2 cm increments carefully and divided into two aliquots for testing the retained GO and Cu. The release of both two from sand surface was driven by adding DI water and acid solution (0.01 M HNO3) correspondingly into 50 mL vials. These vials were gently shaken for 3 h to obtain a homogeneous concentration of GO and Cu in the supernatant (the testing methods were mentioned above). The amount of the sand in each vial was oven dried at 60 °C overnight to obtain the dry weight of the sand. Specially, some of the sand grains that were excavated from the bottom of the packed column at experiment 7 were characterized using a SEM–EDX analysis (SSX-550, Shimadzu Corp., Tokyo) to examine morphology of adsorbed GO and Cu in the sand columns. 2.5. Mathematical modeling Classical DLVO theory was employed for the evaluation of GO and sand grain interactions, which could mainly reflect the impact of GO– Cu complex (charge neutralization) and increase of IS (compression of diffuse double layers). The DLVO forces consider that the total interaction energy is the sum of van der Waals (VDW) attraction and electric double layer (EDL) repulsion. In this study, the Hamaker approximate expression for a sphere-plane case (Gregory, 1981) was used for calculating the retarded VDW attractive interaction; with the assumption of constant potential at the surface, the EDL interaction was calculated by the method derived by Hogg et al. (1966). The transport process of GO or Cu–GO complexes within the saturated sand column was described by an advection–dispersion–retention (ADR) model considering the combined process of dynamics blocking and depth-dependent straining. The governing equations can be written as:

2

∂ðnC Þ ∂S ∂ C ∂C þ ρ ¼ nD 2 −q ∂t ∂t ∂x ∂x

ð1Þ

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ρ

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∂S ¼ nkatt ψC−k det ρS ∂t

ψ¼

  −β S dc þ x 1− S max dc

ð2Þ

ð3Þ

where n is the porosity; D is the dispersion coefficient; C is the concentration in effluent water; ρ is the bulk density of the porous media; S is the solid phase concentration sorbed on quartz sand; q is the flow rate; katt and kdet are the first-order attachment and detachment coefficient, respectively; ψ is a dimensionless function to account for the combined process of dynamics blocking and depth-dependent straining (Bradford and Bettahar, 2006; Shang et al., 2013); Smax is the maximum solid phase particle concentration; dc is the average grain diameter; β is an empirical variable that controls the shape of the retention profile, Bradford et al. (2003) found an optimal value of β = 0.432, and we employed this value in our work. The ADR model was fitted to breakthrough curves to obtain the parameters D, katt, kdet and Smax using Levenberg–Marquardt least-squares algorithm method (Lucia and Xu, 2012). The effluent mass recovery rate was also calculated by integrating the break through curves (BTCs), and the total deposition rates (Rd) in sand columns could be estimated from a mass balance. 3. Results 3.1. Batch experiments of Cu adsorption The results of the batch experiments are given in Fig. 1. Concentrations of Cu in aqueous solutions were the same for 3 h under two specified pH conditions (Fig. 1a), suggesting there is no hydrolysis and precipitation occurred and a pH of 6 was a good choice. Cu adsorbed onto GO and it increased with the GO concentration (CGO) until the maximum removal efficiency was reached (Fig. 1b). It seems that the Cu was bound to GO when CGO reached 110 mg/L that is why we selected GO concentrations of 40, 80 and 120 mg/L for the column experiments. Adsorption isotherms for Cu on sand and GO are shown in Fig. 1c, and the Langmuir model described the curves quite well. The Sm of GO and sand were calculated to be 132.4 mg/g and 0.107 mg/g respectively, indicating that the sorption capacity of Cu on GO was significantly higher than that on sand. The K values also showed that the affinity of Cu to sand was about 46.70% lower compared to GO.

on both the d and ζ. The d increased with IS, while the absolute ζ had the opposite trend (less negative value), as compression of the EDL gave rise to continuous decrease in the magnitude of surface zeta potential (Lanphere et al., 2013; Fan et al., 2015b), and then the less negative ζ was bound to induce weaker electrostatic repulsion forces between particles. These results were consistent with the works reported by Lucia and Xu (2012). The d and ζ of GO in the dispersions containing Cu was larger than that without Cu regardless of the IS. This demonstrated that Cu reduced the ζ and destabilized the GO particles. Note that the d and ζ of GO are independent of GO concentrations (Fig. 2c), implying that the effect of GO concentration on Cu transport can be examined. As would be expected, the sand and GO were negatively charged over the range of solution conditions investigated. Therefore, the Cu adsorption in batch experiments is really meaningful, and it is possible to form unfavorable attachment condition such that GO can facilitate Cu transport in the sand columns. 3.3. Interaction energy profiles between GO particles and sand DLVO interaction energy (Φ) profiles for different electrolyte solutions are plotted as a function of separation distance in Fig. 3. The calculated energy barriers were higher than 350 kBT (where kB is the Boltzmann constant and T is Kelvin temperature) while IS varied from 1 to 100 mM. It is almost impossible for GO to overcome these energy barriers to attach to the sand surfaces in a primary minimum (Li et al., 2011; Shang et al., 2013). This energy barrier gradually decreased with increasing IS, and the profile suggests GO would flocculate in such a favorable condition at 1000 mM. It has been reported in the literatures that secondary energy minima may play a critical role on the particles aggregation and deposition (Mesticou et al., 2014; He et al., 2015). Here, the calculated results showed that the DLVO energy profiles at 1 mM had no any secondary minimum. However, the depths of secondary minimum wells at 10 and 100 mM were −0.0039 and −0.1388 kBT, respectively. The increase of IS deepened the secondary minimum wells distinctly, which indicated that it became more likely for GO to be retained under higher IS. By contrast, increasing GO concentrations had little effect on the DLVO energy profiles as shown in Fig. 3b. This is in accord with the surface property analysis, and the effect of GO (as a sorbent) concentration on Cu transport can be examined with a bit more certainty. 3.4. Separated transport of GO and Cu in columns

3.2. Properties of the GO and sand surfaces Results of the average hydrodynamic diameter (d) and zeta potential (ζ) measurements of GO are shown in Fig. 2. In agreement with the anticipated influence of EDL compression, the IS played a significant role

The observed and simulated BTCs at all experimental conditions are presented in Figs. 4–6, and the best-fit transport parameters in model were listed in Table 2. The ADR model described all the experimental breakthrough curves very well, R 2 N 0.90. In the

Fig. 1. Results of batch experiments on Cu adsorption onto GO and sand. (a) effect of pH on Cu concentration; (b) effect of GO concentration on Cu adsorption; (c) the Langmuir adsorption isotherms of Cu adsorption onto GO and sand.

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Fig. 2. Properties of Cu-bearing GO and sand: (a) effect of IS on zeta-potential of GO and sand in the presence or absence (o) of Cu; (b) effect of IS on the average hydrodynamic sizes of GO; (c) effect of GO concentration on zeta-potential and the average hydrodynamic sizes of GO.

figures, the normalized effluent GO or Cu concentration C/C 0 was plotted as a function of pore volumes. Above all, the tracer (Br−) breakthrough curves in columns were fairly reproducible, because the Br-1, Br-2, Br-3 and Br BTCs in Fig. 4a and 5a depicted 4 similar breakthrough processes of tracer (Br − ) in experiments 1–4. The best-fit value of D can be used to model the transport of GO and Cu reliably.

3.4.1. Effects of IS on the transport of Cu To determine the effects of IS and GO concentration on cotransport of GO–Cu, column experiments with Cu and GO alone were conducted. Fig. 4a shows that in the absence of GO, Cu in the aqueous phase was completely adsorbed by the sand in the column and no Cu breakthrough was observed at IS = 1 mM, and there were only a little breakthrough at IS = 1000 mM (the peak effluent concentration (C/C0)max = 0.028). Actually, all Cu in effluent mentioned in this paper were particulate and soluble Cu(II) was not detected under all measured conditions. This is consistent with the observations reported by Paradelo et al. (2012). This result was expected because the sand had a considerable sorption capacity of Cu due to their opposite charge, especially for the low Cu concentration that was used in this study. We did not repeat to depict

the Cu transport experiments under other IS (10 and 100 mM) in Fig. 4a, because they were quite similar.

3.4.2. Effects of IS on the transport of GO The GO transport through the column was greater for the lower range of IS tested (1 and 10 mM), and moderate retention occurred at 100 mM, while most of the GO was retained at 1000 mM. The peak effluent concentration (C/C0)max at 1 mM was 7.54 times as that at 1000 mM. This trend agrees with the DLVO calculations, suggesting that electrostatic interactions were the main mechanisms controlling GO transport and retention. The diffuse double layers were compressed at higher IS causing a reduction in the repulsive electrostatic doublelayer forces and increase in the particle deposition rate onto the sand (Lanphere et al., 2013). Accordingly, GO was less mobile at higher IS. It is also worth noting that the C/C0 in all BTCs quickly reached 0 after elution by 3 PVs background solution, suggesting that the remobilization of previously adsorbed GO could be inconsiderable at the same IS. That's why the fitted values of kdet in the model were quite small, and they were nearly 3–5 orders of magnitude lower than k att. And the slight differences of k det under these scenarios also could be ignored.

Fig. 3. DLVO energy profiles between the GO and sand particles. (a) CGO = 120 mg/L, CCu = 6.4 mg/L, IS varied from 1 to 1000 mM; (b) CCu = 6.4 mg/L, IS = 1 mM, CGO varied from 40 to 120 mg/L.

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Fig. 4. Observed and simulated breakthrough curves in separated transport experiments. (a) Br−, 6.4 mg/L Cu with varied IS (1, 1000 mM); (b) 120 mg/L GO with varied IS (1, 10, 100, 1000 mM).

3.5. Cotransport experiments of GO and Cu in columns 3.5.1. The GO breakthrough processes in cotransport experiments Results of Cu–GO cotransport studies for various ISs and GO concentrations are shown in Fig. 5 and Fig. 6. For Figs. 5a and c, 6. 4 mg/L of Cu and the various GO concentrations (40, 80 and 120 mg/L) were equilibrated and then fed to sand columns at IS of 1 mM. The GO BTCs for three GO concentrations with Cu (6.4 mg/L) are very similar and nearly 100% breakthrough (C/C0) was quickly reached within 2 PVs (Fig. 5a). This is also consistent with a mass balance results (Table 2). We also found that BTC of 120 mg/L GO with (Fig. 5a) and without 6.4 mg/L Cu (Fig. 4b) essentially overlap. Accordingly, the bound Cu did not significantly affect the GO transport in the column. Thus, GO exhibited high

mobility and transport as the conservative tracer Br− regardless of the GO concentrations (40, 80 and 120 mg/L) in such a lower IS. In addition, we performed the comparisons of GO transport behaviors at different ISs both in the absence and presence of Cu (Fig. 4b and Fig. 5b). It is clear that the presence of Cu could only lead to significantly decrease in GO breakthrough concentrations at higher ISs (100 and 1000 mM). The total deposition rates Rd. were 1.01%, 4.94%, 23.98% and 85.02% in the absence of Cu at 1, 10, 100, 1000 mM, respectively, and they increased to 4.85%, 5.27%, 28.10% and 96.96% in the presence of Cu correspondingly. These results were consistent with our earlier work (Fan et al., 2015a). Retention of GO in sand column was strongly dependent on IS in presence of multivalent cations, and no remarkable influence of

Fig. 5. Breakthrough curves (a and b) and retention profiles (c and d) of GO in cotransport experiments. (a) and (c) depict experiments 2–4; (b) and (d) depict experiments 4–7.

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Fig. 6. Breakthrough curves (a and b) and retention profiles (c and d) of Cu in cotransport experiments. (a) and (c) depict experiments 2–4; (b) and (d) depict experiments 4–7.

multivalent cations on GO transport was found at lower IS. This is consistent with our earlier work (Fan et al., 2015a). It was supposed that compression of diffuse double layers mainly controlled GO deposition under lower IS, while charge neutrality and metal bridging played a significant role at the higher IS here. 3.5.2. The Cu breakthrough processes in cotransport experiments As shown in Fig. 6a, Cu was removed completely by sand column during transport without GO involved (experiment 1). However, in the presence of GO, Cu was detected in the effluent, with (C/C0)max of 0.13, 0.32 and 0.57 at 40, 80 and 120 mg/L (experiments 2–4), respectively. Evidently, GO-facilitated transport was solely responsible for the observed Cu movement, and the amount of Cu breakthrough tended to increase with increasing GO concentration. This trend qualitatively agreed with the results of adsorption experiments shown in Fig. 1b. It was also observed that this increase of Cu mobility facilitated by GO was highly dependent on the IS. Fig. 6b shows that the (C/C 0) max of Cu breakthrough was reduced by more than

45% when IS increased from 1 to 100 mM (experiments 4–6). At 1000 mM, no Cu appeared in the effluent which indicated that GO flocculated and removed by the sand column. This trend of decreasing Cu breakthrough with increasing IS follows the GO BTCs in Fig. 5b, and in turn, it verifies that the mobile GO particles contribute to the migration of Cu. In addition to deposition in second minimum and straining, surface roughness has been demonstrated to play an important role on particles retention under unfavorable condition (Treumann et al., 2014). Fig. 7a shows a SEM micrograph of clean sand grains. This grain possesses obvious surface roughness of different sizes, implicating that hydrodynamically favorable regions for GO retention may be formed possibly by these rough patches. Actually, vivid aggregates were observed after co-transport experiments (Fig. 7b), which filled the rough area to form a smoother surface. Moreover, by contrast the EDX spectrum in Fig. 7a and b, the Cu peak, increased C and O confirm that the large floccules and aggregates were composed of GO-bearing Cu. These provide another view of the GO–Cu binding.

Table 2 Summary of experimental conditions and model results. Expt no.

1 2 3 4 5 6 7

katt [min−1]

Rd GO

Cu

2.67% 3.31% 4.85% 5.27% 28.10% 96.96%

100.00% 87.82% 80.08% 69.46% 75.31% 80.47% 100.00%

kdet [min−1]

GO

Cu

0.305 0.413 0.355 3.739 6.675 12.731

N100.0 6.380 5.590 2.560 7.620 9.400 N100.0

R2

Smax [mg/g]

GO

Cu

0.0002 0.0002 0.0002 0.0002 0.0002 0.0002

0.0000 0.0002 0.0002 0.0002 0.0002 0.0002 0.0000

GO

Cu

GO

Cu

0.057 0.008 0.102 0.126 1.058 13.308

161.080 12.024 6.422 2.711 3.295 3.465 264.910

0.99 0.96 0.99 0.97 0.98 0.96

0.98 0.91 0.99 0.98 0.95 0.98 0.98

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Fig. 7. SEM micrographs and corresponding EDX spectrum of (a) clean sand, (b) sand excavated from the inlet of the packed column after cotransport experiment (no. 7).

3.6. Additional evidence for mechanisms driving the facilitated transport of Cu by GO The above information clearly describes that factors that influence GO retention (such as IS) and the binding capacity of Cu–GO (GO concentration) will have a large impact on the facilitated transport of Cu in pre-equilibrated mode. Furtherly, the sequential elution experiments illustrated the scenario that the Cu and GO were introduced into soil matrix sequentially but not simultaneously (Fig. 8). As expected, no Cu breakthrough was observed in the first triple phases and GO showed fairly high mobility (similar to Br−) in the next two phases as depicted in Figs. 5 and 6. Note that moderate Cu was detected in the outflow during the GO elution phases, with the (C/C0)max of 0.3. Considering all detected Cu were particulate, obviously, the Cu release was attributed to the later introduced GO which can snatch the pre-adsorbed Cu from the sand surface. Now, we can say that the transport of Cu through the sand columns is controlled by GO–Cu binding even if they contact with each other in a short hydraulic retention time (about 35 min in this experiment). This provides further evidence for mechanisms

driving the facilitated transport of Cu by GO in further. Compared with traditional cotransport experiment in pre-equilibrated mode, the sequential elution experiment in our work presents a new perspective on interactions between GO and Cu. The GO-facilitated behavior could works in a short contact time and be competitive. 4. Discussions 4.1. Strong affinity of GO to Cu The high value of Sm and K fitted by adsorption experiments data indicate that the sorption capacity of Cu on GO was significantly higher than that on sand (Fig. 2). This efficient uptake of Cu on GO is consistent with the results reported by Li et al. (2014). GO may be a promising candidate for the efficient binding of Cu from aqueous solutions. Therefore, it is not hard to imagine that GO particles have a significant potential for facilitated transport. Especially when the sorption capacity of Cu onto GO was almost 3 orders of magnitude higher than that of the sand used in this work. As shown in Section 3.6, such competitive

Fig. 8. Breakthrough curves (a) and retention profiles (b) of Cu and GO in sequential elution experiment.

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adsorption behavior would ultimately influence the pre-deposited Cu in sand columns. In the sequential elution experiment, the GOfacilitated behavior occurred in a quite short contact time, and also showed considerable facilitated effect as traditional cotransport experiment in pre-equilibrated mode. This is also a powerful evidence of the strong affinity of GO to Cu. The large specific surface area, electronegativity of GO, and active adsorption sites on GO are the mainly reasons for this. The specific surface area of GO is 1000–1217 m2/g according to the manufacturer (Section 2.2), and even up to 2620 m2/g that mentioned in another work (Wang et al., 2012). The electrokinetic measurements showed that the GO was negatively charged over the range of solution conditions investigated (Fig. 2a). This is consistent with previous studies (Lanphere et al., 2013; Fan et al., 2015a) that report the isoelectric point of GO to be around pH ≤ 3. Consequently, the charge neutralization was triggered when GO was involved in Cu2+ solution (Wu et al., 2013). Further, the large amounts of oxygen-containing functional groups (carboxyl/hydroxyl group) at the basal planes and the edge sites of GO (Chowdhury et al., 2013) ranked GO among the most effective absorbents for Cu2+ removal. Previous research suggested that coordination between Cu2+ and oxygen atoms on GO is regarded as the primary driving force (Yang et al., 2010). This process could also cause GO sheets to be folded and to form large aggregates, which was indicated by the increase of its hydrodynamic diameter (Fig. 2b) and SEM analysis (Fig. 7) in our work. 4.2. Mechanisms influence GO-facilitated Cu transport A striking overall observation in cotransport experiments was that the presence of GO significantly enhanced the transport of Cu at relative lower IS (1 to 100 mM). As shown in Section 3.2, the sand and GO were negatively charged over the range of solution conditions investigated, and it was possible to form chemically unfavorable attachment condition (electrostatic repulsion) (Li et al., 2011; Shang et al., 2013). DLVO curves in Fig. 3 further suggested that the calculated energy barriers were higher than 350 kBT while IS varied from 1 to 100 mM. These insurmountable energy barriers make the GO mobility possible, which presents the necessary prerequisite of the subsequent facilitated transport, just like the enhanced transport of phenanthrene by GO reported by Qi et al., 2014a, b. As expected, the facilitated Cu transport was controlled by the binding capacity of total GO. The amount of Cu breakthrough increases with increasing GO concentration (Fig. 6a). This trend qualitatively agreed with the results of adsorption experiments shown in Fig. 1b. The above information clearly indicates that factors that influence the retention of GO will have a large impact on the facilitated transport of Cu. Ionic strength is considered as one of the important factors influencing the electrostatic interactions between sorbate and sorbent. Increasing salt concentration in aqueous solutions shields electronic charges of both GO and the sand surface, and then decreases the electrostatic repulsion between them (Knappett et al., 2008). This theory explains well the effects of ionic strength on GO retention and cotransport behaviors observed in this study. When the IS increased to 1000 mM, the energy barrier disappeared (Fig. 3) and this induced the deposition of GO into the primary energy minimum. Therefore, the GO-facilitated transport of Cu was sharply reduced (Fig. 6). The theoretical explanation provided here is also supported by the results from other researchers (Wang et al., 2011; Rahman et al., 2013), and what's different is that we capture a new experience of non-facilitated transport at higher IS. The strong dependence of GO and GO-binding Cu sorption on solution IS observed in this study suggests that attachment mechanism was responsible for their retention. Admittedly, the sharply increase of d at 1000 mM also suggested that straining may occur. Straining of particles at grain–grain contacts has been shown to be important when the diameter ratio of particle to grain is larger than 0.005

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(Bradford et al., 2003; Johnson et al., 2007). In this study, the diameter ratio of GO to sand was more than an order of magnitude higher than this threshold at 1000 mM. Therefore, straining mechanism was considerable under that situation. Relatively flat retention profiles were observed except for that at 1000 mM in Figs. 5 and 6, and it showed that much higher concentrations of GO deposited near the inlet, which verified this view. Admittedly, there are more Na+ in pore water that may contact with sand grains when the IS increases, and the Na+ subsequently would competes the adsorption sites on grains (Bradford and Kim, 2010). Theoretically this mechanism could lead to increase of Cu in effluent due to less adsorption, however, there were not obvious distinctions between the BTCs of Cu under different ISs in Section 3.4.1 (Fig. 4a). This is more likely due to the high adsorption capacity of the sand column that covers the impact of cation. In addition, the Cu breakthrough results at 1000 mM in cotransport experiments contradicted to this theoretical expectation (still no Cu breakthrough the column). It seems that the impact of coexisting competitive cations is not prominent mechanism in our work. 5. Conclusions We have reported the underlying mechanisms that are responsible for facilitated transport of Cu by GO in saturated porous media. The complexation of Cu with GO is responsible for the facilitated transport of Cu. We found facilitated transport to be a strong function of the GO concentration and IS. The experiment results showed that GO with high mobility could serve as an effective carrier for Cu in certain IS ranges (1–100 mM), and facilitated transport of Cu by GO increased with the concentration of co-present GO. However, the transport of GO in saturated porous media was sharply reduced at higher IS condition (1000 mM) and was due to electrical double layer compression from the NaCl that was used to increase the IS and adsorption of divalent Cu(II). This factors increased the flocculation rate caused the GO–Cu complexes to be stained out by sand and to attach to the sand. Consequently, the Cu–GO complexes breakthrough was hindered. All conclusions were supported by adsorption tests, DLVO calculations, BTCs and retention profiles monitoring, and sequential elution experiment. An ADR model provided a good description of all collected data, indicating that it can be employed to simulate the co-transport behavior of Cu and GO in soil and groundwater system. Acknowledgments This work was financially supported by the National Natural Science Foundation of China (NSFC NO. 41302196 and 51238001) and Long Term Program in “1000 Talent Plan for High-Level Foreign Experts” (NO. WQ20142200209). It was also supported by the Fundamental Research Funds for the Central Universities (NO. 14QNJJ026). The authors also would like to acknowledge the support by the Brook Byers Institute for Sustainable Systems, Hightower Chair, and the Georgia Research Alliance at the Georgia Institute of Technology. References Akbour, R.A., Douch, J., Hamdani, M., Schmitz, P., 2002. Transport of kaolinite colloids through quartz sand influence of humic acid, Ca2+, and trace metals. J. Colloid Interface Sci. 253, 1–8. Bao, C., Song, L., Xing, W., Yuan, B., Wilkie, C.A., Huang, J., et al., 2012. Preparation of graphene by pressurized oxidation and multiplex reduction and its polymer nanocomposites by masterbatch-based melt blending. J. Mater. Chem. 22, 6088–6096. Bradford, S.A., Bettahar, M., 2006. Concentration dependent transport of colloids in saturated porous media. J. Contam. Hydrol. 82, 99–117. Bradford, S.A., Kim, H.J., 2010. Implications of cation exchange on clay release and colloidfacilitated transport in porous media. J. Environ. Qual. 39 (6), 2040–2046. Bradford, S.A., Simunk, J., Bettahar, M., van Genuchten, M.T., Yates, S.R., 2003. Modeling colloid attachment, straining, and exclusion in saturated porous media. Environ. Sci. Technol. 37, 2242–2250.

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