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IInterface-induced Affinity Sieving in Nanoporous Graphenes for Liquid-Phase Mixtures Yanan Hou, Zhijun Xu, and Xiaoning Yang J. Phys. Chem. C, Just Accepted Manuscript • DOI: 10.1021/acs.jpcc.5b10287 • Publication Date (Web): 08 Feb 2016 Downloaded from http://pubs.acs.org on February 13, 2016

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The Journal of Physical Chemistry

Interface-induced Affinity Sieving in Nanoporous Graphenes for Liquid-Phase Mixtures †

‡

Yanan Hou, Zhijun Xu, Xiaoning Yang

†

*†

State Key Laboratory of Material-Orientated Chemical Engineering, College of Chemistry and Chemical Engineering, Nanjing Tech University, Nanjing 210009, China ‡

Department of Polymer Science, University of Akron, Akron, OH 44325, USA

* Corresponding Author Email: [email protected].

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ABSTRACT: In this work, we simulate the hydraulic permeation of liquid ethanol-water mixtures through a series of nanoporous graphene membranes. Ethanol was found to have larger permeability as compared with water in the mixture. For the first time, we present direct computational evidence that nanoporous graphenes exhibit promising potential as ethanol-permselective sieving membranes for the separation of ethanol-water mixtures, with ethanol permeability several orders of magnitude higher than current pervaporation membranes. The underlying sieving mechanism is not the process of pore-size sieving, but has been distinctively revealed as the sieving mode based on interfacial affinity. The enhanced hydrophobic surface adsorption and preferential pore trapping function in nanoporous graphene structure lead to the selective penetration of ethanol. Our results provide new insight into the molecular penetration across atomically thick nanoporous graphenes and it further represents a proof of concept design of highly efficient nanoporous graphenes in membrane separation and nanofluidic devices for liquid-phase mixtures.

KEYWORDS: graphene, membrane separation, surface adsorption, ethanol-water mixture, molecular simulation

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1. Introduction Nanoporous single-layer graphene can be considered as a new class of highly permeable molecular sieve membranes in gas separation1-6 and water desalination7-10 thanks to its ultrathin thickness.11-13 Experimental techniques14-20 have been developed to drill or create nanoscale holes on two-dimensional (2-D) graphene lattice. In particular, controlled pore sizes with the diameter as small as 4 Å have been experimentally realized on single-layer graphene sheet.3,21-22 Recently, stable pores with precise dimensions in the range of 5-10Å in single-layer graphene were fabricated using oxygen plasma etching,7 and the resulting pristine nanoporous graphene exhibited tremendously high water transport and almost complete salt rejection. In the graphene-based membrane separation process, pore-size sieving is usually the responsible mechanism,1,3,21,23 which allows the penetration of molecules smaller than graphene pores, while blocks the passage of molecules larger than the pores. For example, quantum mechanics computation showed that porous graphene with the pore size around 3.3Å exhibits high selectivity for H2 over other gas molecules.24 Molecular dynamics (MD) simulation8,10,25 also demonstrated that the desalination mechanism of nanoporous graphene is the pore-size exclusion of hydrated ions. Nevertheless, unlike the mechanism with pore-size sieving, it was also reported that larger gas molecules could permeate more effectively through graphene pores, as compared with smaller molecules.26-28 This special sieving behavior can be generally speculated as the enhanced surface adsorption of larger molecules on graphene plane, namely, the preferential surface adsorption could improve the pore penetration of molecules through single-layer graphene. Extensive studies29-33 have shown that hydrophobic carbon surfaces exhibit strong affinity with alcohols. Molecular simulation34 has displayed that the enhanced surface interaction could



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induce dewetting near carbon surfaces in the alcohol-water mixtures. Recent experiments35-36 found that the slit pore, composed by mica surface and graphene layers, allows for micro-phase separation of ethanol-water mixtures. Thus, attributed to its hydrophobic (or organophilic) nature, graphene surface is expected to induce preferential ethanol adsorption, which could increase the ethanol transport across nanoporous graphenes in ethanol-water mixtures. With the above in mind, we perform MD simulations to investigate the transport behavior of ethanol-water mixtures through nanoporous graphene membranes with various pore sizes and shapes. It was computationally demonstrated that in the mixture larger ethanol molecules display higher permeability than water molecules, suggesting selective permeation of ethanol across the graphene membranes. This simulation result shows that nanoporous graphenes could enrich ethanol from its aqueous solutions and can serve as a new-typed ethanol-permselective membrane. The interfacial structures and thermodynamics interaction have been used to explore the novel sieving mechanism in the nanoporous graphenes. Our result presents a new separation mode for possibly using the atomically thick graphene structures in the separation of liquidphase mixtures. 2. Simulation method In this work, seven nanoporous graphene structures with different pore sizes were designed by selectively drilling carbon atoms from the center of the graphene sheets. The nanoscale pores were denoted as P12, P14, P16, etc., as illustrated in Figure 1a, based on the number of benzene ring units drilled out. We characterized the effective pore size as the average value of the maximum and minimum distances within the pore based on its electron density. The carbon atoms in graphene were modeled as neutral Lennard-Jones (L-J) spheres.37 This C-C parameter has been applied in the simulation of methanol-water mixtures on carbon nanotubes.30,38 Ethanol


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and water molecules were represented by the OPLS-AA model39 and the SPC/E model,40 respectively. The non-bonded interactions were described by the L-J and electrostatic interactions. The L-J interactions between different atoms were calculated using the Geometric mixing rule, which has been used in the simulation of ethanol-water mixtures.31,41 The cutoff distance for the L-J interactions was set to be 12 Å. Long-range electrostatic interactions were treated using the particle-mesh Ewald (PME) method.42 All the MD simulations were performed in the NVT ensemble with the temperature of 298.15K using the Lammps code.43 In the simulation cell, the nanoporous graphene membrane was treated as rigid and fixed, which could be viewed as the planar geometry. Although rigid structure of graphene sheets might not represent the actual configuration, the flexible structure of graphene was reported to have weak effect on the molecular permeation in previous studies.44-45 In the non-equilibrium simulations, the nanoporous graphene was placed into the middle of simulation cell (z=0 Å) with the feed side consisting of ethanol-water mixtures and the permeate side initially in the vacuum state, as shown in Figure 1b. The hydrostatic pressure, ranging from 50 to 400 MPa, was created along the negative z direction. The high pressure used here can reduce the thermal noise and enhance signal-to-noise ratio,46-47 aiming to obtain reasonable statistics within nanosecond timescale.8,48-49 It should be emphasized that although high pressure was applied in the non-equilibrium MD simulation, this does not mean actual operation of membrane separation must run in high-pressure conditions. Recent study has shown that nanoporous graphene is indeed able to withstand the higher hydraulic pressure in membrane separation operation. In addition, the inclusion of highly porous substrates in the membrane module would improve the mechanical strength of nanoporous graphene membranes.8,50 The detailed simulation processes including non-equilibrium and equilibrium simulations, the



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potential of mean force (PMF) calculation, and the electron density computations for graphene, ethanol, and water were provided in the Supporting Information. 3. Results and discussion We simulated the pervaporation-like hydraulic permeation of an equimolar mixture of ethanol and water through a series of graphene nanopores (P12-P24 in Figure 1a) from solution phase to vacuum phase. Figure 1b presents the schematic representation of the simulation system. The molecular penetrating numbers are shown as a function of simulation time in Figure 2a for the P14 membrane. The approximate linear relation demonstrates stable pore transport under the applied pressures. It is surprisingly observed that ethanol, with larger kinetics diameter,51 has obviously higher permeation ability than water. In particular, for the smaller pores, few events of water penetration were observed during the simulation. The discrepancy in the ethanol/water penetration through the graphene nanopore is not caused by the mechanism of pore-size sieving. For other graphene pores, similar penetration behavior is obtained for the mixture, as shown in Figure S1 of Supporting Information. The different penetration abilities between the two species suggest that the graphene nanopores are able to selectively separate ethanol from the mixture. The corresponding snapshots in Figure 2b illustrate the permeation process across the P14 membrane. At the beginning of simulation, the initial ethanol-water mixture is on the feed side. As the simulation is in progress, more ethanol molecules move to the permeate side by passing through the graphene nanopore, while relatively few water molecules are found to penetrate across the membrane. The snapshots also show that ethanol molecules prefer to aggregate near the graphene surface, which is consistent with the interfacial behavior of ethanol-water mixtures on carbon surfaces.32,34 It can be intuitively deduced that this favorable adsorption of ethanol


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might promote its penetration through the graphene pores.

Figure 1. Pore structures and simulation system. (a) Pore structures and pore electron density isosurfaces (isovalue of 0.03e/Å3) of the nanoporous graphenes considered in this simulation. The pore size displayed here was characterized as the average value of the maximum and minimum separations within the pores. (b) The non-equilibrium permeation simulation system, the inset is the enlarged snapshot for ethanol molecules passing through the nanopore. Color code: nanoporous graphene, cyan; C(ethanol), green; O(ethanol), blue; O(water), red; H, white.

We also conducted two additional pervaporation simulations with different initial configurations using the P14 membrane. As shown in Figure S2, the favored penetration of ethanol relative to water can be observed for the two simulations, even in the system with water molecules initially closer to the nanoporous graphene in the feeding side. This provides the straight confirmation that our non-equilibrium simulation result is rational. The supplementary simulations imply that the unique surface structure and affinity in the nanoporous graphene are indeed able to ensure the selective pore penetration of ethanol regardless of the starting mixing state in the feed side.



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Figure 2. Penetration through the P14 membrane. (a) The penetrated numbers of ethanol and water molecules passing through the nanopore P14 under different pressures for the (50%) mixture in the initial feed. Inset shows the corresponding nanopore P14. (b) Typical snapshots of the mixture penetration process across the graphene nanopore (P14). For clarity, only part of ethanol-water mixture is displayed in the feed side. (c) The penetrated numbers of ethanol and water molecules passing through the nanopore P14 at 300 MPa with the mixture (50mol%) located in both sides of graphene membrane. Inset shows the corresponding initial configuration. (d) The penetrated numbers of pure ethanol and pure water passing through the nanopore P14 at a pressure of 300 MPa with pure species located in both sides of graphene membrane.

Figure 2c shows the transport of ethanol/water mixture across the nanoporous graphene in the situation with the equimolar ethanol-water mixture in both sides of the P14 membrane. The striking selective permeation of ethanol passing through the graphene pore is also observed in the simulations. In addition, the supplementary reverse osmosis simulation with pure ethanol in the


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downstream side provides additional support of the above result (see Figure S3). For comparison, we also show the permeation of pure species in Figure 2d, where, on the contrast, pure water was shown to permeate faster than pure ethanol. The comparison between the mixture and pure species further highlights the highly competitive and selective penetration of ethanol relative to water in the mixture. All these simulations computationally demonstrate that nanoporous graphenes have promising potential as the sieving membranes for the ethanol separation from ethanol-water mixtures. From the penetration curves (in Figure S1), we can extract the ethanol flux ( J e ) and the water flux ( J w ) by measuring the permeation events of ethanol and water per unit time. As shown in Figure 3a, the ethanol flux significantly surpasses the water flux, and both of them increase with pore size. The fluxes are roughly proportional to the applied pressure, in agreement with previous simulation results regarding water crossing single-layer graphene nanopores.44,52 This linear behavior suggests that the high pressure flux could be extrapolated to the lower pressure conditions. Furthermore, the average permeation flux per unit pressure as a function of pore area is shown in Figure S4. The total flux increases monotonously with pore area, while the flux of ethanol shows a decrease for the P22 membrane, suggesting a competitive penetration between the two species. For the P22 and P24 membranes, the water permeation fluxes have a significant jump owing to their large pore sizes. This result implies ethanol no longer has obvious superiority of permeation as the pore size enlarges. The membrane permeability (P) in the unit of kg/(m 2 ⋅ h ⋅ bar) was characterized for each nanoporous graphene in Figure 3b. Following the previous works,8,25 for all nanoporous graphenes, the relatively conservative surface porosity of 10% was used in the permeability computation. It is observed that, for these nanoporous graphenes, the ethanol permeability



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roughly remains a slow rise with the pore size. The lower permeability in the P12 membrane is ascribed to its pore-size restricting effect. To make a direct evaluation of the selective permeation performance, we calculated the permeability ratio (based on mol / m 2 ⋅ h ⋅ bar ) of ethanol to water for each graphene pore (Figure 3b). It can be clearly seen that the molar permeability ratio declines first, and then flattens out as the pore size further increases; demonstrating that the selectivity decreases with the pore size and the smaller pores P12 and P14 have higher separation performance. Here, we did not rigorously investigate the effect of surface porosity of graphene on the separation performance. However, from the result shown in Figure 3b, larger porosity might generally lead to an increase in the permeation flux, along with a decrease in the selectivity.

Figure 3. Separation performance of nanoporous graphenes. (a) The fluxes of ethanol (up) and water (down) through different graphene nanopores as a function of the applied pressure for the ethanol-water mixture (50%) in the initial feed. Error bars represent the standard average deviation. (b) Mass permeability of ethanol/water and molar permeability ratio for individual graphene nanopore.

In the nanoporous graphene membranes, the ethanol permeability is in the range of (0.9-


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2.0)×102 kg/(m 2 ⋅ h ⋅ bar) , which is several orders of magnitude higher than that in conventional pervaporation membranes, including polymeric, inorganic, and mixed-matrix membranes.53-54 If the separation factor can be calculated using the equation, ( Pe Pw ) ( x e x w ) , where xe x w is the molar ratio of ethanol (xe) to water (xw) in the initial feed, thus, the calculated permeability ratio (Figure 3b) represents the separation factor. It is found that the P12 and P14 membranes display higher separation factor. Figure S5 gives a direct comparison of the separation performance between graphene membranes examined here and the current available pervaporation membranes in the ethanol recovery from aqueous solution. The nanoporous graphene membranes possess remarkably high ethanol permeability and excellent separation factor. This high permeability is associated with the one-atom thickness of graphene membrane. To gain more understanding of the molecular permeation process, we analyzed the passing trajectories of molecules during the permeation course through the P14 pore. Figure S6a shows ethanol molecules usually stay for a long time within the surface layer (z~4Å) before passing through the membrane pore, similar to the so-called surface flow in gas molecule permeation.55 In Figure S6c, the time-dependent moving path of one representative ethanol molecule along the x-axis and z-axis shows, before the pore permeation, the ethanol molecule always adsorbs on graphene surface and then moves along the surface for quite a long time. This behavior agrees well with the observed adsorption layer of ethanol in the snapshot (Figure 2b) and suggests the adsorption of ethanol on the graphene surface might benefit the ethanol permeation. However, as seen in Figure S6b, water molecules display apparent fluctuations in the surface layer, implying that the adsorption layer might have certain impediment on water passage across the graphene pore. The relevant molecular orientation analysis (Figure S7) shows that, for smaller pores, ethanol molecules within the pore region ( z ≤ 1.7Å) usually adopt an alignment with the CH3-



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CH2 bond approximately perpendicular to the graphene plane. This specified molecular orientation could reduce the ethanol passage through the small P12 pore due to the entropic effect. The interfacial density profiles for the ethanol-water mixture near the graphene pores are presented in Figure 4a, which were calculated within an imaginary cylinder (see the insert of Figure 4a), with its axis perpendicular to the nanopore and its diameter equal to the pore diameter. For all the graphene pores, the interfacial density profiles are similar to each other, identically displaying a sharp adsorption layer of ethanol and a deficient water adsorption close to the graphene nanopores. This preferential adsorption of ethanol on graphene surface might help the ethanol passage through the graphene pores. In the situation of pure species, both ethanol and water show the similar adsorption behavior with sharp adsorption layer adjacent to the nanopores, as seen in Figure S8. Thus, this suggests the presence of ethanol molecules can cause exclusion of water molecules from the graphene surface. The unique interfacial behavior can be interpreted in terms of different surface affinities between the two species, in which the strong surface interaction facilitates ethanol to accumulate in the vicinity of graphene nanopores. The supplementary simulation (Figure S9) further verifies that ethanol molecules have stronger competitive adsorption ability and expel water molecules from the graphene surface. For the ethanol-water mixture near the nanoporous graphenes, as shown in Figure S10a, ethanol molecules stably and continuously exist in the pore and have absolute superiority in the pore occupation, while water molecules vary frequently over time with the occupation number being zero in most of the simulation time. In particular, the occupation number of ethanol inside all the nanopores in the mixture is similar to that in pure ethanol (Figure S10b), indicating that water molecules in the mixture has little influence on the pore occupation of ethanol. Figure 4b


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presents an intuitive comparison of the average molecular numbers of species inside each graphene nanopore. Different pores yield diverse molecular trapping abilities, which are qualitatively in accordance with the permeability behavior in Figure 3b. This suggests that mixture permeability is highly associated with the molecular capture ability in the graphene pores. Particularly, the low ethanol permeability in the P22 is mainly caused by the increased pore occupation of water molecules.

Figure 4. Interfacial structure and pore occupation. (a) The interfacial density profiles of ethanol (up) and water (down) in the mixture (50%) near the nanoporous graphenes, obtained within an imaginary cylinder (see the insert) with the axis perpendicular to the graphene surface and the diameter equal to the pore diameter. (b) The average pore occupation numbers of ethanol molecules (olive) and water molecules (red) inside the nanopores. Inset is the electron density isosurfaces of ethanol and the nanopores P12 and P14. Isovalue is at 0.03e/Å3. Error bars represent the standard average deviation.

The two-dimensional planar density maps of ethanol molecules within the nanopores can be illustrated in Figure S11, which reflect the molecular distribution patterns in the graphene nanopores. Only small density region is observed at the pore centers in the P12 and P14. The



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electron density isosurfaces in the insert of Figure 4b show the P12 and P14 pores can be purely filled with single ethanol molecule, leaving less additional space for water occupation and restricting the passage of water. This is consistent with the lower water permeability in the two pores. As the pore size increases, more crossing area is available for the molecular packing and passing, corresponding to a decrease of the molecular penetration selectivity. In order to explore the thermodynamics mechanism for molecules passing through the graphene nanopores, the potential of mean force (PMF) for a single ethanol or a single water molecule in the mixture (50mol%) along the pore axis was calculated. Figures 5a and 5b display the corresponding total PMF profiles across the nanopores P14 and P24, respectively. For the P14 pore, the appearance of PMF well for ethanol indicates that it is thermodynamically favorable for ethanol molecules to stay inside the nanopore. On the contrast, the positive PMF barrier shows water molecules experience thermodynamic repellence at the pore center. This is in good agreement with the observed preferential occupation of ethanol relative to water within the P14 pore. Comparatively, in the P24 pore, the difference in the PMF profiles for the two species becomes less significant, corresponding to the increase of pore occupation of water. Generally, pure water shows higher permeability passing through the nanoporous graphene than pure ethanol (see Figure 2d). Therefore, the low water penetration in the mixture could be considered as the energetic competition effect between the two species. The ethanol molecules have a thermodynamics preference to occupy the pore center, blocking the permeation path of water molecules, and consequently reducing the pore transport of water. The total PMF profiles for molecule passage through the graphene pores can be decomposed into the two contributions arising from the solution medium and the graphene surface. For the nanopore P14, the strong attractive interaction between the graphene and the


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moving ethanol molecule provides a dominating contribution to the free-energy well, enabling ethanol molecules to stably dwell in the nanopore. However, the weak interaction between the graphene and the water molecule cannot compensate the repulsive solvation interaction from the solution environment, resulting in the formation of the free-energy barrier for water passage. For the P24 membrane with larger pore size, the free-energy barrier also appears for the ethanol penetration, which is different from the behavior in the P14 and can be attributed to the reduced surface interaction. Even so, in the P24 membrane the free-energy barrier for ethanol is still lower than that for water.

Figure 5. Thermodynamics interaction mechanism. The total potential of mean force (PMF) for one ethanol molecule and one water molecule in the mixture (50mol%) passing through the nanopores as a function of the z distance from the pore center, and its decomposition into the solution-induced contribution and the graphene interaction contribution in the P14 (a) and the P24 (b). The z=0 represents the position of graphene pore center. Inset is the illustration of the presumable penetration mechanism for ethanol-water mixture though nanoporous graphene membrane.

Overall, the penetration separation mechanism for ethanol-water mixture passing through



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the nanoporous graphene membranes can be explained as the strong attractive interaction of ethanol with the hydrophobic nanoporous graphene, which induces enhanced surface adsorption and preferential pore occupation for ethanol. This unique interfacial behavior could exclude water molecules from the graphene surface and hinder the penetration of water into the nanopore, consequently leading to the different penetrability between the two species in the mixture. However, as the pore size increases, the interacting strength of ethanol with nanoporous graphene declines, and thus the selectivity of ethanol decreases. The similar separation mechanism, known as the chemical affinity sieving mechanism,56-58 has been proposed in the gas transport through porous materials. In this previously proposed mechanism, it is the different chemistry affinity inside the pore structure that results in the selective pore penetration. However, in the 2-D nanoporous graphene, both the pore trapping effect in the monoatomic thick pore and the surface adsorbing interaction on the planar sheet are cooperatively responsible for the separation mechanism for ethanol-water mixtures (see the insert of Figure 5). According to our simulation results, the selective penetration separation is determined by the interfacial affinity of two-dimensional graphene pores. Thus, this sieving mechanism could be applied or extended to other 2-D nanoporous carbon structures for various liquid-phase mixtures. In the present work, we did not consider the functionalization of pore edge in the graphene membranes. According to our results, the separation mechanism for ethanol-permselective sieving is mainly determined by the graphene surface affinity. Therefore, it is expected that the enhanced permeation of ethanol could still be observed in the porous graphene membranes with just functionalization on pore edge. However, with the presence of oxygenated groups on the graphene plane, the surface affinity toward ethanol will decrease and molecular permeation will be affected. Further study will be necessary to clarify this.


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We further investigated the permeation of ethanol-water mixtures under various concentrations by using the P14 membrane. The penetration behavior of ethanol and water molecules across the nanopore is shown in Figure 6a under the simulation pressure of 300 MPa. For all the concentrations, the P14 membrane generally demonstrates higher ethanol penetration ability relative to water. However, under lower concentration condition, the water permeation number shows certain increase, probably due to the reduction of ethanol adsorption on the graphene surface. Figure 6b plots the ethanol concentration in the permeate side as a function of initial feed concentration at various time points. Although the ethanol concentration in the permeate side decreases with the simulation time increases, it is generally higher than the initial concentration in the feed solution. Therefore, the P14 graphene membrane can remain static separation capacity for ethanol-water mixtures over a wide range of concentrations. In particular, there is significant enrichment of ethanol in the permeate side when the ethanol content in feed is dilute. This indicates that the P14 membrane could be applied as the ethanol-permselective membrane to separate lower concentrated ethanol from the aqueous solutions in bioethanol preparation process. It is noted that under the feed molar fraction of 90%, close to the azeotropic composition of ethanol-water mixture, the graphene membrane still demonstrates high permeability of ethanol. The separation performance/efficiency under various concentrations can also be characterized by the variable, ( J e J w ) ( x e x w ) (see Figure 6c). The higher separation performance could be achieved (~35) at the low ethanol concentration in feed. The separation efficiency decreases firstly, and then follows by a rise with the concentration further increasing. The favorable separation performance under high ethanol concentration is owing to the increased ethanol accumulation near the graphene pores, which eliminates the water permeation. This



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separation performance also suggests that the nanoporous graphene membrane could be coupled with other unit operations to separate high concentrated ethanol solution.

Figure 6. Effect of initial feed concentrations on the penetration separation performance of ethanol-water mixtures. (a) Penetrated number of species in the ethanol-water mixtures for various feed concentrations at a pressure of 300 MPa. (b) Ethanol enriching concentration in the permeate side versus the ethanol concentration in the initial feed at different simulation times. (c) The variation of the separation performance with the initial feed concentrations.

4. Conclusion In summary, our MD simulations demonstrate that two-dimensional nanoporous graphene membranes could selectively separate ethanol from ethanol-water mixtures with ethanol permeability several orders of magnitude higher than current pervaporation membranes. The unusual separation mechanism has been revealed on the molecular-level through the interfacial structural analysis. The strong attractive affinity of ethanol with hydrophobic nanoporous graphene induces preferential surface adsorption and pore occupation for ethanol, simultaneously impeding the permeation path of water into the nanopore and consequently giving rise to the


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selective penetration of ethanol over water in the mixture. This simulation result provides new understanding of molecular transport across atomically thick nanoporous membranes. Although the practical application of nanoporous graphene membrane still faces huge challenge, the unique separation mechanism presented in this work opens up a new concept design for developing single-layer graphene networks as new-typed membrane structures and nanofluidic devices. This work is important not only for nanoporous graphenes, but also for graphynes, polyphenylene, and other generalized 2-D ultrathin porous membranes.

AKNOWLEDGEMENTS This work was supported by the National Natural Science Foundation of China under Grants 21376116, 973National Basic research Program of China (2015CB655301), Research funding from State Key Laboratory of Materials-Oriented Chemical Engineering (ZK201404), and A PAPD Project of Jiangsu Higher Education Institution.

ASSOCIATED CONTENT Supporting Information Details of simulation method and some supplementary figures, and a video of the pervaporation MD simulation. This material is available free of charge via the Internet at http://pubs.acs.org.

REFERENCES (1) Jiang, D. E.; Cooper, V. R.; Dai, S. Porous Graphene as the Ultimate Membrane for Gas Separation. Nano Lett. 2009, 9, 4019-4024. (2) Schrier, J. Helium separation using porous graphene membranes. J. Phys. Chem. Lett. 2010, 1, 2284-2287. (3) Koenig, S. P.; Wang, L. D.; Pellegrino, J.; Bunch, J. S. Selective molecular sieving through porous graphene. Nat. Nanotechnol. 2012, 7, 728-732. (4) Boutilier, M. S.; Sun, C.; O’Hern, S. C.; Au, H.; Hadjiconstantinou, N. G.; Karnik, R. Implications of permeation through intrinsic defects in graphene on the design of defecttolerant membranes for gas separation. ACS nano 2014, 8, 841-849. (5) Celebi, K.; Buchheim, J.; Wyss, R. M.; Droudian, A.; Gasser, P.; Shorubalko, I.; Kye, J.-I.; Lee, C.; Park, H. G. Ultimate permeation across atomically thin porous graphene. Science 2014, 344, 289-292.



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(6) Kim, H. W.; Yoon, H. W.; Yoon, S. M.; Yoo, B. M.; Ahn, B. K.; Cho, Y. H.; Shin, H. J.; Yang, H.; Paik, U.; Kwon, S.; Choi, J. Y.; Park, H. B. Selective Gas Transport Through Few-Layered Graphene and Graphene Oxide Membranes. Science 2013, 342, 91-95. (7) Surwade, S. P.; Smirnov, S. N.; Vlassiouk, I. V.; Unocic, R. R.; Veith, G. M.; Dai, S.; Mahurin, S. M. Water desalination using nanoporous single-layer graphene. Nat. Nanotechnol. 2015, 10, 459-464. (8) Cohen-Tanugi, D.; Grossman, J. C. Water desalination across nanoporous graphene. Nano Lett. 2012, 12, 3602-3608. (9) Koh, D. Y.; Lively, R. P. NANOPOROUS GRAPHENE Membranes at the limit. Nat. Nanotechnol. 2015, 10, 385-386. (10) Konatham, D.; Yu, J.; Ho, T. A.; Striolo, A. Simulation insights for graphene-based water desalination membranes. Langmuir 2013, 29, 11884-11897. (11) Ni, Z. H.; Wang, H. M.; Kasim, J.; Fan, H. M.; Yu, T.; Wu, Y. H.; Feng, Y. P.; Shen, Z. X. Graphene thickness determination using reflection and contrast spectroscopy. Nano Lett. 2007, 7, 2758-2763. (12) Geim, A. K. Graphene: Status and Prospects. Science 2009, 324, 1530-1534. (13) Geim, A. K.; Novoselov, K. S. The rise of graphene. Nat. Mater. 2007, 6, 183-191. (14) Schneider, G. F.; Kowalczyk, S. W.; Calado, V. E.; Pandraud, G.; Zandbergen, H. W.; Vandersypen, L. M. K.; Dekker, C. DNA Translocation through Graphene Nanopores. Nano Lett. 2010, 10, 3163-3167. (15) Venkatesan, B. M.; Estrada, D.; Banerjee, S.; Jin, X.; Dorgan, V. E.; Bae, M.-H.; Aluru, N. R.; Pop, E.; Bashir, R. Stacked Graphene-Al2O3 Nanopore Sensors for Sensitive Detection of DNA and DNA–Protein Complexes. ACS Nano 2012, 6, 441-450. (16) Avdoshenko, S. M.; Nozaki, D.; Gomes da Rocha, C.; González, J. W.; Lee, M. H.; Gutierrez, R.; Cuniberti, G. Dynamic and Electronic Transport Properties of DNA Translocation through Graphene Nanopores. Nano Lett. 2013, 13, 1969-1976. (17) Banerjee, S.; Shim, J.; Rivera, J.; Jin, X.; Estrada, D.; Solovyeva, V.; You, X.; Pak, J.; Pop, E.; Aluru, N.; Bashir, R. Electrochemistry at the Edge of a Single Graphene Layer in a Nanopore. ACS Nano 2013, 7, 834-843. (18) Puster, M.; Rodríguez-Manzo, J. A.; Balan, A.; Drndić, M. Toward Sensitive Graphene Nanoribbon – Nanopore Devices by Preventing Electron Beam-Induced Damage. ACS Nano 2013, 7, 11283-11289. (19) Radich, J. G.; Kamat, P. V. Making Graphene Holey. Gold-Nanoparticle-Mediated Hydroxyl Radical Attack on Reduced Graphene Oxide. ACS Nano 2013, 7, 5546-5557. (20) Nam, S.; Choi, I.; Fu, C.-c.; Kim, K.; Hong, S.; Choi, Y.; Zettl, A.; Lee, L. P. Graphene Nanopore with a Self-Integrated Optical Antenna. Nano Lett. 2014, 14, 5584-5589. (21) O'Hern, S. C.; Boutilier, M. S. H.; Idrobo, J. C.; Song, Y.; Kong, J.; Laoui, T.; Atieh, M.; Karnik, R. Selective Ionic Transport through Tunable Subnanometer Pores in Single-Layer Graphene Membranes. Nano Lett. 2014, 14, 1234-1241. (22) Ohba, T. The Thinnest Molecular Separation Sheet by Graphene Gates of Single-Walled Carbon Nanohorns. Acs Nano 2014, 8, 11313-11319. (23) O'Hern, S. C.; Stewart, C. A.; Boutilier, M. S. H.; Idrobo, J.-C.; Bhaviripudi, S.; Das, S. K.; Kong, J.; Laoui, T.; Atieh, M.; Karnik, R. Selective Molecular Transport through Intrinsic Defects in a Single Layer of CVD Graphene. Acs Nano 2012, 6, 10130-10138.


Page 21 of 23

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(24) Tao, Y. H.; Xue, Q. Z.; Liu, Z. L.; Shan, M. X.; Ling, C. C.; Wu, T. T.; Li, X. F. Tunable Hydrogen Separation in Porous Graphene Membrane: First-Principle and Molecular Dynamic Simulation. Acs Appl. Mater. Inter. 2014, 6, 8048-8058. (25) Gai, J.-G.; Gong, X.-L. Zero internal concentration polarization FO membrane: functionalized graphene. J. Mater. Chem. A 2014, 2, 425-429. (26) Du, H.; Li, J.; Zhang, J.; Su, G.; Li, X.; Zhao, Y. Separation of hydrogen and nitrogen gases with porous graphene membrane. J. Phys. Chem. C 2011, 115, 23261-23266. (27) Shan, M.; Xue, Q.; Jing, N.; Ling, C.; Zhang, T.; Yan, Z.; Zheng, J. Influence of chemical functionalization on the CO2/N-2 separation performance of porous graphene membranes. Nanoscale 2012, 4, 5477-5482. (28) Liu, H. J.; Dai, S.; Jiang, D. E. Insights into CO2/N-2 separation through nanoporous graphene from molecular dynamics. Nanoscale 2013, 5, 9984-9987. (29) Balamurugan, K.; Baskar, P.; Kumar, R. M.; Das, S.; Subramanian, V. Interaction of Carbon Nanotube with Ethylene Glycol-Water Binary Mixture: A Molecular Dynamics and Density Functional Theory Investigation. J. Phys. Chem. C 2012, 116, 4365-4373. (30) Zhao, W.-H.; Shang, B.; Du, S.-P.; Yuan, L.-F.; Yang, J.; Zeng, X. C. Highly selective adsorption of methanol in carbon nanotubes immersed in methanol-water solution. J. Chem. Phys. 2012, 137, 034501. (31) Metya, A. K.; Khan, S.; Singh, J. K. Wetting Transition of the Ethanol-Water Droplet on Smooth and Textured Surfaces. J. Phys. Chem. C 2014, 118, 4113-4121. (32) Lundgren, M.; Allan, N. L.; Cosgrove, T.; George, N. Wetting of water and water/ethanol droplets on a non-polar surface: A molecular dynamics study. Langmuir 2002, 18, 1046210466. (33) Zhao, M.; Yang, X. Segregation Structures and Miscellaneous Diffusions for Ethanol/Water Mixtures in Graphene-Based Nanoscale Pores. J. Phys. Chem. C 2015, 119, 21664-21673. (34) Tian, X. L.; Yang, Z. X.; Zhou, B.; Xiu, P.; Tu, Y. S. Alcohol-induced drying of carbon nanotubes and its implications for alcohol/water separation: A molecular dynamics study. J. Chem. Phys. 2013, 138, 204711. (35) Severin, N.; Sokolov, I. M.; Rabe, J. P. Dynamics of Ethanol and Water Mixtures Observed in a Self-Adjusting Molecularly Thin Slit Pore. Langmuir 2014, 30, 3455-3459. (36) Severin, N.; Gienger, J.; Scenev, V.; Lange, P.; Sokolov, I. M.; Rabe, J. P. Nanophase Separation in Monomolecularly Thin Water-Ethanol Films Controlled by Graphene. Nano Lett. 2015, 15, 1171-1176. (37) Cheng, A.; Steele, W. Computer simulation of ammonia on graphite. I. Low temperature structure of monolayer and bilayer films. J. Chem. Phys. 1990, 92, 3858-3866. (38) Zheng, J.; Lennon, E. M.; Tsao, H.-K.; Sheng, Y.-J.; Jiang, S. Transport of a liquid water and methanol mixture through carbon nanotubes under a chemical potential gradient. J. Chem. Phys. 2005, 122, 214702. (39) Fern, J. T.; Keffer, D. J.; Steele, W. V. Vapor-liquid equilibrium of ethanol by molecular dynamics simulation and voronoi tessellation. J. Phys. Chem. B 2007, 111, 13278-13286. (40) Berendsen, H.; Grigera, J.; Straatsma, T. The missing term in effective pair potentials. J. Phys. Chem. 1987, 91, 6269-6271. (41) Ren, X.; Zhou, B.; Wang, C. Water-induced ethanol dewetting transition. J. Chem. Phys. 2012, 137, 024703. (42) Darden, T.; York, D.; Pedersen, L. Particle mesh Ewald: An N⋅ log (N) method for Ewald sums in large systems. J. Chem. Phys. 1993, 98, 10089-10092.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 22 of 23

(43) Plimpton, S. FAST PARALLEL ALGORITHMS FOR SHORT-RANGE MOLECULARDYNAMICS. J. Comput. Phys. 1995, 117, 1-19. (44) Suk, M.; Aluru, N. Molecular and continuum hydrodynamics in graphene nanopores. Rsc. Adv. 2013, 3, 9365-9372. (45) Suk, M. E.; Aluru, N. Ion transport in sub-5-nm graphene nanopores. J. Chem. Phys. 2014, 140, 084707. (46) Hu, Z. Q.; Chen, Y. F.; Jiang, J. W. Zeolitic imidazolate framework-8 as a reverse osmosis membrane for water desalination: Insight from molecular simulation. J. Chem. Phys. 2011, 134, 134705. (47) Ma, M. D.; Shen, L.; Sheridan, J.; Liu, J. Z.; Chen, C.; Zheng, Q. Friction of water slipping in carbon nanotubes. Phys. Rev. B 2011, 83, 036316. (48) Goldsmith, J.; Martens, C. C. Molecular dynamics simulation of salt rejection in model surface-modified nanopores. J. Phys. Chem. Lett. 2009, 1, 528-535. (49) Suk, M. E.; Aluru, N. Water transport through ultrathin graphene. J. Phys. Chem. Lett. 2010, 1, 1590-1594. (50) Cohen-Tanugi, D.; Grossman, J. C. Mechanical Strength of Nanoporous Graphene as a Desalination Membrane. Nano Lett. 2014, 14, 6171-6178. (51) Borjigin, T.; Sun, F.; Zhang, J.; Cai, K.; Ren, H.; Zhu, G. A microporous metal–organic framework with high stability for GC separation of alcohols from water. Chem. Commun. 2012, 48, 7613-7615. (52) Cohen-Tanugi, D.; Grossman, J. C. Water permeability of nanoporous graphene at realistic pressures for reverse osmosis desalination. J. Chem. Phys. 2014, 141, 074704. (53) Peng, P.; Shi, B.; Lan, Y. A review of membrane materials for ethanol recovery by pervaporation. Sep. Sci. Technol. 2010, 46, 234-246. (54) Wei, P.; Cheng, L.-H.; Zhang, L.; Xu, X.-H.; Chen, H.-l.; Gao, C.-j. A review of membrane technology for bioethanol production. Renew. Sust. Energ. Rev. 2014, 30, 388-400. (55) Sun, C.; Boutilier, M. S.; Au, H.; Poesio, P.; Bai, B.; Karnik, R.; Hadjiconstantinou, N. G. Mechanisms of molecular permeation through nanoporous graphene membranes. Langmuir 2014, 30, 675-682. (56) Jiao, Y.; Du, A.; Hankel, M.; Smith, S. C. Modelling carbon membranes for gas and isotope separation. Phys. Chem. Chem. Phys. 2013, 15, 4832-4843. (57) Zhao, Y.; Xie, Y.; Liu, Z.; Wang, X.; Chai, Y.; Yan, F. Two‐Dimensional Material Membranes: An Emerging Platform for Controllable Mass Transport Applications. Small 2014, 10, 4521-4542. (58) Thomas, M.; Corry, B.; Hilder, T. A. What Have We Learnt About the Mechanisms of Rapid Water Transport, Ion Rejection and Selectivity in Nanopores from Molecular Simulation? Small 2014, 10, 1453-1465.


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