Home Journals Articles Updates About
CN
image
收藏切换
How Pore Hydrophilicity Influences Water Permeability?
收藏切换
PDF
Fang Xu, Mingjie Wei, Xin Zhang, Yang Song, Wei Zhou, Yong Wang
Research. Vol 2019 Article ID 2581241
Less
收藏切换
Research. Vol 2019 Article ID 2581241
• Research Article •
How Pore Hydrophilicity Influences Water Permeability?
Full
Fang Xu, Mingjie Wei, Xin Zhang, Yang Song, Wei Zhou, Yong Wang
Affiliations
  • State Key Laboratory of Materials-Oriented Chemical Engineering, Jiangsu National Synergetic Innovation Center for Advanced Materials, and College of Chemical Engineering, Nanjing Tech University, Nanjing 211816, Jiangsu, China
About Author:

Authors’ Contributions

Yong Wang supervised this research. Fang Xu and Mingjie Wei conducted the simulations with assistance from Xin Zhang, Zhou Wei and Yang Song. All authors discussed and analyzed results. Fang Xu, Mingjie Wei, and Yong Wang wrote the manuscript with inputs from other authors. Fang Xu and Mingjie Wei contributed equally to this work.

Published: 2019-02-20 doi: 10.34133/2019/2581241
Outline
收藏切换

Membrane separation is playing increasingly important role in providing clean water. Simulations predict that membrane pores with strong hydrophobicity produce ultrahigh water permeability as a result of low friction. However, experiments demonstrate that hydrophilic pores favor higher permeability. Herein we simulate water molecules transporting through interlayers of two-dimensional nanosheets with various hydrophilicities using nonequilibrium molecular dynamics. We reveal that there is a threshold pressure drop (ΔPT), exceeding which stable water permeability appears. Strongly hydrophobic pores exhibit extremely high ΔPT, prohibiting the achievement of ultrahigh water permeability under the experimentally accessible pressures. Under pressures < ΔPT, water flows in hydrophobic pores in a running-stop mode because of alternative wetting and nonwetting, thus leading to significantly reduced permeability. We discover that hydrophilic modification to one surface of the nanosheet can remarkably reduce ΔPT by > 99%, indicating a promising strategy to experimentally realize ultrafast membranes.

Fang Xu, Mingjie Wei, Xin Zhang, Yang Song, Wei Zhou, Yong Wang. How Pore Hydrophilicity Influences Water Permeability?[J]. Research, 2019 , 2019 (2) : 2581241 - . DOI: 10.34133/2019/2581241
Full Text
Less
1. Introduction
Less
Membrane separation is playing a key role in supplying potable water to people’s daily life and industry [1]. Separation performances are significantly influenced by the hydrophilicity of the membrane pores. However, there are contradictory observations between experimental and simulation studies in the influence of membrane hydrophilicity on water permeability. Experimentally, it is commonly acknowledged that membranes for applications ranging from microfiltration to reverse osmosis (RO) should have strong hydrophilicity to ensure adequate water permeabilities [2–4]. Oppositely, molecular dynamics (MD) simulations demonstrate that hydrophobic pores favor enhanced water flux [5–8]. When water is confined in subnanometer pores, e.g., in nanofiltration (NF) or RO membranes [9], there would be only one or two water layers inside the pores; i.e., all the water molecules are included in the boundary layer. Therefore, the interaction between water molecules and pore walls would significantly influence water transport. Hydrophilicity increases the interaction between water molecules and pore walls and influences water permeability of a membrane from two opposite sides. On one hand, the positive side, the hydrophilic interaction could increase the infiltration capillary force, helping the membrane to uptake water molecules and to increase the membrane wettability. The infiltration capillary force increases dramatically with decreasing pore sizes. When the pore size is narrowed down to the subnanometer scale, water molecules inside the membrane will have greater probability to form hydrogen bonded networks, which plays an important role for water transport in the confined environment [10]. On the other hand, the negative side, stronger interaction between water molecules and pore walls results in increased friction and consequently reduces the flow velocity [11]. Larger proportion of water molecules will interact with the pore wall as the pore size decreases, and thus the friction effect becomes more pronounced. Therefore, it is necessary and worthwhile to investigate the combination effect of these two sides on water permeability.
Specifically, the recently emerging laminated membranes established from two-dimensional (2D) materials have subnanometer interlayer gaps, which offer slit-shaped pores for water transport [12–17]. In studies of these membranes, there also exists contradiction between experimental observations and simulation results on how the pore hydrophilicity influences the permeability. In experiments, Sun et al. [17] reported a laminar MoS2 membrane exhibiting a 3- to 5-times higher water flux than the graphene oxide (GO) membrane. They attributed the improved flux to the exposure of the hydrophilic sulfur atoms in the MoS2 monolayer sheets. Ren et al. [18] fabricated ion separation membranes from nanosheets of Ti3C2Tx MXene and they believed the promoted water flow was due to the hydrophilic nature of Ti3C2Tx. In contrast, by MD simulations, Wei et al. [19] found that the flow rate exhibited a significant enhancement between graphene layers, but the enhancement broke down while the graphene sheets were modified with hydrophilic groups. Moreover, Chen et al. [20] simulated water transport through GO interlayers with various concentrations of hydrophilic hydroxyl groups and found that the volumetric flux was negatively related to hydroxyl concentrations, implying that stronger hydrophilicity was unfavorable for water transport. This contradiction between experimental and simulation results confuses the understanding on the effect of pore hydrophilicity on water permeability. Therefore, identifying the origin of this contradiction is of great significance not only for deeper understanding on the role of material hydrophilicity in water transport but also for the design and preparation of ultrafast membranes.
In this work, we simulate water transport through pores constructed from 2D nanosheets with various hydrophilicities by using nonequilibrium molecular dynamics (NEMD). We reveal that strong hydrophobicity leads to high water permeability but also high threshold pressure drop (ΔPT), and only the applied pressure drop exceeds ΔPT; the high permeability can occur. For hydrophobic membranes, ΔPT’s are typically at the scale of several hundreds of MPa, far more than experimentally accessible pressure drops. This explains why we seldom experimentally observe simulation-predicted ultrahigh water permeability on hydrophobic membranes. Based on these understandings we develop a new strategy—hydrophilic modification to the outer pore surface—to efficiently reduce ΔPT of hydrophobic membranes to the experimentally accessible scale at slight expense of permeability loss, thus enabling the experimental realization of ultrafast membranes.
2. Results
Less
2.1. Apparent Flux and Permeability
We calculate the water flux of membranes with varying hydrophilicities under pressure drops (ΔPs) ranging from 100 to 600 MPa by fitting the slope of the flow curve (Figure S1). As shown in Figure 1(a), the flux of each membrane is proportional to ΔP despite the changing hydrophilicities. However, to achieve the continuous water flow ΔPs should be higher than a certain value, beyond which the proportional relationship between flux and ΔP is valid. For hydrophilic membranes (contact angles, CAs < 95°), the proportional relationship is valid from 100 MPa. For the membrane with CA= 120°, no water flux can be produced under ΔPs < 200 MPa. For the most hydrophobic membrane (CA = 138°), ΔP should be raised to nearly 400 MPa to obtain the continuous water flow. Apparently, these ΔPs are much larger than those applied in experiments. Therefore, we denote the water flux or permeability under such high ΔPs as apparent flux or apparent permeability as they will hardly be obtained in experiments. Figure 1(b) presents the apparent permeabilities of each membrane, which are obtained by directly fitting the flux values in Figure 1(a). It is obvious that the hydrophilicity of membranes plays a key role in governing water permeability. The permeability increases monotonically with rising hydrophobicity. Other simulation works give similar results no matter how they tune the hydrophilicity of the membrane, such as scaling the vdW interactions strength [21], applying artificial surface partial charge patterns [5], adjusting the density of hydroxyl groups [20], or utilizing different substances [6]. That is, all simulation works demonstrate a negative correlation between water flux and hydrophilicity.
2.2. Water Transport in Hydrophobic Membranes
As mentioned above, for hydrophobic membranes (CAs > 120°), no water molecule could pass through the pores and consequently no flux was measured under the ΔP of 100 MPa. This implies that the proportional relationship between flux and ΔP cannot extend to the region of lower ΔPs. For the most hydrophobic membrane with a CA of 138°, the proportional relationship starts around 400 MPa. However, it does not necessarily imply that it gives no flux under ΔPs below 400 MPa. We further simulated its flux under lower ΔPs with smaller pressure intervals to reveal the relationship in a broader range of ΔPs. As shown in Figure 1(c), it is obvious that water flux of the hydrophobic membrane (CA = 138°) is not proportional to ΔP and instead there exists three stages within the range of ΔPs from 100 to 600 MPa. In the first stage where ΔP < 220 MPa, no evident water flux is observed. It is found that there is no water molecule inside the membrane, indicating the nonwetting state of the membrane. In the second stage, where ΔP ranges from 220 to 350 MPa, the water flux rises rapidly. In the last stage, the water flux is proportional to ΔPs. In contrast, for the hydrophilic membrane with a CA of 29° the water flux is always proportional to ΔP within the entire range of ΔP, implying that under high ΔPs hydrophobic pores exhibit a wetting state similar to hydrophilic pores.
To elucidate the three-staged relationship between water flux and ΔP for the membrane with the CA of 138°, we investigated the microscopic details of water molecules inside this hydrophobic membrane under different ΔPs. We recorded the number of water molecules in the permeate side as a function of simulation time, which is shown in Figure 1(d). Sufficient water molecules were provided to the feed side so that the simulation can continue for adequate time. The evolution of the number of permeated water molecules demonstrates that the flow rate of water through the membrane is constant under 400 MPa (in the third stage), which is also similar to water passing through the membrane with a CA of 29° shown in Figure S1. However, there appears many steps in the flow curve in the case of 250 MPa (in the second stage), which implies a discontinuous water flow. This is actually a running-stop mode including alternative “running” and “stop” states.
To quantitatively investigate this “running-stop” water flow, we monitored the number of water molecules inside the membrane (Nwater) during the entire flow process and revealed the tendency of its variations. Figure 2(a) illustrates the probability distribution of Nwater under four different ΔPs spanning from the second to the third stage. In the case of 400 MPa, Nwater is distributing near 250, indicating that the number of water molecules remains ~250 during the simulation time. Nevertheless, when ΔP is decreased from 400 MPa to 250 MPa, Nwater distributes in an increasingly wide range, indicating that the pores are not completely filled with water during the entire simulation time. As vividly shown in the snapshots in Figure 2(b), water streams inside the pores under these ΔPs break off during the flowing process. This results in water transport in the running-stop mode.
When the system reaches a steady state, the driving force and resistance are in balance, which leads to a continuous water flow and a stable flux. Oppositely, the running-stop transport is a metastable state with a wetting and a nonwetting state repeating alternately rather than a stable continuous flow. The hydrogen bonded network that stretched from the entrance to the exit of the pore was reported to be responsible for the fast transport in membranes made of 2D grapheme [12]. Moreover, the hydrogen bonded network must be reformed when water molecules enter highly confined pores [22, 23]. Based on these understandings, we realize that in the second stage the wetting behavior of water molecules cycles between the wetting state and the nonwetting state. In the wetting state, the hydrogen bonded network completely forms, therefore, the running state is reached and the flow occurs. However, in the nonwetting state, the water stream is disconnected as the hydrogen bonded network breaks, terminating water flow (the stop state). Figure 2(a) shows Nwater distributes in wider ranges with decreasing ΔPs, indicating the running state lasts shorter and the stop state last longer. Consequently, lower water permeabilities are obtained under decreasing ΔPs. When the ΔP is decreased to be lower than 220 MPa, the stop states (nonwetting) dominate, and consequently no flux can be observed. Similarly, the transport mode would turn into the constant running state (wetting) when the ΔP rises to a certain value, beyond which the water flux becomes always proportional to ΔPs.
2.3. The Threshold Pressure Drop
We term this critical ΔP, above which the pores are in the wetting state and water flow reaches the constant running state, the threshold pressure drop, ΔPT. The ΔPT’s for membranes with various CAs are plotted in Figure 3. For hydrophilic membranes (CA = 29°, 50°, or 70°), the ΔPT’s are nearly zero because these hydrophilic membranes could be constantly wetted and no external pressure is needed. For hydrophobic membranes, the ΔPT appears and rises rapidly with increasing CAs. Now we understand that higher hydrophobicity leads to greater water permeability but higher ΔPT’s. ΔPs used in experimental works generally exceed ΔPT’s of hydrophilic membranes, so one could easily obtain the continuous flux. For hydrophobic membranes, the ΔPT’s are much higher than experimental pressures, and it is difficult to wet the pores under the experimentally used ΔPs, resulting in no water permeability. In contrast, in simulations the employed ΔPs are typically in the scale of several hundreds of MPa, generally exceeding the ΔPT’s of both hydrophobic and hydrophilic membranes, and both membranes can reach the wetting state. Once wetted, hydrophobic membranes exhibit higher water permeabilities than hydrophilic ones due to the low friction of the former, as shown in Figure 1(b). This explains the contradictory results between experiments and simulations with respect to the influence of pore hydrophilicity on the water permeability.
2.4. Modification Strategies to Reduce Threshold Pressure Drop
Hydrophobic membranes exhibit ultrahigh water permeabilities, but only under extremely high pressures because of their high ΔPT’s. Therefore, to experimentally realize the ultrahigh permeabilities, the ΔPT’s of hydrophobic membranes should be significantly depressed to the scale close to the experimentally used ΔPs. ΔPT’s are dependent on the wetting state of the membrane. Therefore, suitably changing the wetting behavior of the membranes by hydrophilic modification may reduce ΔPT’s at no or little expense of water permeabilities. In addition to the complete hydrophilic modification to pore walls, which totally eliminates the pristine hydrophobicity of the pores and consequently the ultrahigh permeability, there has emerged a few other strategies to perform hydrophilic modification on selective positions of 2D nanosheets, that is, hydrophilic modification to the pore entrances [24] and regionally hydrophilic modification inside the pores [12, 25]. We then examine their efficiency in reducing ΔPT’s.
In the first strategy, the hydrophilic modification was applied only to the entrance region while the inner pore was still kept its pristine hydrophobicity (with CA of 95°, 120°, or 138°). The entrance region was defined as 5 Å from the inlet of the pore and modified with atoms to give a CA of 29°. We investigated the wetting behavior and the permeability under both low and high ΔPs. As shown in Figure 4(a), water molecules could occupy only the modified part at the entrance region under low ΔPs (≤1 MPa), suggesting that the ΔPT exists not only at the entire entrance region, but throughout the inner hydrophobic area. As a result, we did not observe any water molecules passing through the membranes. Under high ΔPs (hundreds of MPa), the water flux also experience the three-staged flow as described above. Moreover, no evident change of ΔPT for these membranes with various CAs is observed. The ∆PT in this case is still as high as that for the unmodified hydrophobic membrane, indicating that this hydrophilic modification takes little effect on depressing the ΔPT.
Hydrophilic modification to graphene is frequently achieved by oxidation [26], and the generated oxygen-containing groups prefer to form clusters, which resulting in the patches of pristine (hydrophobic) and oxidized (hydrophilic) regions on the surface of grapheme [27–29]. This can be considered as regionally hydrophilic modification inside the pore. To find out the influence of this modification on water permeability, the pore wall was patterned with two regions: one-half is hydrophilic (CA = 29°) and the other is hydrophobic (CA = 95°, 120°, or 138°). Under low ΔPs, there are two distinctive wetting states for membranes with different hydrophilicities. When the hydrophobicity is moderate (CA = 95°), water molecules start to occupy the hydrophilic region and then saturate the hydrophobic region progressively, indicating that the impact of hydrophilic interaction help to uptake more water molecules into the pores. Ban et al. [30] also observed that water first saturated the oxidized regions and then flooded to the pristine regions in GO membranes. ΔPT of this modified membrane is significantly decreased from ~ 40 MPa for the pristine membrane to less than 0.1 MPa, while the permeability under high ΔPs is moderately decreased from ~20×103 to ~17×103 L·m−2·h−1·MPa−1.
However, as can be seen from Figure 4(b) when the CA of the hydrophobic region is higher than 120°, water molecules would not wet the hydrophobic region after covering the hydrophilic region. This implies that the ΔPT of the hydrophobic region cannot be depressed by this regional modification. The nonwetting state of the hydrophobic regions in the pores means that the actual porosity under this condition turns into only half of the pristine one, thus, the flux will not be higher than half of the pristine one. While under high ΔPs, water molecules fill the membrane completely like that in the unmodified hydrophobic membrane, however the water permeability is lower compared to the unmodified hydrophobic membrane. Wei et al. [25] attributed this to a prominent side-pinning effect. Due to the inconsistence of wetting under low and high ΔPs, it is unreliable to extrapolate the permeability under high ΔPs to those under low ΔPs. Therefore, we understand that the regional hydrophilic modification cannot depress ΔPT’s of strongly hydrophobic membranes although it works for moderately hydrophobic membranes (CA < 120°).
Alternatively, we propose another strategy, that is, hydrophilic modification to the outer pore walls, which can efficiently reduce ΔPT’s of strongly hydrophobic membranes (CA = 120° or higher). The fast water transport through graphene interlayers is prohibited by the hydrophilic oxygen-containing groups inside the pores [19, 20]. This is because the interaction between water and hydrophilic groups is too strong. Considering that 2D monolayer nanosheets are only one-atom thick, we anticipate that presence of these groups on the outer surface will reduce the interaction to some degree but will not fully eliminate it. Thus, the modification to the outer walls may also affect the water transport inside the pore across this one-atom thickness. As shown in Figure 4(c), the commonly used hydrophilic hydroxyl groups are chosen to functionalize the outer walls. The hydroxyl groups were randomly distributed outside the pore wall with concentration c = nOH/nC = 24%, which is similar to both experimental [31, 32] and simulation [20] results. The all-atom optimized potential for liquid simulations (OPLS-AA) [33] was used for the functional groups, which is successfully applied in previous studies on the graphene oxides membrane [19, 25, 34].
We investigated the membrane with the CA of 120°, whose ΔPT cannot be reduced by the strategy of regional modification as we discussed above. By hydrophilic modification to the outer surface the pore could be wetted under low pressures (~1 MPa), suggesting that the ΔPT is depressed over 99% as it is ~140 MPa for the pristine membrane. Meanwhile, the permeability under high ΔPs, which is the slope of the flux-ΔP curve, is decreased by only 10 % (Figure 5(a)) and this value could be applied to the low ΔPs due to the consistence of their wetting states. Moreover, to exclude the particularity of the channel size, we also applied this method to 0.7 nm-wide pores, and obtain similar results as shown in Figure 5(b). Furthermore, Figure 5 also indicates that thus modified membranes exhibit much higher water permeability than initially hydrophilic membranes, for instance, the membrane with a CA of 70°. Therefore, we conclude that hydrophilic modification to the outer pore surface is a highly efficient strategy to reduce ΔPT at slight expense of water permeability. This modification strategy requires hydrophilic modification exclusively on one side of 2D monolayers while keeps the strong hydrophobicity of the other side intact. Importantly, such a “Janus” 2D structure has already been experimentally obtained [35, 36], implying the big potential to experimentally realize ultrafast membranes by this hydrophilic modification strategy.
3. Discussion
Less
In summary, membranes were constructed from 2D nanosheets with varying hydrophilicities to investigate the contradiction between experimental and simulation results on the influence of the pore hydrophilicity on water transport. We discover that the contradiction originates from the discrepancy of pressure drops (ΔP) in experiments and simulations. For hydrophilic membranes, there is a proportional relationship between flux and ΔPs. In contrast, for hydrophobic membranes, this proportional relationship only exits under high ΔPs. Under a threshold pressure drops, ΔPT, there is no flux or a discontinuous water flow in the “running-stop” mode. In simulations, the ΔP is usually set to be extremely high to accelerate the computing. This high ΔP could facilely exceed the ΔPT, so that the membranes are always in the wetting state. The different wetting states hinder the extrapolation of permeability from high ΔP’s to low ΔP’s, leading to the contradictory understanding on the effect of hydrophilicity. Unlike the hydrophobic membranes, the hydrophilic membranes could be wetted under low ΔPs, indicating ΔPT is nearly zero. We investigate the efficiency of different hydrophilic modifications in depressing ΔPT’s of hydrophobic membranes and propose a new strategy, hydrophilic modification of the outer pore wall, which is able to depress ΔPT’s by > 99% of highly hydrophobic membranes at the slight expense of ~ 10% reduction in water permeability. This work identifies the origin of the contradictions between experimental and simulation results on the effect of pore hydrophilicity on water permeability, which not only helps to understand water transport in nanopores but also to design and experimental realization of ultrafast membranes.
4. Methods
Less
The simulation system consisted of a membrane with one water reservoir and one additional moveable piston at each end, as shown in Figure S2. A 2D nanosheet bilayer was set to be parallel to the yz plane and modeled as the slit pore, and the length of the pore was set to 6 nm while the thickness of the pore was set to 0.8 nm, which was based on the interlayer spacing of the graphene oxide membrane [12, 31, 37]. Another four constraint walls (vertical to the pore wall and parallel to the xy plane) were served as the membrane surfaces, which were used to embed the pores and restrict the movement of water molecules. Such a membrane model is reliable and has also been adopted in other MD simulations [21]. The two water reservoirs formed the feed side (high pressure) and the permeate side (low pressure). Hence, the flow direction through the membrane was defined along the z direction. Besides, the system was bounded by two rigid pistons used to create a pressure drop (ΔP) across the membrane. The simulation box lengths in x and y directions are set equal to the lateral size of the bilayer (34.3 Å) and the height of four constraint walls (43.7 Å), respectively.
All the simulations were carried out using the large-scale atomic/molecular massively parallel simulator (LAMMPS) package. The SPC/E model was utilized to construct the water molecules. The SHAKE algorithm was applied to constrain the bonds and angles of the water molecules, which reduced high frequency vibrations and saved the simulation time. The interaction for all atoms included vdW and electrostatic terms. The former was modeled with a LJ potential, 4ε[(σ/r)12-(σ/r)6], and the cutoff was set as 1.0 nm. In this work, the structure of graphene was merely used as the channel model. According to Werder et al. [38], the hydrophilicity of the membrane could be tuned by changing the ε parameter between C and O atoms while keeping the σ parameter fixed. The ε parameter signifies a microscopic interaction, which is not a macroscopic manifestation of hydrophilicity. In addition, the CA of a water droplet on a surface is usually employed to measure the membrane hydrophilicity, which is a macroscopic manifestation of the microscopic interactions between the surface and the water molecules. Hence, according to the work studying the CA of water on 2D planes with varying ε parameters [38], we also adopt the CAs ranging from 29° to 138° to characterize and distinguish the membranes with varying hydrophilicities. This method to adjust hydrophilicity was reported to have a negligible effect on the channel diameter [39]. The long-range electrostatic interactions are computed by using the particle-particle particle-mesh (PPPM) algorithm with a root mean square accuracy of 10−5. The membrane models were fixed in position throughout the simulation, and periodic boundary conditions (PBC) were applied in the x and y directions. A time step of 1 fs was used for all simulations.
Initially, for each simulation, the energy of system was minimized for 1000 steps. Then an external force along the -z/+z direction, f, is applied to each atom of top/bottom piston to produce a pressure of 1 atm (Equation (1)), allowing the system to reach the desired pressure and the bulk water to reach the equilibrium density (1 g/cm3).
(1)f=PAn
where P denoted the desired pressure on the piston, A was the area of the piston, and n represented the atom number of the piston. The system was kept at room temperature (300K) using the Berendsen thermostat. After the equilibrium simulation of 2 ns, the NEMD simulations were carried out by applying another external force on the bottom piston, so the ΔP could be calculated by
(2)∆P=Pbottom-Ptop
After the first 1 ns of stabilization of the NEMD simulation, statistics and trajectories were gathered during 2-10 ns for different systems, which was long enough to obtain the well-converged simulation results. This method of applying ΔP has been thoroughly described in a previous study [40] and adopted by many other researchers to simulate the membrane process [41–44]. To enhance the signal-to-noise ratio and accelerate the NEMD simulations, like other simulating studies [41–46] we perform the simulations under very high ΔPs (100 MPa-600 MPa), which are much higher than pressures applied in experiments and industrial usage.
Funding
Less
  • National Basic Research Program of China(2015CB655301)
  • National Key Research and Development Program of China(2017YFC0403902)
  • National Natural Science Foundation of China(21825803; 21506091)
  • Jiangsu Natural Science Foundations(BK20150944; BK20150063)
References
Less
1
Werber J. R., Osuji C. O., and Elimelech M., “Materials for next-generation desalination and water purification membranes,” Nature Reviews Materials, vol. 1, 2016
2
Xu Q., Yang Y., Wang X., Wang Z., Jin W., Huang J., and Wang Y., “Atomic layer deposition of alumina on porous polytetrafluoroethylene membranes for enhanced hydrophilicity and separation performances,” Journal of Membrane Science, vol. 415-416, pp. 435–443, 2012
3
Yan F., Chen H., Lü Y., Lü Z., Yu S., Liu M., and Gao C., “Improving the water permeability and antifouling property of thin-film composite polyamide nanofiltration membrane by modifying the active layer with triethanolamine,” Journal of Membrane Science, vol. 513, pp. 108–116, 2016
4
Zhao Y., Zhang Z., Dai L., Mao H., and Zhang S., “Enhanced both water flux and salt rejection of reverse osmosis membrane through combining isophthaloyl dichloride with biphenyl tetraacyl chloride as organic phase monomer for seawater desalination,” Journal of Membrane Science, vol. 522, pp. 175–182, 2017
5
Goldsmith J., and Martens C. C., “Molecular dynamics simulation of salt rejection in model surface-modified nanopores,” The Journal of Physical Chemistry Letters, vol. 1, no. 2, pp. 528–535, 2010
6
Joseph S., and Aluru N. R., “Why are carbon nanotubes fast transporters of water?,” Nano Letters, vol. 8, no. 2, pp. 452–458, 2008
7
Wang L., Dumont R. S., and Dickson J. M., “Nonequilibrium molecular dynamics simulation of pressure-driven water transport through modified CNT membranes,” The Journal of Chemical Physics, vol. 138, no. 12, 2013
8
Li T.-D., Gao J., Szoszkiewicz R., Landman U., and Riedo E., “Structured and viscous water in subnanometer gaps,” Physical Review B: Condensed Matter and Materials Physics, vol. 75, no. 11, 2007
9
Wei T., Zhang L., Zhao H., Ma H., Sajib M. S. J., Jiang H., and Murad S., “Aromatic polyamide reverse-osmosis membrane: an atomistic molecular dynamics simulation,” The Journal of Physical Chemistry B, vol. 120, no. 39, pp. 10311–10318, 2016
10
Suk M. E., and Aluru N. R., “Water transport through ultrathin graphene,” The Journal of Physical Chemistry Letters, vol. 1, no. 10, pp. 1590–1594, 2010
11
Xu F., Song Y., Wei M., and Wang Y., “Water flow through interlayer channels of two-dimensional materials with various hydrophilicities,” The Journal of Physical Chemistry C, vol. 122, no. 27, pp. 15772–15779, 2018
12
Nair R. R., Wu H. A., Jayaram P. N., Grigorieva I. V., and Geim A. K., “Unimpeded permeation of water through helium-leak-tight graphene-based membranes,” Science, vol. 335, pp. 442–444, 2012
13
Kim W.-G., and Nair S., “Membranes from nanoporous 1D and 2D materials: A review of opportunities, developments, and challenges,” Chemical Engineering Science, vol. 104, pp. 908–924, 2013
14
Hirunpinyopas W., Prestat E., Worrall S. D., Haigh S. J., Dryfe R. A. W., and Bissett M. A., “Desalination and nanofiltration through functionalized laminar MoS2 membranes,” ACS Nano, vol. 11, no. 11, pp. 11082–11090, 2017
15
Liu G., Jin W., and Xu N., “Two-dimensional-material membranes: a new family of high-performance separation membranes,” Angewandte Chemie International Edition, vol. 55, no. 43, pp. 13384–13397, 2016
16
Sun L., Ying Y., Huang H., Song Z., Mao Y., Xu Z., and Peng X., “Ultrafast molecule separation through layered WS2 nanosheet membranes,” ACS Nano, vol. 8, no. 6, pp. 6304–6311, 2014
17
Sun L., Huang H., and Peng X., “Laminar MoS2 membranes for molecule separation,” Chemical Communications, vol. 49, no. 91, pp. 10718–10720, 2013
18
Ren C. E., Hatzell K. B., Alhabeb M., Ling Z., Mahmoud K. A., and Gogotsi Y., “Charge- and size-selective ion sieving through Ti3C2Tx mxene membranes,” The Journal of Physical Chemistry Letters, vol. 6, no. 20, pp. 4026–4031, 2015
19
Wei N., Peng X., and Xu Z., “Breakdown of fast water transport in graphene oxides,” Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, vol. 89, no. 1, 2014
20
Chen B., Jiang H., Liu X., and Hu X., “Observation and analysis of water transport through graphene oxide interlamination,” The Journal of Physical Chemistry C, vol. 121, no. 2, pp. 1321–1328, 2017
21
Liu B., Wu R., Baimova J. A., Wu H., Law A. W.-K., Dmitriev S. V., and Zhou K., “Molecular dynamics study of pressure-driven water transport through graphene bilayers,” Physical Chemistry Chemical Physics, vol. 18, no. 3, pp. 1886–1896, 2016
22
Muscatello J., Jaeger F., Matar O. K., and Müller E. A., “Optimizing water transport through graphene-based membranes: insights from nonequilibrium molecular dynamics,” ACS Applied Materials & Interfaces, vol. 8, no. 19, pp. 12330–12336, 2016
23
Tunuguntla R. H., Henley R. Y., Yao Y.-C., Pham T. A., Wanunu M., and Noy A., “Enhanced water permeability and tunable ion selectivity in subnanometer carbon nanotube porins,” Science, vol. 357, no. 6353, pp. 792–796, 2017
24
Huang K., Liu G., Shen J., Chu Z., Zhou H., Gu X., Jin W., and Xu N., “High-efficiency water-transport channels using the synergistic effect of a hydrophilic polymer and graphene oxide laminates,” Advanced Functional Materials, vol. 25, no. 36, pp. 5809–5815, 2015
25
Wei N., Peng X., and Xu Z., “Understanding water permeation in graphene oxide membranes,” ACS Applied Materials & Interfaces, vol. 6, no. 8, pp. 5877–5883, 2014
26
Dikin D. A., Stankovich S., Zimney E. J., Piner R. D., Dommett G. H. B., Evmenenko G., Nguyen S. T., and Ruoff R. S., “Preparation and characterization of graphene oxide paper,” Nature, vol. 448, no. 7152, pp. 457–460, 2007
27
Wilson N. R., Pandey P. A., Beanland R., Young R. J., Kinloch I. A., Gong L., Liu Z., Suenaga K., Rourke J. P., York S. J., and Sloan J., “Graphene oxide: structural analysis and application as a highly transparent support for electron microscopy,” ACS Nano, vol. 3, no. 9, pp. 2547–2556, 2009
28
Kumar P. V., Bardhan N. M., Tongay S., Wu J., Belcher A. M., and Grossman J. C., “Scalable enhancement of graphene oxide properties by thermally driven phase transformation,” Nature Chemistry, vol. 6, no. 2, pp. 151–158, 2014
29
Erickson K., Erni R., Lee Z., Alem N., Gannett W., and Zettl A., “Determination of the local chemical structure of graphene oxide and reduced graphene oxide,” Advanced Materials, vol. 22, no. 40, pp. 4467–4472, 2010
30
Ban S., Xie J., Wang Y., Jing B., Liu B., and Zhou H., “Insight into the nanoscale mechanism of rapid H2O transport within a graphene oxide membrane: impact of oxygen functional group clustering,” ACS Applied Materials & Interfaces, vol. 8, no. 1, pp. 321–332, 2016
31
Zheng S., Tu Q., Urban J. J., Li S., and Mi B., “Swelling of graphene oxide membranes in aqueous solution: characterization of interlayer spacing and insight into water transport mechanisms,” ACS Nano, vol. 11, no. 6, pp. 6440–6450, 2017
32
Hu M., and Mi B., “Layer-by-layer assembly of graphene oxide membranes via electrostatic interaction,” Journal of Membrane Science, vol. 469, pp. 80–87, 2014
33
Jorgensen W. L., Maxwell D. S., and Tirado-Rives J., “Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids,” Journal of the American Chemical Society, vol. 118, no. 45, pp. 11225–11236, 1996
34
Chen B., Jiang H., Liu X., and Hu X., “Molecular insight into water desalination across multilayer graphene oxide membranes,” ACS Applied Materials & Interfaces, vol. 9, no. 27, pp. 22826–22836, 2017
35
Zheng Z., Nottbohm C. T., Turchanin A., Muzik H., Beyer A., Heilemann M., Sauer M., and Gölzhäuser A., “Janus nanomembranes: A generic platform for chemistry in two dimensions,” Angewandte Chemie International Edition, vol. 49, no. 45, pp. 8493–8497, 2010
36
Zhang L., Yu J., Yang M., Xie Q., Peng H., and Liu Z., “Janus graphene from asymmetric two-dimensional chemistry,” Nature Communications, vol. 4, no. 1, 2013
37
Abraham J., Vasu K. S., Williams C. D., Gopinadhan K., Su Y., Cherian C. T., Dix J., Prestat E., Haigh S. J., Grigorieva I. V., Carbone P., Geim A. K., and Nair R. R., “Tunable sieving of ions using graphene oxide membranes,” Nature Nanotechnology, vol. 12, no. 6, pp. 546–550, 2017
38
Werder T., Walther J. H., Jaffe R. L., Halicioglu T., and Koumoutsakos P., “On the water-carbon interaction for use in molecular dynamics simulations of graphite and carbon nanotubes,” The Journal of Physical Chemistry B, vol. 107, no. 6, pp. 1345–1352, 2003
39
Strong S. E., and Eaves J. D., “The dynamics of water in porous two-dimensional crystals,” The Journal of Physical Chemistry B, vol. 121, no. 1, pp. 189–207, 2016
40
Huang C., Nandakumar K., Choi P. Y. K., and Kostiuk L. W., “Molecular dynamics simulation of a pressure-driven liquid transport process in a cylindrical nanopore using two self-adjusting plates,” The Journal of Chemical Physics, vol. 124, no. 23, 2006
41
Cohen-Tanugi D., Lin L.-C., and Grossman J. C., “Multilayer nanoporous graphene membranes for water desalination,” Nano Letters, vol. 16, no. 2, pp. 1027–1033, 2016
42
Cohen-Tanugi D., and Grossman J. C., “Water desalination across nanoporous graphene,” Nano Letters, vol. 12, no. 7, pp. 3602–3608, 2012
43
Heiranian M., Farimani A. B., and Aluru N. R., “Water desalination with a single-layer MoS 2 nanopore,” Nature Communications, vol. 6, 2015
44
Shen M., Keten S., and Lueptow R. M., “Dynamics of water and solute transport in polymeric reverse osmosis membranes via molecular dynamics simulations,” Journal of Membrane Science, vol. 506, pp. 95–108, 2016
45
Ding M., Szymczyk A., Goujon F., Soldera A., and Ghoufi A., “Structure and dynamics of water confined in a polyamide reverse-osmosis membrane: A molecular-simulation study,” Journal of Membrane Science, vol. 458, pp. 236–244, 2014
46
Gao W., She F., Zhang J., Dumée L. F., He L., Hodgson P. D., and Kong L., “Understanding water and ion transport behaviour and permeability through poly(amide) thin film composite membrane,” Journal of Membrane Science, vol. 487, pp. 32–39, 2015
Appendix
Less
Year 2019 volume 2019 Issue 2
PDF
35
20
Cite this Article
BibTeX
Article Info
doi: 10.34133/2019/2581241
  • Receive Date：2018-11-02
  • Online Date：2026-10-27
  • Published：2019-02-20
Article Data
Affiliations
History
  • Received：2018-11-02
  • Accepted：2019-01-10
Funding
National Basic Research Program of China(2015CB655301)
National Key Research and Development Program of China(2017YFC0403902)
National Natural Science Foundation of China(21825803; 21506091)
Jiangsu Natural Science Foundations(BK20150944; BK20150063)
Affiliations
    State Key Laboratory of Materials-Oriented Chemical Engineering, Jiangsu National Synergetic Innovation Center for Advanced Materials, and College of Chemical Engineering, Nanjing Tech University, Nanjing 211816, Jiangsu, China
References
Share
https://castjournals.cast.org.cn/joweb/research/EN/10.34133/2019/2581241
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62662699(Operation and Technical Support)
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House