Preanchoring Enabled Directional Modification of Atomically Thin Membrane for High-Performance Osmotic Energy Generation.

Salinity gradient energy is an environmentally friendly energy source that possesses potential to meet the growing global energy demand. Although covalently modified nanoporous graphene membranes are prospective candidates to break the trade-off between ion selectivity and permeability, the random reaction sites and inevitable defects during modification reduce the reaction efficiency and energy conversion performance. Here, we developed a preanchoring method to achieve directional modification near the graphene nanopores periphery. Numerical simulation revealed that the improved surface charge density around nanopores results in exceptional K+/Cl- selectivity and osmotic energy conversion performance, which agreed well with experimental results. Ionic transport measurements showed that the directionally modified graphene membranes achieved an outstanding power density of 81.6 W m-2 with an energy conversion efficiency of 35.4% under a 100-fold salinity gradient, outperforming state-of-the-art graphene-based nanoporous membranes. This work provided a facile approach for precise modification of nanoporous graphene membranes and opened up new ways for osmotic power harvesting.

Biomimetic N-Doped Graphene Membrane for Proton Exchange Membranes.

Proton exchange membranes (PEMs) with both high selectivity and high permeance are of great demand in hydrogen-based applications, especially in fuel cells. Although graphene membranes have shown high selectivity of protons over other ions and molecules, the relatively low permeance of protons through perfect pristine graphene restricts its practical applications. Inspired by the nitrogen-assisted proton transport in biological systems, we introduced N-doping to increase the proton permeance and proposed a type of N-doped graphene membranes (NGMs) for proton exchange, which have both high proton permeance and high selectivity. Compared to the state-of-the-art commercial PEMs, the NGMs show significant increases in both areal proton conductivity (2-3 orders of magnitude) and selectivity of proton to methanol (1-2 orders of magnitude). The work realized the controllable tuning of proton permeance of the graphene membrane with N-doping and developed a new type of graphene-based PEMs with high performance for practical applications.

Covalently Functionalized Nanopores for Highly Selective Separation of Monovalent Ions.

Biological ion channels possess prominent ion transport performances attributed to their critical chemical groups across the continuous nanoscale filters. However, it is still a challenge to imitate these sophisticated performances in artificial nanoscale systems. Herein, this work develops the strategy to fabricate functionalized graphene nanopores in pioneer based on the synergistic regulation of the pore size and chemical properties of atomically thin confined structure through decoupling etching combined with in situ covalent modification. The modified graphene nanopores possess asymmetric ion transport behaviors and efficient monovalent metal ions sieving (K+ /Li+ selectivity ≈48.6). Meanwhile, it also allows preferential transport for cations, the resulting membranes exhibit a K+ /Cl- selectivity of 76 and a H+ /Cl- selectivity of 59.3. The synergistic effects of steric hindrance and electrostatic interactions imposing a higher energy barrier for Cl- or Li+ across nanopores lead to ultra-selective H+ or K+ transport. Further, the functionalized graphene nanopores generate a power density of 25.3 W m-2 and a conversion efficiency of 33.9%, showing potential application prospects in energy conversion. The theoretical studies quantitatively match well with the experimental results. The feasible preparation of functionalized graphene nanopores paves the way toward direct investigation on ion transport mechanism and advanced design in devices.

Artificial Monovalent Metal Ion-Selective Fluidic Devices Based on Crown Ether@Metal-Organic Frameworks with Subnanochannels.

Precise regulation of ion transport through nanoscale pores will profoundly impact diverse fields from separation to energy conversion but is still challenging to achieve in artificial ion channels. Herein, inspired by the exquisite ion selectivity of biological Na+ channels, we have successfully fabricated hierarchically grown metal-organic frameworks (MOFs) on an asymmetrical substrate assisted by atomically thin nanoporous graphene. Efficient separation of monovalent metal ions is realized by encapsulating 18-crown-6 into MOF crystals. The resulting 18-crown-6@ZIF-67/ZIF-8 device, with subnanochannels and specific K+ binding sites, shows an ultrahigh Li+ conductivity of 1.46 × 10-2 S cm-1 and selectivities of 9.56 and 6.43 for Li+/K+ and Na+/K+, respectively. The Li+ conductivity is around 1-2 orders of magnitude higher than reported values for the other MOF materials. It is the first time that MOFs with subnanochannels realize selective transport of Na+ (ionic diameter of 1.9 Å) over K+ (2.6 Å) based on subangstrom differences in their ionic diameter. Our work opens new avenues to develop crown ether@MOF platforms toward efficient ion transistors, fluidic logic devices, and biosensors.

Accurate Sequence-Based Prediction of Deleterious nsSNPs with Multiple Sequence Profiles and Putative Binding Residues.

Non-synonymous single nucleotide polymorphisms (nsSNPs) may result in pathogenic changes that are associated with human diseases. Accurate prediction of these deleterious nsSNPs is in high demand. The existing predictors of deleterious nsSNPs secure modest levels of predictive performance, leaving room for improvements. We propose a new sequence-based predictor, DMBS, which addresses the need to improve the predictive quality. The design of DMBS relies on the observation that the deleterious mutations are likely to occur at the highly conserved and functionally important positions in the protein sequence. Correspondingly, we introduce two innovative components. First, we improve the estimates of the conservation computed from the multiple sequence profiles based on two complementary databases and two complementary alignment algorithms. Second, we utilize putative annotations of functional/binding residues produced by two state-of-the-art sequence-based methods. These inputs are processed by a random forests model that provides favorable predictive performance when empirically compared against five other machine-learning algorithms. Empirical results on four benchmark datasets reveal that DMBS achieves AUC > 0.94, outperforming current methods, including protein structure-based approaches. In particular, DMBS secures AUC = 0.97 for the SNPdbe and ExoVar datasets, compared to AUC = 0.70 and 0.88, respectively, that were obtained by the best available methods. Further tests on the independent HumVar dataset shows that our method significantly outperforms the state-of-the-art method SNPdryad. We conclude that DMBS provides accurate predictions that can effectively guide wet-lab experiments in a high-throughput manner.

Highly porous nanofiber-supported monolayer graphene membranes for ultrafast organic solvent nanofiltration.

Scalable fabrication of monolayer graphene membrane on porous supports is key to realizing practical applications of atomically thin membranes, but it is technologically challenging. Here, we demonstrate a facile and versatile electrospinning approach to realize nanoporous graphene membranes on different polymeric supports with high porosity for efficient diffusion- and pressure-driven separations. The conductive graphene works as an excellent receptor for deposition of highly porous nanofibers during electrospinning, thereby enabling direct attachment of graphene to the support. A universal "binder" additive is shown to enhance adhesion between the graphene layer and polymeric supports, resulting in high graphene coverage on nanofibers made from different polymers. After defect sealing and oxygen plasma treatment, the resulting nanoporous membranes demonstrate record-high performances in dialysis and organic solvent nanofiltration, with a pure ethanol permeance of 156.8 liters m−2 hour−1 bar−1 and 94.5% rejection to Rose Bengal (1011 g mol−1) that surpasses the permeability-selectivity trade-off.

Decimeter-Scale Atomically Thin Graphene Membranes for Gas-Liquid Separation.

Graphene holds great potential for fabricating ultrathin selective membranes possessing high permeability without compromising selectivity and has attracted intensive interest in developing high-performance separation membranes for desalination, natural gas purification, hemodialysis, distillation, and other gas-liquid separation. However, the scalable and cost-effective synthesis of nanoporous graphene membranes, especially designing a method to produce an appropriate porous polymer substrate, remains very challenging. Here, we report a facile route to fabricate decimeter-scale (∼15 × 10 cm2) nanoporous atomically thin membranes (NATMs) via the direct casting of the porous polymer substrate onto graphene, which was produced by chemical vapor deposition (CVD). After the vapor-induced phase-inversion process under proper experimental conditions (60 °C and 60% humidity), the flexible nanoporous polymer substrate was formed. The resultant skin-free polymer substrate, which had the proper pore size and a uniform spongelike structure, provided enough mechanical support without reducing the permeance of the NATMs. It was demonstrated that after creating nanopores by the O2 plasma treatment, the NATMs were salt-resistant and simultaneously showed 3-5 times higher gas (CO2) permeance than the state-of-the-art commercial polymeric membranes. Therefore, our work provides guidance for the technological developments of graphene-based membranes and bridges the gap between the laboratory-scale "proof-of-concept" and the practical applications of NATMs in the industry.

Tunable Pore Size from Sub-Nanometer to a Few Nanometers in Large-Area Graphene Nanoporous Atomically Thin Membranes.

Membranes are key components in chemical purification, biological separation, and water desalination. Traditional polymeric membranes are subjected to a ubiquitous trade-off between permeance and selectivity, which significantly hinders the separation performance. Nanoporous atomically thin membranes (NATMs), such as graphene NATMs, have the potential to break this trade-off. Owing to their uniqueness of two-dimensional structure and potential nanopore structure controllability, NATMs are expected to have outstanding selectivity through molecular sieving while achieving ultimate permeance at the same time. However, a drastic selectivity discrepancy exists between the proof-of-concept demonstrations and scalable separation applications in graphene membranes. In this paper, we offer a possible solution to narrow this discrepancy by tuning the pore density and pore size separately with two successive plasma treatments. We demonstrate that by narrowing the pore size distribution, the selectivity of graphene membranes can be greatly increased. Low-energy argon plasma is first applied to nucleate high density of defects in graphene. Controlled oxygen plasma is then utilized to selectively enlarge the defects into nanopores with desired sizes. This method is scalable, and the fabricated 1 cm2 graphene NATMs with sub-nanometer pores can separate KCl and Allura Red with a selectivity of 104 and a permeance of 1.1 × 10-6 m s-1. The pores in NATMs can be further tuned from gas-selective sub-nanometer pores to a few nanometer size. The fabricated NATMs show a selectivity of 35 between CO2 and N2. With longer enlargement time, a selectivity of 21.2 between a lysozyme and bovine serum albumin can also be achieved with roughly four times higher permeance than that of a commercial dialysis membrane. This research offers a solution to realize NATMs of tunable pore size with a narrow pore size distribution for different separation processes from sub-nanometer in gas separation or desalination to a few nanometers in dialysis.