Ion adsorption within nanopores is involved in numerous applications. However, a comprehensive understanding of the fundamental relationship between in-pore ion concentration and pore size, ...particularly in the sub-2 nm range, is scarce. This study investigates the ion-species-dependent concentration in multilayered graphene membranes (MGMs) with tunable nanoslit sizes (0.5–1.6 nm) using nuclear magnetic resonance and computational simulations. For Na+-based electrolytes in MGMs, the concentration of anions in graphene nanoslits increases in correlation with their chaotropic properties. As the nanoslit size decreases, the concentration of chaotropic ion (BF4 –) increases, whereas the concentration of kosmotropic ions (Cit3–, PO4 3–) and other ions (Ac–, F–) decreases or changes slightly. Notably, anions remain more concentrated than counter Na+ ions, leading to electroneutrality breakdown and unipolar anion packing in MGMs. A continuum modeling approach, integrating molecular dynamic simulation with the Poisson–Boltzmann model, elucidates these observations by considering water-mediated ion–graphene non-electrostatic interactions and charge screening from graphene walls.
In this study, we have demonstrated a simple, scalable, and environmentally friendly route for controllable fabrication of continuous, well-intergrown ZIF-8 on a flexible polymer substrate via ...contra-diffusion method in conjunction with chemical vapor modification of the polymer surface. The combined chemical vapor modification and contra-diffusion method resulted in controlled formation of a thin, defect-free, and robust ZIF-8 layer on one side of the support in aqueous solution at room temperature. The ZIF-8 membrane exhibited propylene permeance of 1.50 × 10–8 mol m–2 s–1 Pa−1 and excellent selective permeation properties; after post heat-treatment, the membrane showed ideal selectivities of C3H6/C3H8 and H2/C3H8 as high as 27.8 and 2259, respectively. The new synthesis approach holds promise for further development of the fabrication of high-quality polymer-supported ZIF membranes for practical separation applications.
Graphene-based nanoporous materials have been extensively explored as high-capacity ion electrosorption electrodes for supercapacitors. However, little attention has been paid to exploiting the ...interactions between electrons that reside in the graphene lattice and the ions adsorbed between the individual graphene sheets. Here we report that the electronic conductance of a multilayered reduced graphene oxide membrane, when used as a supercapacitor electrode, can be modulated by the ionic charging state of the membrane, which gives rise to a collective electrolyte gating effect. This gating effect provides an in-operando approach for probing the charging dynamics of supercapacitors electrically. Using this approach, we observed a pore-size-dependent ionic hysteresis or memory effect in reduced graphene oxide membranes when the interlayer distance is comparable to the ion diameter. Our results may stimulate the design of novel devices based on the ion-electron interactions under nanoconfinement.
Controlling the pore sizes of graphene-based membranes is essential for exploring their transport properties and related applications such as water desalination. We demonstrated the scalable ...fabrication of ultrathin graphene membranes with precisely controlled subnanometer pores by co-assembly of graphene oxide nanosheet and polymer on a porous ceramic substrate and subsequent reduction and carbonization. The resulting graphene membranes with an intercalated structure show unprecedented molecular-sieving water evaporation properties, achieving water evaporation flux of 49.8 ± 1.5–472.3 ± 14.2 L m−2 h−1 and 99.99% NaCl rejection at 20–70 °C and a vacuum of −800 mbar. The water flux is remarkably higher than that of conventional microporous membranes and greatly surpasses that of the state-of-the art membranes with various pore sizes. This work provides a new insight into water transport and evaporation through graphene subnanometer pores, and a new strategy for membrane design for water desalination and other evaporation separations.
Display omitted
•Graphene-based subnanometer porous membranes were fabricated.•The graphene membranes exhibited high water evaporation flux and complete salt rejection.•This work provides new fundamental insights into water evaporation at molecular scale.•The graphene membranes show great potential for desalination and evaporation separations.
In this work, the nitrogen doped graphene films with hierarchical network structures have been successfully fabricated via a robust routine of adjusting the content of large- and small-sized reduced ...graphene oxide (RGO) sheets in the graphene films, where the large-sized RGO sheets act as the backbone to form the well-connected network structures and small-sized RGO sheets act as the interlayer and interpore linkers to connect the large-sized RGO sheets and large pores. A maximum capacitance of 429.7 F g−1 is obtained for the optimized RGO electrode with small-sized RGO content of 40 wt%, which is 61.9% improvement compared to the RGO electrodes with pure large-sized RGO sheets. While, the specific surface area and electrical conductivity of the optimized RGO electrode are 36.1% and 26.2% improvement compared to the pure large-sized electrode. Therefore, we attribute the improved capacitance primarily comes from the increasing of specific surface area due to the formation of fine hierarchical structures and the better connection of the network structures (the increasing of electrical conductivity) for the optimized RGO electrode. Besides, the optimized RGO electrode exhibits good cycle, rate performance, power and energy density compared to the previous carbon based supercapacitor. Herein, we can precisely control the specific surface area and electrical conductivity of RGO electrode by simply adjusting the content of large- and small-sized RGO, which may shed useful insight for the design of high performance supercapacitors and Li-ion batteries.
Laminar membranes stacked by 2D materials are an emerging selective unit in separating processes across disciplines for their controllable mass transport properties. In general, parallel nanochannels ...formed between neighboring layers, owing to their adjustable size and surface chemistry, are considered the dominant transport regulator. Besides these flat interlayer channels, wrinkled morphology has also existed in 2D membranes, but the structure and potential transporting role of such curved channel remain largely unexplored. This study demonstrates that nanowrinkles are intrinsically formed in graphene‐based membranes, featuring an arc‐like shape with around 2.5 nm high center and two narrow wedge corners. By a facile “solvent‐treatment” during assembly, the membranes are tuned to possess different wrinkle density. In transport tests involving water and ions, the appearance of more wrinkles yields higher water permeation yet has limited effect on ion passage. These findings suggest that nanowrinkles by themselves serve as fast transporting ways while their connection with narrow interlayer channels can form a selective network. Results here are expected to deepen the understanding of mass transport mechanisms in current laminar membranes (e.g., graphene‐based) and provide strategies for designing future 2D membranes via wrinkle engineering.
Two‐dimensional (2D) layered membranes are a multidisciplinary selective unit for possessing parallel interlayer channels to control mass transport. Besides these flat channels, wrinkled structures can also emerge to affect transmembrane transport. The formation and tuning of nanowrinkles are studied in graphene‐based membranes to understand their role in regulating mass transport in 2D membranes.
Confined ion transport is involved in nanoporous ionic systems. However, it is challenging to mechanistically predict its electrical characteristics for rational system design and performance ...evaluation using an electrical circuit model due to the gap between the circuit theory and the underlying physical chemistry. Here, we demonstrate that machine learning can bridge this gap and produce physics-based nanocircuitry, based on equation discovery from the modified Poisson–Nernst–Planck simulation results where an anomalous constructive diffusion–migration interplay of confined ions is unveiled. This bridging technique allows us to gain physical insights into ion dynamics in nanoporous electrodes, such as nonideal cyclic voltammetry and dynamic, pore-size-dependent surface condution.
We report a cooperative reformable channel system in a ZIF-L crystal arising from coexistence of three types of local flexible ligands. The reformable channel is able to regulate permeation of a ...nonspherical guest molecule, such as N2 or CO2, based on its longer molecular dimension, which is in a striking contrast to conventional molecular sieves that regulate the shorter cross-sectional dimension of the guest molecules. Our density functional theory (DFT) calculations reveal that the guest molecule induces dynamic motion of the flexible ligands, leading to the channel reformation, and then the guest molecule reorientates itself to fit in the reformed channel. Such a unique “induced fit-in” mechanism causes the gas molecule to pass through six-membered-ring windows in the c- crystal direction of ZIF-L with its longer axis parallel to the window plane. Our experimental permeance of N2 through the ZIF-L membranes is about three times greater than that of CO2, supporting the DFT simulation predictions.
Finite element method has been widely applied in modeling natural fibers and natural fiber reinforced composites. This paper is a comprehensive review of finite element models of natural fibers and ...natural fiber reinforced composites, focusing on the micromechanical properties (strength, deformation, failure, and damage), thermal properties (thermal conductivity), and macro shape deformation (stress–strain and fracture). Representative volume element model is the most popular homogenization-based multi-scale constitutive method used in the finite element method to investigate the effect of microstructures on the mechanical and thermal properties of natural fibers and natural fiber reinforced composites. The representative volume element models of natural fibers and natural fiber reinforced composites at various length scales are discussed, including two types of geometrical modeling methods, the computer-based modeling method and the image-based modeling method. Their modeling efficiency and accuracy are also discussed.
Pairing the positive and negative electrodes with their individual dynamic characteristics at a realistic cell level is essential to the practical optimal design of electrochemical energy storage ...devices. However, the complex relationship between the performance data measured for individual electrodes and the two‐electrode cells used in practice often makes an optimal pairing experimentally challenging. Taking advantage of the developed tunable graphene‐based electrodes with controllable structure, experiments with machine learning are successfully united to generate a large pool of capacitance data for graphene‐based electrode materials with varied slit pore sizes, thicknesses, and charging rates and numerically pair them into different combinations for two‐electrode cells. The results show that the optimal pairing parameters of positive and negative electrodes vary considerably with the operation rate of the cells and are even influenced by the thickness of inactive components. The best‐performing individual electrode does not necessarily result in optimal cell‐level performance. The machine learning‐assisted pairing approach presents much higher efficiency compared with the traditional trial‐and‐error approach for the optimal design of supercapacitors. The new engineering science insights observed in this work enable the adoption of artificial intelligence techniques to efficiently translate well‐developed high‐performance individual electrode materials into real energy storage devices.
This work reports how combining experiments and machine learning provides a new, practical approach to pairing the two electrodes in an electrochemical energy storage device for optimal cell‐level performance under various operating conditions. This approach and the new engineering science insights in this work are vital for translating breakthrough energy materials into optimal cell‐level performance for practical applications.
PDF
