Facile Anhydrous Proton Transport on Hydroxyl Functionalized Graphane

Machine Learning Electron Density Prediction Using Weighted Smooth Overlap of Atomic Positions

Having access to accurate electron densities in chemical systems, especially for dynamical systems involving chemical reactions, ion transport, and other charge transfer processes, is crucial for numerous applications in materials chemistry. Traditional methods for computationally predicting electron density data for such systems include quantum mechanical (QM) techniques, such as density functional theory. However, poor scaling of these QM methods restricts their use to relatively small system sizes and short dynamic time scales. To overcome this limitation, we have developed a deep neural network machine learning formalism, which we call deep charge density prediction (DeepCDP), for predicting charge densities by only using atomic positions for molecules and condensed phase (periodic) systems. Our method uses the weighted smooth overlap of atomic positions to fingerprint environments on a grid-point basis and map it to electron density data generated from QM simulations. We trained models for bulk systems of copper, LiF, and silicon; for a molecular system, water; and for two-dimensional charged and uncharged systems, hydroxyl-functionalized graphane, with and without an added proton. We showed that DeepCDP achieves prediction R2 values greater than 0.99 and mean squared error values on the order of 10-5e2 Å-6 for most systems. DeepCDP scales linearly with system size, is highly parallelizable, and is capable of accurately predicting the excess charge in protonated hydroxyl-functionalized graphane. We demonstrate how DeepCDP can be used to accurately track the location of charges (protons) by computing electron densities at a few selected grid points in the materials, thus significantly reducing the computational cost. We also show that our models can be transferable, allowing prediction of electron densities for systems on which it has not been trained but that contain a subset of atomic species on which it has been trained. Our approach can be used to develop models that span different chemical systems and train them for the study of large-scale charge transport and chemical reactions.

In Silico Demonstration of Fast Anhydrous Proton Conduction on Graphanol

Development of new materials capable of conducting protons in the absence of water is crucial for improving the performance, reducing the cost, and extending the operating conditions for proton exchange membrane fuel cells. We present detailed atomistic simulations showing that graphanol (hydroxylated graphane) will conduct protons anhydrously with very low diffusion barriers. We developed a deep learning potential (DP) for graphanol that has near-density functional theory accuracy but requires a very small fraction of the computational cost. We used our DP to calculate proton self-diffusion coefficients as a function of temperature, to estimate the overall barrier to proton diffusion, and to characterize the impact of thermal fluctuations as a function of system size. We propose and test a detailed mechanism for proton conduction on the surface of graphanol. We show that protons can rapidly hop along Grotthuss chains containing several hydroxyl groups aligned such that hydrogen bonds allow for conduction of protons forward and backward along the chain without hydroxyl group rotation. Long-range proton transport only takes place as new Grotthuss chains are formed by rotation of one or more hydroxyl groups in the chain. Thus, the overall diffusion barrier consists of a convolution of the intrinsic proton hopping barrier and the intrinsic hydroxyl rotation barrier. Our results provide a set of design rules for developing new anhydrous proton conducting membranes with even lower diffusion barriers.

Aqueous Proton Transportation in Graphene-Based Nanochannels

Graphene oxide (GO) has been unveiled to exhibit high proton conductivity in a humidified or aqueous environment, making it a promising candidate to construct proton conduction nanochannels. In this work, we systematically investigate how the confinement effect and surface chemistry influence the proton transportation behavior in graphene-based nanochannels via extensive ReaxFF MD simulations. Graphene (GE), graphane (GA), and hydroxygraphane (HG) sheets were employed to mimic the graphitic and functionalized region of GO and construct nanochannels with different interlayer distances. We find that confined water molecules are stratified and their orientation is influenced by the surface chemistry, thus impacting the distribution of protons. Surface chemistry makes the compression of the hydrogen-bond network induced by the confinement effect more variable. The hydrogen-bond network between GE slabs is crushed by extreme confinement and ultrafast proton transportation behavior mainly achieved via vehicle mechanism. Meanwhile, the hydrogen-bond network and solvation structure can be kept more complete with the existence of functional groups. The hydrogen bonds formed with surface functional groups impede the transportation of water molecules but allow more Grotthuss hopping of protons to different extents. Our work clarified the proton transportation mechanism in graphene-based nanochannels with different interlayer distances and surface chemistry and can guide the future design of proton conduction devices such as proton exchange membranes.

Dynamic Optical Visualization of Proton Transport Pathways at Water-Solid Interfaces

Probing proton transport is of vital importance for understanding cellular transport, surface catalysis and fuel cells. Conventional proton transport measurements rely on the use of electrochemical conductivity and do not allow for the direct visualization of proton transport pathways. The development of novel experimental techniques to spatiotemporally resolve proton transport is in high demand. Here, building upon the general conversion of aqueous proton flux into spatially resolved fluorescence signals, we optically visualize proton transport through nanopores and along hydrophilic interfaces. We observed that the fluorescence intensity increased at negative voltage due to lateral transport. Thanks to the temporal resolution of optical imaging, our technique further empowers the analysis of proton transport dynamics.

Mechanical hydrolysis imparts self-destruction of water molecules under steric confinement

Decoding behavioral aspects associated with the water molecules in confined spaces such as an interlayer space of two-dimensional nanosheets is key for the fundamental understanding of water-matter interactions and identifying unexpected phenomena of water molecules in chemistry and physics. Although numerous studies have been conducted on the behavior of water molecules in confined spaces, their reach stops at the properties of the planar ice-like formation, where van der Waals interactions are the predominant interactions and many questions on the confined space such as the possibility of electron exchange and excitation state remain unsettled. We used density functional theory and reactive molecular dynamics to reveal orbital overlap and induction bonding between water molecules and graphene sheets under much less pressure than graphene fractures. Our study demonstrates high amounts of charge being transferred between water and the graphene sheets, as the interlayer space becomes smaller. As a result, the inner face of the graphene nanosheets is functionalized with hydroxyl and epoxy functional groups while released hydrogen in the form of protons either stays still or traverses a short distance inside the confined space via the Grotthuss mechanism. We found signatures of a new hydrolysis mechanism in the water molecules, i.e. mechanical hydrolysis, presumably responsible for relieving water from extremely confined conditions. This phenomenon where water reacts under extreme confinement by disintegration rather than forming ice-like structures is observed for the first time, illustrating the prospect of treating ultrafine porous nanostructures as a driver for water splitting and material functionalization, potentially impacting the modern design of nanofilters, nanochannels, nano-capacitators, sensors, and so on.

Penta-Hexa-Graphene Nanoribbons: Intrinsic Magnetism and Edge Effect Induce Spin-Gapless Semiconducting and Half-Metallic Properties

Two-dimensional materials with intrinsic long-range ordered magnetic moments have drawn a lot of attention. However, for practical applications, whether or not the magnetism is stable in their nanostructures has not been revealed. Here, based on the recently proposed magnetic penta-hexa-graphene, we study the electronic and magnetic properties of its nanoribbons (named PHGNRs). The results show that the PHGNRs have intrinsic robust magnetic moments that are different from zigzag graphene nanoribbons, where the magnetic moments caused by the edge effect are vulnerable. Moreover, the magnetic ground states, namely, ferromagnetic (FM) or antiferromagnetic (AFM), can be transformed by changing the width of PHGNRs. Most interestingly, under the FM ground state, the spin-polarized electronic properties reveal that the zigzag PHGNRs transform from spin-gapless semiconductors (SGSs) to half-metals, as the width of nanoribbons increases, while all the armchair PHGNRs are magnetic semiconductors. Furthermore, by considering different edge effects caused by the residual carbon atoms on the edges, the PHGNRs can further derive different types of SGSs, as well as half-metals. Our work suggests that the PHGNRs possessing intrinsic robust magnetic moments have potential applications in the field of spintronic devices.

Design principles of ion selective nanostructured membranes for the extraction of lithium ions

It is predicted that the continuously increasing demand for the energy-critical element of lithium will soon exceed its availability, rendering it a geopolitically significant resource. The present work critically reviews recent reports on Li⁺ selective membranes. Particular emphasis has been placed on the basic principles of the materials' design for the development of membranes with nanochannels and nanopores with Li⁺ selectivity. Fundamental and practical challenges, as well as prospects for the targeted design of Li⁺ ion-selective membranes are also presented, with the goal of inspiring future critical research efforts in this scientifically and strategically important field.

Light-Driven Active Proton Transport through Photoacid- and Photobase-Doped Janus Graphene Oxide Membranes

Biological electrogenic systems use protein-based ionic pumps to move salt ions uphill across a cell membrane to accumulate an ion concentration gradient from the equilibrium physiological environment. Toward high-performance and robust artificial electric organs, attaining an antigradient ion transport mode by fully abiotic materials remains a great challenge. Herein, a light-driven proton pump transport phenomenon through a Janus graphene oxide membrane (JGOM) is reported. The JGOM is fabricated by sequential deposition of graphene oxide (GO) nanosheets modified with photobase (BOH) and photoacid (HA) molecules. Upon ultraviolet light illumination, the generation of a net protonic photocurrent through the JGOM, from the HA-GO to the BOH-GO side, is observed. The directional proton flow can thus establish a transmembrane proton concentration gradient of up to 0.8 pH units mm^-2 membrane area at a proton transport rate of 3.0 mol h^-1 m^-2 . Against a concentration gradient, antigradient proton transport can be achieved. The working principle is explained in terms of asymmetric surface charge polarization on HA-GO and BOH-GO multilayers triggered by photoisomerization reactions, and the consequent intramembrane proton concentration gradient. The implementation of membrane-scale light-harvesting 2D nanofluidic system that mimics the charge process of the bioelectric organs makes a straightforward step toward artificial electrogenic and photosynthetic applications.

Asymmetric Electrokinetic Proton Transport through 2D Nanofluidic Heterojunctions

Nanofluidic ion transport in nacre-like 2D layered materials attracts broad research interest due to subnanometer confined space and versatile surface chemistry for precisely ionic sieving and ultrafast water permeation. Currently, most of the 2D-material-based nanofluidic systems are homogeneous, and the investigations of proton conduction therein are restricted to symmetric transport behaviors. It remains a great challenge to endow the 2D nanofluidic systems with asymmetric proton transport characteristics and adaptive responsibilities. Herein, we report the asymmetric proton transport phenomena through a 2D nanofluidic heterojunction membrane under three different types of electrokinetic driving force, that is, the external electric field, the transmembrane concentration gradient, and the hydraulic pressure difference. The heterogeneous 2D nanofluidic membrane comprises of sequentially stacked negatively and positively charged graphene oxide (n-GO and p-GO) multilayers. We find that the preferential direction for proton transport is opposite under the three types of electrokinetic driving force. The preferential direction for electric-field-driven proton transport is from the n-GO multilayers to the p-GO multilayers, showing rectified behaviors. Intriguingly, when the transmembrane concentration difference and the hydraulic flow are used as the driving force, a preferred diffusive and streaming proton current is found in the reverse direction, from the p-GO to the n-GO multilayers. The asymmetric proton transport phenomena are explained in terms of asymmetric proton concentration polarization and difference in proton selectivity. The membrane-scale heterogeneous 2D nanofluidic devices with electrokinetically controlled asymmetric proton flow provide a facile and general strategy for potential applications in biomimetic energy conversion and chemical sensing.

Unraveling Anhydrous Proton Conduction in Hydroxygraphane

We predict that graphane functionalized with hydroxyl groups, hydroxygraphane, can conduct protons in the complete absence of water, as shown from density functional theory calculations. Hydroxygraphane's anhydrous intrinsic proton conductivity results from the self-assembling two-dimensional network of hydrogen bonds on its surface. We show that the proton conduction occurs through a Grotthuss-like mechanism, as protons hop between neighboring hydroxyl groups, aided by their rotation. Our calculations predict that hydroxygraphane has a direct bandgap of 3.43 eV, a phonon dispersion spectrum with no instabilities, and a 2-D Young's modulus and Poisson's ratio stiffer than those for graphane-the parent material for hydroxygraphane. Hence, hydroxygraphane has the desired electronic and mechanical properties to make it a viable candidate for a proton exchange membrane material capable of operating under anhydrous or low-humidity conditions.