Strongly facet-dependent activity of iron-doped ß-nickel oxyhydroxide for the oxygen evolution reaction.

Iron (Fe)-doped ß-nickel oxyhydroxide (ß-NiOOH) is a highly active, noble-metal-free electrocatalyst for the oxygen evolution reaction (OER), with the latter being the bottleneck in electrochemical water splitting for sustainable hydrogen production. The mechanisms underlying how the Fe dopant modulates this host material's water electro-oxidation activity are still not entirely clear. Here, we combine hybrid density functional theory (DFT) and Hubbard-corrected DFT to investigate the OER activity of the most thermodynamically favorable (and therefore, expected to be the majority) crystallographic facets of ß-NiOOH, namely (0001) and (101̄0). By considering active sites involving both oxidation and reduction of the transition-metal active center during the redox cycle on these two different facets, we show that six-fold-lattice-coordinated Fe in ß-NiOOH is redox inactive towards both oxidation and reduction while five-fold-lattice-coordinated Fe in ß-NiOOH does exhibit redox activity. However, the determined redox activity of Fe (or lack of it) is not indicative of good (or bad) performance as a dopant on these two facets. Three of the four active sites investigated (oxo and hydroxo sites on (0001) and a hydrated site on (101̄0)) exhibit only a marginal (<0.1 V) decrease or increase in the thermodynamic overpotential upon doping with Fe. Only one of the redox-active sites investigated, the hydroxo site on (101̄0), exhibits a large attenuation in the thermodynamic overpotential upon doping (to ∼0.52 V from 0.86 V), although the doped overpotential is larger than that observed experimentally for Fe-doped NiOOH. Thus, although pure ß-NiOOH facets containing four-, five-, or six-fold lattice-coordinated Ni sites have roughly equal OER activities, yielding similar OER onset potentials (shown in A. Govind Rajan, J. M. P. Martirez and E. A. Carter, J. Am. Chem. Soc., 2020, 142, 3600-3612), only those facets containing four-fold lattice-coordinated Fe (e.g., as shown in J. M. P. Martirez and E. A. Carter, J. Am. Chem. Soc., 2019, 141, 693-705) would be active under analogous conditions for the Fe-doped material. It follows that, while undoped ß-NiOOH demonstrates a roughly facet-independent oxygen evolution activity, the activity of Fe-doped ß-NiOOH strongly depends on the crystallographic facet. Our study further motivates the investigation of strategies for the selective growth of facets with low iron coordination number to enhance the water splitting activity of Fe-doped ß-NiOOH.

Enumerating Stable Nanopores in Graphene and Their Geometrical Properties Using the Combinatorics of Hexagonal Lattices.

Nanopores in two-dimensional (2D) materials, including graphene, can be used for a variety of applications, such as gas separations, water desalination, and DNA sequencing. So far, however, all plausible isomeric shapes of graphene nanopores have not been enumerated. Instead, a probabilistic approach has been followed to predict nanopore shapes in 2D materials, due to the exponential increase in the number of nanopores as the size of the vacancy increases. For example, there are 12 possible isomers when N = 6 atoms are removed, a number that theoretically increases to 11.7 million when N = 20 atoms are removed from the graphene lattice. In this regard, the development of a smaller, exhaustive data set of stable nanopore shapes can help future experimental and theoretical studies focused on using nanoporous 2D materials in various applications. In this work, we use the theory of 2D triangular "lattice animals" to create a library of all stable graphene nanopore shapes based on a modification of a well-known algorithm in the mathematical combinatorics of polyforms known as Redelmeier's algorithm. We show that there exists a correspondence between graphene nanopores and triangular polyforms (called polyiamonds) as well as hexagonal polyforms (called polyhexes). We develop the concept of a polyiamond ID to identify unique nanopore isomers. We also use concepts from polyiamond and polyhex geometries to eliminate unstable nanopores containing dangling atoms, bonds, and moieties. We verify using density functional theory calculations that such pores are indeed unstable. The exclusion of these unstable nanopores leads to a remarkable reduction in the possible nanopores from 11.7 million for N = 20 to only 0.184 million nanopores, thereby indicating that the number of stable nanopores is almost 2 orders of magnitude lower and is much more tractable. Not only that, by extracting the polyhex outline, our algorithm allows searching for nanopores with dimensions and shape factors in a specified range, thus aiding the design of the geometrical properties of nanopores for specific applications. We also provide the coordinate files of the stable nanopores as a library to facilitate future theoretical studies of these nanopores.

Incorporating ion-specific van der Waals and soft repulsive interactions in the Poisson-Boltzmann theory of electrical double layers.

Electrical double layers (EDLs) arise when an electrolyte is in contact with a charged surface, and are encountered in several application areas including batteries, supercapacitors, electrocatalytic reactors, and colloids. Over the last century, the development of Poisson-Boltzmann (PB) models and their modified versions have provided significant physical insight into the structure and dynamics of the EDL. Incorporation of physics such as finite-ion-size effects, dielectric decrement, and ion-ion correlations has made such models increasingly accurate when compared to more computationally expensive approaches such as molecular simulations and classical density functional theory. However, a prominent knowledge gap has been the exclusion of van der Waals (vdW) and soft repulsive interactions in modified PB models. Although short-ranged as compared to electrostatic interactions, we show here that vdW and soft repulsive interactions can play an important role in determining the structure of the EDL via the formation of a Stern layer and in modulating the differential capacitance of an electrode in an electrolyte. To this end, we incorporate ion-ion and wall-ion vdW attraction and soft repulsion via a 12-6 Lennard-Jones (LJ) potential, resulting in a modified PB-LJ approach. The wall-ion LJ interactions were found to have a significant effect on the electrical potential and concentration profiles, especially close to the wall. However, ion-ion LJ interactions do not affect the EDL structure at low bulk ion concentrations (<1 M). We also derive dimensionless numbers to quantify the impact of ion-ion and wall-ion LJ interactions on the EDL. Furthermore, in the pursuit of capturing ion-specific effects, we apply our model by considering various ions such as Na, K+, Mg2+, Cl-, and SO42-. We observe how varying parameters such as the electrolyte concentration and electrode potential affect the structure of the EDL due to the competition between ion-specific LJ and electrostatic interactions. Lastly, we show that the inclusion of vdW and soft repulsion interactions, as well as hydration effects, leads to a better qualitative agreement of the PB models with experimental double-layer differential capacitance data. Overall, the modified PB-LJ approach presented herein will lead to more accurate theoretical descriptions of EDLs in various application areas.

Surface Roughness Explains the Observed Water Contact Angle and Slip Length on 2D Hexagonal Boron Nitride.

Hexagonal boron nitride (hBN) is a two-dimensional (2D) material that is currently being explored in a number of applications, such as atomically thin coatings, water desalination, and biological sensors. In many of these applications, the hBN surface comes into intimate contact with water. In this work, we investigate the wetting and frictional behavior of realistic 2D hBN surfaces with atomic-scale defects and roughness. We combine density functional theory calculations of the charge distribution inside hBN with free energy calculations using molecular dynamics simulations of the hBN-water interface. We find that the presence of surface roughness, but not that of vacancy defects, leads to remarkable agreement with the experimentally observed water contact angle of 66° on freshly synthesized, uncontaminated hBN. Not only that, the inclusion of surface roughness predicts with exceptional accuracy the experimental water slip length of ∼1 nm on hBN. Our results underscore the importance of considering realistic models of 2D materials with surface roughness while modeling nanomaterial-water interfaces in molecular simulations.

Tailoring nanoporous graphene via machine learning: Predicting probabilities and formation times of arbitrary nanopore shapes.

Nanopores in graphene, a 2D material, are currently being explored for various applications, such as gas separation, water desalination, and DNA sequencing. The shapes and sizes of nanopores play a major role in determining the performance of devices made out of graphene. However, given an arbitrary nanopore shape, anticipating its creation probability and formation time is a challenging inverse problem, solving which could help develop theoretical models for nanoporous graphene and guide experiments in tailoring pore sizes/shapes. In this work, we develop a machine learning framework to predict these target variables, i.e., formation probabilities and times, based on data generated using kinetic Monte Carlo simulations and chemical graph theory. Thereby, we enable the rapid quantification of the ease of formation of a given nanopore shape in graphene via silicon-catalyzed electron-beam etching and provide an experimental handle to realize it, in practice. We use structural features such as the number of carbon atoms removed, the number of edge atoms, the diameter of the nanopore, and its shape factor, which can be readily extracted from the nanopore shape. We show that the trained models can accurately predict nanopore probabilities and formation times with R2 values on the test set of 0.97 and 0.95, respectively. Not only that, we obtain physical insight into the working of the model and discuss the role played by the various structural features in modulating nanopore formation. Overall, our work provides a solid foundation for experimental studies to manipulate nanopore sizes/shapes and for theoretical studies to consider realistic structures of nanopores in graphene.

Modulating Water Slip Using Atomic-Scale Defects: Friction on Realistic Hexagonal Boron Nitride Surfaces.

Atomic-scale defects are ubiquitous in nanomaterials, yet their role in modulating fluid flow is inadequately understood. Hexagonal boron nitride (hBN) is an important two-dimensional material with applications in desalination and osmotic power. Although pristine hBN offers higher friction to the flow of water than graphene, we show here that certain defects can enhance water slippage on hBN. Using classical molecular dynamics simulations assisted by quantum-mechanical density functional theory, we compute the friction coefficient of water on hBN containing various vacancies (B, N, BN, B2N, and B3N) and the Stone-Wales defect. By investigating two defect concentrations, we obtain friction coefficients ranging from 0.4 to 2.6 times that of pristine hBN, leading to a maximum water slip length of 18.1 nm on hBN with a N vacancy or a Stone-Wales defect. Our work informs the use of defects to tune water flow and reveals defective hBN as an alternative high-slip surface to graphene.

Facet-Independent Oxygen Evolution Activity of Pure ß-NiOOH: Different Chemistries Leading to Similar Overpotentials.

ß-Nickel oxyhydroxide (ß-NiOOH) is a promising electrocatalyst for the oxygen evolution reaction (OER), which is the more difficult half-reaction involved in water splitting. In this study, we revisit the OER activities of the two most abundant crystallographic facets of pristine ß-NiOOH, the (0001) and (1010) facets, which expose 6-fold-lattice-oxygen-coordinated and 5-fold-lattice-oxygen-coordinated Ni sites, respectively. To this end, we model various active sites on these two facets using hybrid density functional theory, which includes a fraction of the exact nonlocal Fock exchange in the electronic description of the system. By evaluating thermodynamic OER overpotentials, we show that the two active sites considered on each crystallographic facet demonstrate OER activities remarkably different from each other. However, the lowest OER overpotentials calculated for the two facets were found to be similar to each other and comparable to the overpotential for the 4-fold-lattice-oxygen-coordinated Ni site on the (1211) facet of ß-NiOOH previously examined in J. Am. Chem. Soc. 2019 , 141 , 1 , 693 - 705 . This finding shows that all of the low-index facets investigated so far could be responsible for the experimentally observed OER activity of pristine ß-NiOOH. However, the lowest overpotential active sites on these three crystallographic facets operate via different mechanisms, underscoring the importance of considering multiple OER pathways and intermediates on each crystallographic facet of a potential electrocatalyst. Specifically, our work demonstrates that consideration of previously overlooked active sites, transition-metal-ion oxidation states, reaction intermediates, and lattice-oxygen-stabilization are critical to reveal the lowest overpotential OER pathways on pristine ß-NiOOH.

Addressing the isomer cataloguing problem for nanopores in two-dimensional materials.

The presence of extended defects or nanopores in two-dimensional (2D) materials can change the electronic, magnetic and barrier membrane properties of the materials. However, the large number of possible lattice isomers of nanopores makes their quantitative study a seemingly intractable problem, confounding the interpretation of experimental and simulated data. Here we formulate a solution to this isomer cataloguing problem (ICP), combining electronic-structure calculations, kinetic Monte Carlo simulations, and chemical graph theory, to generate a catalogue of unique, most-probable isomers of 2D lattice nanopores. The results demonstrate remarkable agreement with precise nanopore shapes observed experimentally in graphene and show that the thermodynamic stability of a nanopore is distinct from its kinetic stability. Triangular nanopores prevalent in hexagonal boron nitride are also predicted, extending this approach to other 2D lattices. The proposed method should accelerate the application of nanoporous 2D materials by establishing specific links between experiment and theory/simulations, and by providing a much-needed connection between molecular design and fabrication.

Ionic Layering and Overcharging in Electrical Double Layers in a Poisson-Boltzmann Model.

Electrical double layers (EDLs) play a significant role in a broad range of physical phenomena related to colloidal stability, diffuse-charge dynamics, electrokinetics, and energy storage applications. Recently, it has been suggested that for large ion sizes or multivalent electrolytes, ions can arrange in a layered structure inside the EDLs. However, the widely used Poisson-Boltzmann models for EDLs are unable to capture the details of ion concentration oscillations and the effect of electrolyte valence on such oscillations. Here, by treating a pair of ions as hard spheres below the distance of closest approach and as point charges otherwise, we are able to predict ionic layering without any additional parameters or boundary conditions while still being compatible with the Poisson-Boltzmann framework. Depending on the combination of ion valence, size, and concentration, our model reveals a structured EDL with spatially oscillating ion concentrations. We report the dependence of critical ion concentration, i.e., the ion concentration above which the oscillations are observed, on the counter-ion valence and the ion size. More importantly, our model displays quantitative agreement with the results of computationally intensive models of the EDL. Finally, we analyze the nonequilibrium problem of EDL charging and demonstrate that ionic layering increases the total charge storage capacity and the charging timescale.

Liquids with Lower Wettability Can Exhibit Higher Friction on Hexagonal Boron Nitride: The Intriguing Role of Solid-Liquid Electrostatic Interactions.

We investigate the wetting and frictional behavior of polar (water and ethylene glycol) and nonpolar (diiodomethane) liquids on the basal plane of hexagonal boron nitride (hBN) using molecular dynamics simulations. Our results for the wettability of water on the hBN basal plane (contact angle 81°) are in qualitative agreement with the experimentally deduced mild hydrophilicity of the hBN basal plane (contact angle 66°). We find that water exhibits the lowest wettability, as quantified by the highest contact angle, but the highest friction coefficient of (1.9 ± 0.4) × 105 N-s/m3 on the hBN basal plane among the three liquids considered. This intriguing finding is explained in terms of the competition between dispersion and electrostatic interactions operating between the hBN basal plane and the three liquids. We find that electrostatic interactions do not affect the wetting behavior appreciably, as quantified by a less than 3° change in the respective contact angles of the three liquids considered. On the other hand, electrostatic interactions are found to increase the friction coefficients of the three liquids in contact with hBN to different extents, indicating that despite the increased friction of water on hBN, relative to that on graphene, nonpolar liquids may exhibit similar friction coefficients on hBN and graphene. Our findings reveal that the increase in the friction coefficient, upon incorporation of solid-liquid electrostatic interactions, is brought about by a greater increase in the solid-liquid mean-squared total lateral force, as compared to a smaller reduction in the decorrelation time of the solid-liquid force.

Current and future directions in electron transfer chemistry of graphene.

The participation of graphene in electron transfer chemistry, where an electron is transferred between graphene and other species, encompasses many important processes that have shown versatility and potential for use in important applications. Examples of these processes range from covalent functionalization of graphene to modify its properties and incorporate different functional groups, to electrochemical reactions and selective etching. In this paper, we review recent developments in these areas of the electron transfer chemistry of graphene. We address recent progress on controlling covalent functionalization through chemical and physical methods, and how carefully functionalized graphene can be incorporated into composite materials with enhanced properties. We review the selective etching of graphene to form edges and nanopores, which have unique chemical and physical properties. Nanoporous graphene is promising for new membrane and filtration applications. We also discuss the electrochemistry of graphene grown by chemical vapour deposition in two-dimensional and three-dimensional geometries, which enables large surface areas and control over the distribution and concentration of edge and basal plane sites. We discuss the potential for each of these areas to have an impact in future applications such as filtration membranes, electronic devices, electrochemical electrodes, composite materials, and chemical sensors.

Predicting Quantum-Mechanical Partial Charges in Arbitrarily Long Boron Nitride Nanotubes to Accurately Simulate Nanoscale Water Transport.

Single-walled boron nitride nanotubes (BNNTs) have been explored for various applications, ranging from water desalination to osmotic power harvesting. However, no simulation work so far has modeled the changes in the partial charge distribution when a flat sheet is rolled into a tube, hindering the ability to perform accurate molecular dynamics (MD) simulations of water flow through BNNTs. To address this knowledge gap, we employ electronic density functional theory (DFT) calculations to precisely estimate quantum-mechanically derived partial charges on boron (B) and nitrogen (N) atoms in BNNTs of varying lengths and diameters. We observe a spatially varying charge distribution inside both armchair and zigzag nanotubes of finite lengths. Performing DFT calculations for longer BNNTs is computationally intractable, even with state-of-the-art computing resources. To solve this issue, we devise a charge assignment scheme to predict partial charges for longer BNNTs using DFT data for shorter nanotubes, thus overcoming the need to perform more expensive DFT calculations. We show that these charges reproduce the electrostatic potential predicted from first-principles simulations. Subsequently, we carried out MD simulations to predict the effect of the charge distribution inside BNNTs on water flow enhancement via them. We find that using uniform charges leads to an underprediction in flow enhancement, as compared to using quantum-mechanical charges for both armchair and zigzag BNNTs. We also incorporate atomic vibrations into our simulations and show that these vibrations lead to a reduction in the water flow through aperiodic BNNTs. Our work demonstrates the requirement of a quantum-mechanical charge assignment scheme for BNNTs and evolves a framework to assign charges to nanotubes of arbitrary length, thus allowing realistic MD simulations of long BNNTs using accurate partial charges.

Chemical vapor deposition of 2D materials: A review of modeling, simulation, and machine learning studies.

Chemical vapor deposition (CVD) is extensively used to produce large-area two-dimensional (2D) materials. Current research is aimed at understanding mechanisms underlying the nucleation and growth of various 2D materials, such as graphene, hexagonal boron nitride (hBN), and transition metal dichalcogenides (e.g., MoS2/WSe2). Herein, we survey the vast literature regarding modeling and simulation of the CVD growth of 2D materials and their heterostructures. We also focus on newer materials, such as silicene, phosphorene, and borophene. We discuss how density functional theory, kinetic Monte Carlo, and reactive molecular dynamics simulations can shed light on the thermodynamics and kinetics of vapor-phase synthesis. We explain how machine learning can be used to develop insights into growth mechanisms and outcomes, as well as outline the open knowledge gaps in the literature. Our work provides consolidated theoretical insights into the CVD growth of 2D materials and presents opportunities for further understanding and improving such processes.

How Grain Boundaries and Interfacial Electrostatic Interactions Modulate Water Desalination via Nanoporous Hexagonal Boron Nitride.

To fulfill the increasing demand for drinking water, researchers are currently exploring nanoporous two-dimensional materials, such as hexagonal boron nitride (hBN), as potential desalination membranes. A prominent, yet unsolved challenge is to understand how such membranes will perform in the presence of defects or surface charge in the membrane material. In this work, we study the effect of grain boundaries (GBs) and interfacial electrostatic interactions on the desalination performance of bicrystalline nanoporous hBN using classical molecular dynamics simulations supported by quantum-mechanical density functional theory (DFT) calculations. We investigate three different nanoporous bicrystalline hBN configurations, with symmetric tilt GBs having misorientation angles of 13.2, 21.8, and 32.2°. Using lattice dynamics calculations, we find that grain boundaries alter the areas and shapes of nanopores in bicrystalline hBN, as compared to the nanopores in monocrystalline hBN. We observe that, although bicrystalline nanoporous hBN with a misorientation angle of 13.2° shows an improved water flow rate by ∼30%, it demonstrates reduced Na+ ion rejection by ∼6%, as compared to monocrystalline hBN. We also uncover the role of the nanopore shape in water desalination, finding that more elongated pores with smaller sizes (in 21.8- and 32.2°-misoriented bicrystalline hBN) can match water permeation through less elongated pores of slightly larger sizes, with a concomitant ∼3-4% decrease in Na+ rejection. Simulations also predict that the water flow rate is significantly affected by interfacial electrostatic interactions. Indeed, the water flow rate is the highest when altered partial charges on B and N atoms were determined using DFT calculations, as compared to when no partial charges or bulk partial charges (i.e., charged hBN) were considered. Overall, our work on water/ion transport through nanopores in bicrystalline hBN indicates that the presence of GBs and surface charge can lead, respectively, to a decrease in the ion rejection and water permeation performance of hBN membranes.

Predicting Gas Separation through Graphene Nanopore Ensembles with Realistic Pore Size Distributions.

The development of nanoporous single-layer graphene membranes for gas separation has prompted increasing theoretical investigations of gas transport through graphene nanopores. However, computer simulations and theories that predict gas permeances through individual graphene nanopores are not suitable to describe experimental results, because a realistic graphene membrane contains a large number of nanopores of diverse sizes and shapes. With this need in mind, here, we generate nanopore ensembles in silico by etching carbon atoms away from pristine graphene with different etching times, using a kinetic Monte Carlo algorithm developed by our group for the isomer cataloging problem of graphene nanopores. The permeances of H2, CO2, and CH4 through each nanopore in the ensembles are predicted using transition state theory based on classical all-atomistic force fields. Our findings show that the total gas permeance through a nanopore ensemble is dominated by a small fraction of large nanopores with low energy barriers of pore crossing. We also quantitatively predict the increase of the gas permeances and the decrease of the selectivities between the gases as functions of the etching time of graphene. Furthermore, by fitting the theoretically predicted selectivities to the experimental ones reported in the literature, we show that nanopores in graphene effectively expand as the temperature of permeation measurement increases. We propose that this nanopore "expansion" is due to the desorption of contaminants that partially clog the graphene nanopores. In general, our study highlights the effects of the pore size and shape distributions of a graphene nanopore ensemble on its gas separation properties and calls into attention the potential effect of pore-clogging contamination in experiments.

Fundamental scaling laws for the direct-write chemical vapor deposition of nanoscale features: modeling mass transport around a translating nanonozzle.

The nanometer placement of nanomaterials, such as nanoribbons and nanotubes, at a specific pitch and orientation on a surface, remains an unsolved fundamental problem in nanotechnology. In this work, we introduce and analyze the concept of a direct-write chemical vapor deposition (CVD) system that enables the in-place synthesis of such structures with control over orientation and characteristic features. A nanometer scale pore or conduit, called the nanonozzle, delivers precursor gases for CVD locally on a substrate, with spatial translation of either the nozzle or the substrate to enable a novel direct write (DW) tool. We analyze the nozzle under conditions where it delivers reactants to a substrate while translating at a constant velocity over the surface at a fixed reaction temperature. We formulate and solve a multi-phase three-dimensional reaction and diffusion model of the direct-write operation, and evaluate specific analytically-solvable limits to determine the allowable operating conditions, including pore dimensions, reactant flow rates, and nozzle translation speed. A Buckingham Π analysis identifies six dimensionless quantities crucial for the design and operation of the direct-write synthesis process. Importantly, we derive and validate what we call the ribbon extension inequality that brackets the allowable nozzle velocity relative to the CVD growth rate - a key constraint to enabling direct-write operation. Lastly, we include a practical analysis using attainable values towards the experimental design of such a system, building the nozzle around a commercially available near-field scanning optical microscopy (NSOM) tip as a feasible example.

Multi-scale approach for modeling stability, aggregation, and network formation of nanoparticles suspended in aqueous solutions.

Suspensions of nanoparticles (NPs) in aqueous solutions hold promise in many research fields, including energy applications, water desalination, and nanomedicine. The ability to tune NP interactions, and thereby to modulate the NP self-assembly process, holds the key to rationally synthesize NP suspensions. However, traditional models obtained by coupling the DLVO (Derjaguin, Landau, Verwey, and Overbeek) theory of NP interactions, or suitable modifications of it, with the kinetic theory of colloidal aggregation are inadequate to precisely model NP self-assembly because they neglect hydration forces and discrete-size effects predominant at the nanoscale. By synergistically blending molecular dynamics and stochastic dynamics simulations with continuum theories, we develop a multi-scale (MS) model, which is able to accurately predict suspension stability, timescales for NP aggregation, and macroscopic properties (e.g., the thermal conductivity) of bare and surfactant-coated NP suspensions, in good agreement with the experimental data. Our results enable the formulation of design rules for engineering NP aqueous suspensions in a wide range of applications.

Ab Initio Molecular Dynamics and Lattice Dynamics-Based Force Field for Modeling Hexagonal Boron Nitride in Mechanical and Interfacial Applications.

Hexagonal boron nitride (hBN) is an up-and-coming two-dimensional material, with applications in electronic devices, tribology, and separation membranes. Herein, we utilize density-functional-theory-based ab initio molecular dynamics (MD) simulations and lattice dynamics calculations to develop a classical force field (FF) for modeling hBN. The FF predicts the crystal structure, elastic constants, and phonon dispersion relation of hBN with good accuracy and exhibits remarkable agreement with the interlayer binding energy predicted by random phase approximation calculations. We demonstrate the importance of including Coulombic interactions but excluding 1-4 intrasheet interactions to obtain the correct phonon dispersion relation. We find that improper dihedrals do not modify the bulk mechanical properties and the extent of thermal vibrations in hBN, although they impact its flexural rigidity. Combining the FF with the accurate TIP4P/Ice water model yields excellent agreement with interaction energies predicted by quantum Monte Carlo calculations. Our FF should enable an accurate description of hBN interfaces in classical MD simulations.

Understanding the colloidal dispersion stability of 1D and 2D materials: Perspectives from molecular simulations and theoretical modeling.

The colloidal dispersion stability of 1D and 2D materials in the liquid phase is critical for scalable nano-manufacturing, chemical modification, composites production, and deployment as conductive inks or nanofluids. Here, we review recent computational and theoretical studies carried out by our group to model the dispersion stability of 1D and 2D materials, including single-walled carbon nanotubes, graphene, and graphene oxide in aqueous surfactant solutions or organic solvents. All-atomistic (AA) molecular dynamics (MD) simulations can probe the molecular level details of the adsorption morphology of surfactants and solvents around these materials, as well as quantify the interaction energy between the nanomaterials mediated by surfactants or solvents. Utilizing concepts from reaction kinetics and diffusion, one can directly predict the rate constants for the aggregation kinetics and dispersion life times using MD outputs. Furthermore, the use of coarse-grained (CG) MD simulations allows quantitative prediction of surfactant adsorption isotherms. Combined with the Poisson-Boltzmann equation, the Langmuir isotherm, and the DLVO theory, one can directly use CGMD outputs to: (i) predict electrostatic potentials around the nanomaterial, (ii) correlate surfactant surface coverages with surfactant concentrations in the bulk dispersion medium, and (iii) determine energy barriers against coagulation. Finally, we discuss challenges associated with studying emerging 2D materials, such as, hexagonal boron nitride (h-BN), phosphorene, and transition metal dichalcogenides (TMDCs), including molybdenum disulfide (MoS2). An outlook is provided to address these challenges with plans to develop force-field parameters for MD simulations to enable predictive modeling of emerging 2D materials in the liquid phase.

Mechanism and Prediction of Gas Permeation through Sub-Nanometer Graphene Pores: Comparison of Theory and Simulation.

Due to its atomic thickness, porous graphene with sub-nanometer pore sizes constitutes a promising candidate for gas separation membranes that exhibit ultrahigh permeances. While graphene pores can greatly facilitate gas mixture separation, there is currently no validated analytical framework with which one can predict gas permeation through a given graphene pore. In this work, we simulate the permeation of adsorptive gases, such as CO2 and CH4, through sub-nanometer graphene pores using molecular dynamics simulations. We show that gas permeation can typically be decoupled into two steps: (1) adsorption of gas molecules to the pore mouth and (2) translocation of gas molecules from the pore mouth on one side of the graphene membrane to the pore mouth on the other side. We find that the translocation rate coefficient can be expressed using an Arrhenius-type equation, where the energy barrier and the pre-exponential factor can be theoretically predicted using the transition state theory for classical barrier crossing events. We propose a relation between the pre-exponential factor and the entropy penalty of a gas molecule crossing the pore. Furthermore, on the basis of the theory, we propose an efficient algorithm to calculate CO2 and CH4 permeances per pore for sub-nanometer graphene pores of any shape. For the CO2/CH4 mixture, the graphene nanopores exhibit a trade-off between the CO2 permeance and the CO2/CH4 separation factor. This upper bound on a Robeson plot of selectivity versus permeance for a given pore density is predicted and described by the theory. Pores with CO2/CH4 separation factors higher than 102 have CO2 permeances per pore lower than 10-22 mol s-1 Pa-1, and pores with separation factors of ∼10 have CO2 permeances per pore between 10-22 and 10-21 mol s-1 Pa-1. Finally, we show that a pore density of 1014 m-2 is required for a porous graphene membrane to exceed the permeance-selectivity upper bound of polymeric materials. Moreover, we show that a higher pore density can potentially further boost the permeation performance of a porous graphene membrane above all existing membranes. Our findings provide insights into the potential and the limitations of porous graphene membranes for gas separation and provide an efficient methodology for screening nanopore configurations and sizes for the efficient separation of desired gas mixtures.