Discovery of Catalysts and Mechanisms for Sustainable Energy Conversion
The electrochemical reduction of carbon dioxide (CO2) to hydrocarbons is a potential option to achieve carbon neutrality. Although copper (Cu) shows the highest activity for the CO2 reduction reaction (CO2RR) to hydrocarbons among metals, high reaction overpotentials and significant H2 production limit its use. We investigate single-atom alloys (SAAs) of ten metals (Ag, Au, Fe, Ir, Ni, Pd, Pt, Rh, Ru, Al) on Cu(111), which is the most-favored facet on Cu for methane production, using density functional theory. We examined the dopants’ ability to lower the free energy of the elementary reaction, *CO to *CHO, which is the potential-determining step (PDS). Out of the SAAs studied, only Al-doped Cu demonstrated a lowering of the PDS free energy. Additionally, weaker adsorption energies of *CO and *H on Al-Cu(111) suggest a preference for C1 hydrocarbons and inhibition of H2 evolution. Finally, activation barrier calculations for the PDS on Al-Cu(111) involving an explicitly hydrated proton indicated better intrinsic activity for C1 hydrocarbons compared to pure Cu(111). We also confirmed the stability of Al-Cu SAA compared to small Al clusters. Through a comprehensive study of both thermodynamics and kinetics, our study presents Al-Cu SAA as a promising catalyst for CO2 electroreduction to C1 hydrocarbons. | See ChemCatChem 2023, 15 (14), e202300188.
Predicting the Shapes of Nanopores and Defects in Two-Dimensional Materials
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. | See J. Chem. Inf. Model. 2023, 63, 3, 870–881.
Machine Learning for Materials Systems
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. | See J. Chem. Phys. 2022, 156, 204703.
Development of Force Field Models for Materials
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. | See J. Phys. Chem. Lett., 2018, 9, pp. 1584-1591.
Wetting, Friction, and Other Interfacial Phenomena at Nanomaterial 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. | See Nano Lett. 2021, 21, 19, 8008–8016.
Kinetic and Mechanistic Models for Chemical Vapor Deposition Growth of Two-Dimensional Materials
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 | See iScience 2022, 25, 103832.