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Abstract Reproducible wafer-scale growth of two-dimensional (2D) materials using the Chemical Vapor Deposition (CVD) process with precise control over their properties is challenging due to a lack of understanding of the growth mechanisms spanning over several length scales and sensitivity of the synthesis to subtle changes in growth conditions. A multiscale computational framework coupling Computational Fluid Dynamics (CFD), Phase-Field (PF), and reactive Molecular Dynamics (MD) was developed – called the CPM model – and experimentally verified. Correlation between theoretical predictions and thorough experimental measurements for a Metal-Organic CVD (MOCVD)-grown WSe₂model material revealed the full power of this computational approach. Large-area uniform 2D materials are synthesized via MOCVD, guided by computational analyses. The developed computational framework provides the foundation for guiding the synthesis of wafer-scale 2D materials with precise control over the coverage, morphology, and properties, a critical capability for fabricating electronic, optoelectronic, and quantum computing devices. 

AbstractThe exotic properties of 2D materials made them ideal candidates for applications in quantum computing, flexible electronics, and energy technologies. A major barrier to their adaptation for industrial applications is their controllable and reproducible growth at a large scale. A significant effort has been devoted to the chemical vapor deposition (CVD) growth of wafer-scale highly crystalline monolayer materials through exhaustive trial-and-error experimentations. However, major challenges remain as the final morphology and growth quality of the 2D materials may significantly change upon subtle variation in growth conditions. Here, we introduced a multiscale/multiphysics model based on coupling continuum fluid mechanics and phase-field models for CVD growth of 2D materials. It connects the macroscale experimentally controllable parameters, such as inlet velocity and temperature, and mesoscale growth parameters such as surface diffusion and deposition rates, to morphology of the as-grown 2D materials. We considered WSe₂as our model material and established a relationship between the macroscale growth parameters and the growth coverage. Our model can guide the CVD growth of monolayer materials and paves the way to their synthesis-by-design. Graphic abstract 

Abstract The successful discovery and isolation of graphene in 2004, and the subsequent synthesis of layered semiconductors and heterostructures beyond graphene have led to the exploding field of two-dimensional (2D) materials that explore their growth, new atomic-scale physics, and potential device applications. This review aims to provide an overview of theoretical, computational, and machine learning methods and tools at multiple length and time scales, and discuss how they can be utilized to assist/guide the design and synthesis of 2D materials beyond graphene. We focus on three methods at different length and time scales as follows: (i) nanoscale atomistic simulations including density functional theory (DFT) calculations and molecular dynamics simulations employing empirical and reactive interatomic potentials; (ii) mesoscale methods such as phase-field method; and (iii) macroscale continuum approaches by coupling thermal and chemical transport equations. We discuss how machine learning can be combined with computation and experiments to understand the correlations between structures and properties of 2D materials, and to guide the discovery of new 2D materials. We will also provide an outlook for the applications of computational approaches to 2D materials synthesis and growth in general. 

Transport and migration of elongated, deformable micrometer-sized particles around circular obstacles is investigated. This study is specifically motivated by the need to understand the movement and environmental impact of microplastic fibers (microfibers), particularly as contaminants in groundwater resources. Through microscale modeling, we examine how deformation, motion, and localization of microfibers are affected by medium morphology and local flow inhomogeneities. Extensive numerical simulations are performed to study the complex fluid–solid interactions taking place and to reveal the connection between microfiber transport dynamics and the arrangement of periodic and random obstacles. The trajectories of microfibers, as well as hotspots of their accumulation within both periodic and random structured media, are studied. We show that a random structured medium gives rise to anomalous transport features, such as breakthrough long tailing. A generalized probabilistic framework based on continuous time random walk is utilized to describe the upscaled transport model and capture the memory effects as well as the non-Fickian transport features. The upscaled model parameters, including effective velocity, dispersion coefficients, and transition time distributions, are extracted from direct numerical simulations. 

This study investigates the stability of Inconel–Cu Multimetallic Layered Composites (MMLCs) in nuclear reactor applications using Molecular Dynamics simulations. The focus is on understanding the underlying mechanisms governing the properties of MMLCs for advanced nuclear reactors, specifically, the mechanochemistry of the interface between Inconel and copper alloys. The selection of Inconel–Cu MMLCs is primarily due to copper’s superior thermal conductivity, enhancing heat management within reactors by preventing hotspots and ensuring uniform temperature distribution. This research examines Incoloy 800H and two Inconel variants (718 and 625), assessing their stability at 1000 K after exposure to 10 keV collision cascades up to 0.12 dpa. Notable findings include defect clustering on the {1 2 0} family of planes of Inconel and Cu, with Stacking Faults and Lomer–Cottrell locks on the Inconel side.