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Creators/Authors contains: "Ji, Yanzhou"

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  1. Free, publicly-accessible full text available September 1, 2023
  2. Abstract

    The 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-fieldmore »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.

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  3. Abstract

    Precipitation strengthening of alloys by the formation of secondary particles (precipitates) in the matrix is one of the techniques used for increasing the mechanical strength of metals. Understanding the precipitation kinetics such as nucleation, growth, and coarsening of these precipitates is critical for evaluating their hardening effects and improving the yield strength of the alloy during heat treatment. To optimize the heat treatment strategy and accelerate alloy design, predicting precipitate hardening effects via numerical methods is a promising complement to trial-and-error-based experiments and the physics-based phase-field method stands out with the significant potential to accurately predict the precipitate morphologymore »and kinetics. In this study, we present a phase-field model that captures the nucleation, growth, and coarsening kinetics of precipitates during isothermal heat treatment conditions. Thermodynamic data, diffusion coefficients, and misfit strain data from experimental or lower length-scale calculations are used as input parameters for the phase-field model. Classical nucleation theory is implemented to capture the nucleation kinetics. As a case study, we apply the model to investigate γ″ precipitation kinetics in Inconel 625. The simulated mean particle length, aspect ratio, and volume fraction evolution are in agreement with experimental data for simulations at 600 °C and 650 °C during isothermal heat treatment. Utilizing the meso-scale results from the phase-field simulations as input parameters to a macro-scale coherency strengthening model, the evolution of the yield strength during heat treatment was predicted. In a broader context, we believe the current study can provide practical guidance for applying the phase-field approach as a link in the multiscale modeling of material properties.

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  4. 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 includingmore »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.

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