Although nanostructures have been considered for industrial applications, the current production and yield rate remains rather low, hovering in 10-20%. An effective process planning and design for nanomanufacturing is considered necessary to improve quality of nanostructures and consequentially the yield rate. Key to quality assurance in nanomanufacturing is to derive desired geometric features, which are determinant to physical and chemical properties (e.g., Young's modulus), of nanomaterials, such as carbon nanotubes (CNTs). While atomistic simulation models are widely used to study those nano-scale phenomena and consequentially the process design, they suffer from an overwhelming computational overhead. In this paper, we present a meso-scale fast Monte Carlo (MC) simulation approach to investigate large-scale CNT synthesis in chemical vapor deposition, and identify key parameters (here, catalyst diameter, temperature, and CNT length) to maximize the Young's modulus.
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