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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.
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This content will become publicly available on February 1, 2026
Multiscale-multiphysics predictive modeling of chemical vapor deposition processes for carbon nanotube synthesis
The synthesis of carbon nanotubes has attracted considerable interest due to their unique physical and structural properties. Despite notable experimental advancements, particularly in chemical vapor deposition (CVD) techniques, a significant gap remains in developing comprehensive mechanistic models that correlate nanotube growth dynamics with gas-phase composition. The CVD involves a complex interplay of multiscale phenomena, including hydrocarbon transport within the reactor and surface reactions on catalyst nanoparticles, collectively contributing to nucleation and growth. This paper introduces a computational modeling framework that integrates these phenomena by leveraging density functional theory energy data, microkinetic modeling, and computational fluid dynamics. The proposed approach addresses the challenges inherent in this multiscale-multiphysics problem, providing insights into nanotube growth as a function of gas composition and transport, temperature, and catalyst properties. The simulation results show strong agreement with experimental trends, highlighting the significance of gas-phase reactions in a mixed hydrocarbon feedstock and the effects of catalyst deactivation.
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- Award ID(s):
- 2414683
- PAR ID:
- 10650002
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Chemical Engineering Science
- Volume:
- 305
- Issue:
- C
- ISSN:
- 0009-2509
- Page Range / eLocation ID:
- 121137
- Subject(s) / Keyword(s):
- Multiscale modeling, Predictive modeling, Computational fluid dynamics, Chemical vapor deposition, Carbon nanotube synthesis
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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