重复性
再现性
计算流体力学
石墨烯
化学气相沉积
过程(计算)
材料科学
沉积(地质)
工艺工程
纳米技术
黑匣子
化学反应器
工具箱
计算机科学
核工程
机械
机械工程
化学工程
化学
物理
工程类
色谱法
操作系统
生物
沉积物
古生物学
人工智能
作者
Shahana Chatterjee,Thomas Abadie,Meihui Wang,Omar K. Matar,Rodney S. Ruoff
标识
DOI:10.1021/acs.chemmater.3c02361
摘要
Although chemical vapor deposition (CVD) remains the method of choice for synthesizing defect-free and high-quality 2D films (such as graphene and h-BN), the method has serious issues with process repeatability and reproducibility. This makes it difficult to build up from the literature, test a hypothesis quickly, or scale up a process. The primary reason for this is that the CVD reactor, to this day, remains a black box with a reaction environment that is poorly understood and cannot be measured or monitored directly. Consequently, it is also difficult to study process kinetics and growth mechanisms and correlate experimental results to atomic-level simulations. A possible way to overcome this problem is to use Computational Fluid Dynamics (CFD), both to identify the measurable external (process and reactor) parameters that control the reaction environment and to simulate this reaction environment and understand how it changes when these controllable external parameters are varied. This paper describes how this may be done in practice using the growth of single-layer graphene in a hot-wall tube reactor as the representative case and the CFD toolbox OpenFOAM. Based on our findings, we have shown why it is critical (1) to understand the flow properties inside the reactor and combine it with experimental results to study the growth process for graphene and other 2D films and (2) to measure, monitor, and report all relevant external parameters to ensure process repeatability and reproducibility.
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