A Stochastic Graphene Growth Kinetics Model
针对化学气相沉积法制备石墨烯的随机成核与生长过程，提出了一个贝叶斯推断框架，用于从实验数据中学习生长动力学并量化不确定性，同时将动力学与可控实验因素关联，以指导未来实验设计。
Summary Graphene is an emerging nanomaterial for a wide variety of novel applications. Controlled synthesis of high quality graphene sheets requires analytical understanding of graphene growth kinetics. Graphene growth via chemical vapour deposition starts with randomly nucleated islands that gradually develop into complex shapes, grow in size and eventually connect together to form a graphene sheet. Models proposed for this stochastic process do not, in general, permit assessment of uncertainty. We develop a stochastic framework for the growth process and propose Bayesian inferential models, which account for the data collection mechanism and allow for uncertainty analyses, for learning about the kinetics from experimental data. Furthermore, we link the growth kinetics with controllable experimental factors, thus providing a framework for statistical design and analysis of future experiments.