▎ 摘　　要

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.