Possible Gas-Phase Reactions of H2/CH4/Tetramethylsilane in Diamond/β-SiC Nanocomposite Film Deposition: An Ab-Initio Study

A Q-learning method based on coarse-to-fine potential energy surface for locating transition state and reaction pathway

Transition state (TS) on the potential energy surface (PES) plays a key role in determining the kinetics and thermodynamics of chemical reactions. Inspired by the fact that the dynamics of complex systems are always driven by rare but significant transition events, we herein propose a TS search method in accordance with the Q-learning algorithm. Appropriate reward functions are set for a given PES to optimize the reaction pathway through continuous trial and error, and then the TS can be obtained from the optimized reaction pathway. The validity of this Q-learning method with reasonable settings of Q-value table including actions, states, learning rate, greedy rate, discount rate, and so on, is exemplified in 2 two-dimensional potential functions. In the applications of the Q-learning method to two chemical reactions, it is demonstrated that the Q-learning method can predict consistent TS and reaction pathway with those by ab initio calculations. Notably, the PES must be well prepared before using the Q-learning method, and a coarse-to-fine PES scanning scheme is thus introduced to save the computational time while maintaining the accuracy of the Q-learning prediction. This work offers a simple and reliable Q-learning method to search for all possible TS and reaction pathway of a chemical reaction, which may be a new option for effectively exploring the PES in an extensive search manner.

Graphene/3C-SiC Hybrid Nanolaminate

In this work, we demonstrate a one-step approach to create graphene/3C-SiC nanolaminate structure using microwave plasma chemical vapor deposition technique. Layer-by-layer arrangement of thin 3C-SiC layers and graphene sheets is obtained with the thicknesses of the individual 3C-SiC layers and graphene sheets being 5-10 nm and 2-5 nm, respectively. An intimate contact between 3C-SiC and the graphene sheets is achieved and the nanolaminate film shows a high room temperature conductivity of 96.1 S/cm. A dedicated structural analysis of the nanolaminates by means of high-resolution transmission electron microscopy (HRTEM) reveals that the growth of the nanolaminates follows an iterative process: preferential graphene nucleation around the planar defects at the central region of the SiC layer, leading to the "splitting" of the SiC layer; and the thickening of the SiC layer after being "split". A growth mechanism based on both kinetics and thermodynamics is proposed. Following the proposed mechanism, it is possible to control the layer thickness of the graphene/3C-SiC hybrid nanolaminate by manipulating the carbon concentration in the gas phase, which is further experimentally verified. The high electrical conductivity, large surface area porous structure, feasible integration on different substrates (metal, Mo; semiconductor, Si and 2H-SiC; insulator, diamond) of the graphene/3C-SiC hybrid nanolaminate as well as other unprecedented advantages of the nanolaminate structure make it very promising for applications in mechanical, energy, and sensor-related areas.