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arXiv 2305.18331cond-mat.mtrl-sciphysics.chem-ph
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原位表征与分子模拟揭示化学气相沉积过程中石墨烯在液态铜上的生长动力学

Operando Characterization and Molecular Simulations Reveal the Growth Kinetics of Graphene on Liquid Copper during Chemical Vapor Deposition

Valentina Rein, Hao Gao, Hendrik H. Heenen, Wissal Sghaier, Anastasios C. Manikas, Mehdi Saedi, Johannes T. Margraf, Costas Galiotis, Gilles Renaud, Oleg V. Kon… 展开作者

Valentina Rein, Hao Gao, Hendrik H. Heenen, Wissal Sghaier, Anastasios C. Manikas, Mehdi Saedi, Johannes T. Margraf, Costas Galiotis, Gilles Renaud, Oleg V. Konovalov, Irene M. N. Groot, Karsten Reuter, Maciej Jankowski

AI总结:

本文结合原位光学显微与机器学习势自由能模拟,建立微动力学模型,揭示液态铜上石墨烯CVD生长的混合动力学机制,并阐明抑制二聚体附着和低势垒开环自修复是提升石墨烯质量的关键。

摘要:

近年来,液态金属催化剂已成为二维材料可控、大规模、高质量合成的引人注目的选择。目前,人们对这一复杂催化过程的机理理解甚少,对其控制因素或它为何优于在相应固体催化剂上的生长也缺乏认识。在此,我们报告了一项实验与计算相结合的研究,针对化学气相沉积过程中石墨烯在液态铜催化剂上生长的动力学。通过使用原位辐射模式光学显微镜实时监测生长的石墨烯薄片,我们在广泛的CH4与H2压力比和沉积温度范围内探索了生长形貌和动力学。薄片半径的恒定生长速率表明生长模式受前驱体附着限制,而依赖于甲烷通量的薄片形状则指向有限的前驱体可用性。利用一个高效的机器学习矩张量势(该势函数基于密度泛函理论数据训练)实现的大规模自由能模拟,为关键原子尺度生长过程提供了定量势垒。丰富的实验和理论数据可以一致地整合到一个微动力学模型中,该模型揭示了混合生长动力学,与固体Cu上的情况不同,它部分受前驱体附着控制,同时也受前驱体可用性控制。直接指向石墨烯质量提高的关键机理方面包括:碳二聚体附着被大幅抑制,原因是该前驱体物种容易并入液体表面;以及一个低势垒开环过程,该过程能够自修复由残余二聚体附着产生的五元环。

英文摘要:

In recent years, liquid metal catalysts have emerged as a compelling choice for the controllable, large-scale, and high-quality synthesis of two-dimensional materials. At present, there is little mechanistic understanding of the intricate catalytic process, though, of its governing factors or what renders it superior to growth at the corresponding solid catalysts. Here, we report on a combined experimental and computational study of the kinetics of graphene growth during chemical vapor deposition on a liquid copper catalyst. By monitoring the growing graphene flakes in real time using in situ radiation-mode optical microscopy, we explore the growth morphology and kinetics over a wide range of CH4-to-H2 pressure ratios and deposition temperatures. Constant growth rates of the flakes' radius indicate a growth mode limited by precursor attachment, whereas methane-flux-dependent flake shapes point to limited precursor availability. Large-scale free energy simulations enabled by an efficient machine-learning moment tensor potential trained to density-functional theory data provide quantitative barriers for key atomic-scale growth processes. The wealth of experimental and theoretical data can be consistently combined into a microkinetic model that reveals mixed growth kinetics that, in contrast to the situation at solid Cu, is partly controlled by precursor attachment alongside precursor availability. Key mechanistic aspects that directly point toward the improved graphene quality are a largely suppressed carbon dimer attachment due to the facile incorporation of this precursor species into the liquid surface and a low-barrier ring-opening process that self-heals 5-membered rings resulting from remaining dimer attachments.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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