• 文献标题:   Real-Time Multiscale Monitoring and Tailoring of Graphene Growth on Liquid Copper
  • 文献类型:   Article
  • 作  者:   JANKOWSKI M, SAEDI M, LA PORTA F, MANIKAS AC, TSAKONAS C, CINGOLANI JS, ANDERSEN M, DE VOOGD M, VAN BAARLE GJC, REUTER K, GALIOTIS C, RENAUD G, KONOVALOV OV, GROOT IMN
  • 作者关键词:   cvd graphene, liquid metal catalyst, twodimensional material, raman spectroscopy, xray diffraction, radiation optical microscopy, selforganization
  • 出版物名称:   ACS NANO
  • ISSN:   1936-0851 EI 1936-086X
  • 通讯作者地址:  
  • 被引频次:   17
  • DOI:   10.1021/acsnano.0c10377 EA JUN 2021
  • 出版年:   2021

▎ 摘  要

The synthesis of large, defect-free two-dimensional materials (2DMs) such as graphene is a major challenge toward industrial applications. Chemical vapor deposition (CVD) on liquid metal catalysts (LMCats) is a recently developed process for the fast synthesis of high-quality single crystals of 2DMs. However, up to now, the lack of in situ techniques enabling direct feedback on the growth has limited our understanding of the process dynamics and primarily led to empirical growth recipes. Thus, an in situ multiscale monitoring of the 2DMs structure, coupled with a real-time control of the growth parameters, is necessary for efficient synthesis. Here we report real-time monitoring of graphene growth on liquid copper (at 1370 K under atmospheric pressure CVD conditions) via four complementary in situ methods: synchrotron X-ray diffraction and reflectivity, Raman spectroscopy, and radiation-mode optical microscopy. This has allowed us to control graphene growth parameters such as shape, dispersion, and the hexagonal supra-organization with very high accuracy. Furthermore, the switch from continuous polycrystalline film to the growth of millimeter-sized defect-free single crystals could also be accomplished. The presented results have far-reaching consequences for studying and tailoring 2D material formation processes on LMCats under CVD growth conditions. Finally, the experimental observations are supported by multiscale modeling that has thrown light into the underlying mechanisms of graphene growth.