Abstract
Wafer-scale monolayer two-dimensional (2D) materials have been realized by epitaxial chemical vapor deposition (CVD) in recent years. To scale up the synthesis of 2D materials, a systematic analysis of how the growth dynamics depend on the growth parameters is essential to unravel its mechanisms. However, the studies of CVD-grown 2D materials mostly adopted the control variate method and considered each parameter as an independent variable, which is not comprehensive for 2D materials growth optimization. Herein, we synthesized a representative 2D material, monolayer hexagonal boron nitride (hBN), on single-crystalline Cu (111) by epitaxial chemical vapor deposition and varied the growth parameters to regulate the hBN domain sizes. Furthermore, we explored the correlation between two growth parameters and provided the growth windows for large flake sizes by the Gaussian process. This new analysis approach based on machine learning provides a more comprehensive understanding of the growth mechanism for 2D materials.
Original language | English |
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Pages (from-to) | 4741-4748 |
Number of pages | 8 |
Journal | Nano Letters |
Volume | 23 |
Issue number | 11 |
DOIs | |
State | Published - Jun 14 2023 |
Funding
J.-H.P., A.-Y.L., J.W., and J.K. acknowledge the support from the U.S. Army Research Office (ARO) MURI project under grant W911NF-18-1-04320431 and the US Army Research Office through the Institute for Soldier Nanotechnologies at MIT, under cooperative agreement W911NF-18-2-0048. T.Z., Z.W., and J.K. acknowledge the support from the U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES) under award DE-SC0020042. J.M. acknowledges partial support by the US Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), Division of Materials Sciences and Engineering, under award DE-SC0010378. The STEM experiment was performed at the Center for Nanophase Materials Sciences (CNMS), which is a U.S. Department of Energy Office of Science User Facility. M.C. acknowledges Early Career project FWP# ERKCZ55 supported by the DOE Office of Science.
Keywords
- Gaussian process
- chemical vapor deposition
- growth parameter
- hexagonal boron nitride
- machine learning