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Revealing Variable Dependences in Hexagonal Boron Nitride Synthesis via Machine Learning.

Publication ,  Journal Article
Park, J-H; Lu, A-Y; Tavakoli, MM; Kim, NY; Chiu, M-H; Liu, H; Zhang, T; Wang, Z; Wang, J; Martins, LGP; Luo, Z; Chi, M; Miao, J; Kong, J
Published in: Nano letters
June 2023

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.

Duke Scholars

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Published In

Nano letters

DOI

EISSN

1530-6992

ISSN

1530-6984

Publication Date

June 2023

Volume

23

Issue

11

Start / End Page

4741 / 4748

Related Subject Headings

  • Nanoscience & Nanotechnology
 

Citation

APA
Chicago
ICMJE
MLA
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Park, J.-H., Lu, A.-Y., Tavakoli, M. M., Kim, N. Y., Chiu, M.-H., Liu, H., … Kong, J. (2023). Revealing Variable Dependences in Hexagonal Boron Nitride Synthesis via Machine Learning. Nano Letters, 23(11), 4741–4748. https://doi.org/10.1021/acs.nanolett.2c04624
Park, Ji-Hoon, Ang-Yu Lu, Mohammad Mahdi Tavakoli, Na Yeon Kim, Ming-Hui Chiu, Hongwei Liu, Tianyi Zhang, et al. “Revealing Variable Dependences in Hexagonal Boron Nitride Synthesis via Machine Learning.Nano Letters 23, no. 11 (June 2023): 4741–48. https://doi.org/10.1021/acs.nanolett.2c04624.
Park J-H, Lu A-Y, Tavakoli MM, Kim NY, Chiu M-H, Liu H, et al. Revealing Variable Dependences in Hexagonal Boron Nitride Synthesis via Machine Learning. Nano letters. 2023 Jun;23(11):4741–8.
Park, Ji-Hoon, et al. “Revealing Variable Dependences in Hexagonal Boron Nitride Synthesis via Machine Learning.Nano Letters, vol. 23, no. 11, June 2023, pp. 4741–48. Epmc, doi:10.1021/acs.nanolett.2c04624.
Park J-H, Lu A-Y, Tavakoli MM, Kim NY, Chiu M-H, Liu H, Zhang T, Wang Z, Wang J, Martins LGP, Luo Z, Chi M, Miao J, Kong J. Revealing Variable Dependences in Hexagonal Boron Nitride Synthesis via Machine Learning. Nano letters. 2023 Jun;23(11):4741–4748.
Journal cover image

Published In

Nano letters

DOI

EISSN

1530-6992

ISSN

1530-6984

Publication Date

June 2023

Volume

23

Issue

11

Start / End Page

4741 / 4748

Related Subject Headings

  • Nanoscience & Nanotechnology