Machine learning driven synthesis of few-layered WTe₂ with geometrical control
Reducing the lateral scale of two-dimensional (2D) materials to one-dimensional (1D) has attracted substantial research interest not only to achieve competitive electronic applications but also for the exploration of fundamental physical properties. Controllable synthesis of high-quality 1D nanoribb...
| Main Authors: | , , , , , , , , , , , , , , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/159346 |
| _version_ | 1824453986759999488 |
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| author | Xu, Manzhang Tang, Bijun Lu, Yuhao Zhu, Chao Lu, Qianbo Zhu, Chao Zheng, Lu Zhang, Jingyu Han, Nannan Fang, Weidong Guo, Yuxi Di, Jun Song, Pin He, Yongmin Kang, Lixing Zhang, Zhiyong Zhao, Wu Guan, Cuntai Wang, Xuewen Liu, Zheng |
| author2 | School of Materials Science and Engineering |
| author_facet | School of Materials Science and Engineering Xu, Manzhang Tang, Bijun Lu, Yuhao Zhu, Chao Lu, Qianbo Zhu, Chao Zheng, Lu Zhang, Jingyu Han, Nannan Fang, Weidong Guo, Yuxi Di, Jun Song, Pin He, Yongmin Kang, Lixing Zhang, Zhiyong Zhao, Wu Guan, Cuntai Wang, Xuewen Liu, Zheng |
| author_sort | Xu, Manzhang |
| collection | NTU |
| description | Reducing the lateral scale of two-dimensional (2D) materials to one-dimensional (1D) has attracted substantial research interest not only to achieve competitive electronic applications but also for the exploration of fundamental physical properties. Controllable synthesis of high-quality 1D nanoribbons (NRs) is thus highly desirable and essential for further study. Here, we report the implementation of supervised machine learning (ML) for the chemical vapor deposition (CVD) synthesis of high-quality quasi-1D few-layered WTe2 NRs. Feature importance analysis indicates that H2 gas flow rate has a profound influence on the formation of WTe2, and the source ratio governs the sample morphology. Notably, the growth mechanism of 1T' few-layered WTe2 NRs is further proposed, which provides new insights for the growth of intriguing 2D and 1D tellurides and may inspire the growth strategies for other 1D nanostructures. Our findings suggest the effectiveness and capability of ML in guiding the synthesis of 1D nanostructures, opening up new opportunities for intelligent materials development. |
| first_indexed | 2025-02-19T03:15:08Z |
| format | Journal Article |
| id | ntu-10356/159346 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:15:08Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1593462022-06-15T01:41:24Z Machine learning driven synthesis of few-layered WTe₂ with geometrical control Xu, Manzhang Tang, Bijun Lu, Yuhao Zhu, Chao Lu, Qianbo Zhu, Chao Zheng, Lu Zhang, Jingyu Han, Nannan Fang, Weidong Guo, Yuxi Di, Jun Song, Pin He, Yongmin Kang, Lixing Zhang, Zhiyong Zhao, Wu Guan, Cuntai Wang, Xuewen Liu, Zheng School of Materials Science and Engineering School of Electrical and Electronic Engineering School of Computer Science and Engineering CNRS International NTU THALES Research Alliances Engineering::Materials Engineering::Computer science and engineering Chemical Vapor Deposition Nanostructures Reducing the lateral scale of two-dimensional (2D) materials to one-dimensional (1D) has attracted substantial research interest not only to achieve competitive electronic applications but also for the exploration of fundamental physical properties. Controllable synthesis of high-quality 1D nanoribbons (NRs) is thus highly desirable and essential for further study. Here, we report the implementation of supervised machine learning (ML) for the chemical vapor deposition (CVD) synthesis of high-quality quasi-1D few-layered WTe2 NRs. Feature importance analysis indicates that H2 gas flow rate has a profound influence on the formation of WTe2, and the source ratio governs the sample morphology. Notably, the growth mechanism of 1T' few-layered WTe2 NRs is further proposed, which provides new insights for the growth of intriguing 2D and 1D tellurides and may inspire the growth strategies for other 1D nanostructures. Our findings suggest the effectiveness and capability of ML in guiding the synthesis of 1D nanostructures, opening up new opportunities for intelligent materials development. Ministry of Education (MOE) The authors gratefully acknowledge financial support by National Key Research and Development Program of China (2020YFB2008501 and 2019YFC1520900), the National Natural Science Foundation of China (61974120, 11904289, 61701402, and 61804125), Key Research and Development Program of Shaanxi Province (2020ZDLGY04-08, 2020GXLH-Z-027, 2021JZ-43), the Key Program for International Science and Technology Cooperation Projects of Shaanxi Province (2018KWZ-08), the Natural Science Foundation of Shaanxi Province (2019JQ-613), the Natural Science Foundation of Ningbo (202003N4003), the Fundamental Research Funds for the Central Universities (3102019PY004, 31020190QD010, and 3102019JC004), and the start-up funds from Northwestern Polytechnical University. The authors also acknowledge the support from Ministry of Education, Singapore, under its AcRF Tier 3 Programme "Geometrical Quantum Materials" (MOE2018-T3-1-002) and AcRF Tier 1 RG161/19. 2022-06-15T01:41:24Z 2022-06-15T01:41:24Z 2021 Journal Article Xu, M., Tang, B., Lu, Y., Zhu, C., Lu, Q., Zhu, C., Zheng, L., Zhang, J., Han, N., Fang, W., Guo, Y., Di, J., Song, P., He, Y., Kang, L., Zhang, Z., Zhao, W., Guan, C., Wang, X. & Liu, Z. (2021). Machine learning driven synthesis of few-layered WTe₂ with geometrical control. Journal of the American Chemical Society, 143(43), 18103-18113. https://dx.doi.org/10.1021/jacs.1c06786 0002-7863 https://hdl.handle.net/10356/159346 10.1021/jacs.1c06786 34606266 2-s2.0-85117207797 43 143 18103 18113 en MOE2018-T3-1-002 RG161/19 Journal of the American Chemical Society © 2021 American Chemical Society. All rights reserved. |
| spellingShingle | Engineering::Materials Engineering::Computer science and engineering Chemical Vapor Deposition Nanostructures Xu, Manzhang Tang, Bijun Lu, Yuhao Zhu, Chao Lu, Qianbo Zhu, Chao Zheng, Lu Zhang, Jingyu Han, Nannan Fang, Weidong Guo, Yuxi Di, Jun Song, Pin He, Yongmin Kang, Lixing Zhang, Zhiyong Zhao, Wu Guan, Cuntai Wang, Xuewen Liu, Zheng Machine learning driven synthesis of few-layered WTe₂ with geometrical control |
| title | Machine learning driven synthesis of few-layered WTe₂ with geometrical control |
| title_full | Machine learning driven synthesis of few-layered WTe₂ with geometrical control |
| title_fullStr | Machine learning driven synthesis of few-layered WTe₂ with geometrical control |
| title_full_unstemmed | Machine learning driven synthesis of few-layered WTe₂ with geometrical control |
| title_short | Machine learning driven synthesis of few-layered WTe₂ with geometrical control |
| title_sort | machine learning driven synthesis of few layered wte₂ with geometrical control |
| topic | Engineering::Materials Engineering::Computer science and engineering Chemical Vapor Deposition Nanostructures |
| url | https://hdl.handle.net/10356/159346 |
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