High-throughput screening and machine learning for the efficient growth of high-quality single-wall carbon nanotubes

• Published in: Nano research 2021-12, Vol.14 (12), p.4610-4615
• Main Authors: Ji, Zhong-Hai, Zhang, Lili, Tang, Dai-Ming, Chen, Chien-Ming, Nordling, Torbjörn E. M., Zhang, Zheng-De, Ren, Cui-Lan, Da, Bo, Li, Xin, Guo, Shu-Yu, Liu, Chang, Cheng, Hui-Ming
• Format: Article
• Language: English
• Subjects: Atomic/Molecular Structure and Spectra; Biomedicine; Biotechnology; Carbon; Catalysts; Chemical vapor deposition; Chemistry and Materials Science; Cobalt; Condensed Matter Physics; Growth conditions; high throughput; High-throughput screening; Immunoglobulins; Learning algorithms; Machine learning; Materials Science; Nanoparticles; Nanotechnology; Nanotubes; optimization; Parameters; Raman spectroscopy; Regression analysis; Research Article; Silicon wafers; Single wall carbon nanotubes; single-wall carbon nanotube
• ISSN: 1998-0124; 1998-0000; 1998-0000
• DOI: 10.1007/s12274-021-3387-y

It has been a great challenge to optimize the growth conditions toward structure-controlled growth of single-wall carbon nanotubes (SWCNTs). Here, a high-throughput method combined with machine learning is reported that efficiently screens the growth conditions for the synthesis of high-quality SWCNTs. Patterned cobalt (Co) nanoparticles were deposited on a numerically marked silicon wafer as catalysts, and parameters of temperature, reduction time and carbon precursor were optimized. The crystallinity of the SWCNTs was characterized by Raman spectroscopy where the featured G/D peak intensity ( I G / I D ) was extracted automatically and mapped to the growth parameters to build a database. 1,280 data were collected to train machine learning models. Random forest regression (RFR) showed high precision in predicting the growth conditions for high-quality SWCNTs, as validated by further chemical vapor deposition (CVD) growth. This method shows great potential in structure-controlled growth of SWCNTs.