@article{Ji2021, 
author = {Zhong-Hai Ji and Lili Zhang and Dai-Ming Tang and Chien-Ming Chen and Torbjörn E. M. Nordling and Zheng-De Zhang and Cui-Lan Ren and Bo Da and Xin Li and Shu-Yu Guo and Chang Liu and Hui-Ming Cheng},
title = {High-throughput screening and machine learning for the efficient growth of high-quality single-wall carbon nanotubes},
year = {2021},
journal = {Nano Research},
volume = {14},
number = {12},
pages = {4610-4615},
keywords = {single-wall carbon nanotube, high throughput, machine learning, optimization, chemical vapor deposition},
url = {https://www.sciopen.com/article/10.1007/s12274-021-3387-y},
doi = {10.1007/s12274-021-3387-y},
abstract = {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 (IG/ID) 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.}
}