Publications

@article{Ji2020,

title = {High-throughput screening and machine learning for the efficient growth of high-quality single-wall carbon nanotubes},

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},

url = {http://link.springer.com/10.1007/s12274-021-3387-y},

doi = {10.1007/s12274-021-3387-y},

issn = {1998-0124},

year  = {2021},

date = {2021-03-01},

journal = {Nano Research},

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 the 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. 1280 data were collected to train machine learning models. Random forest regression (RFR) showed desirable precision in predicting the growth conditions for high-quality SWCNTs, as validated by further chemical vapor deposition (CVD) growth. This method shows great potential for use in structure-controlled growth of SWCNTs.},

keywords = {chemical vapor deposition, high throughput, machine learning, optimization, single-wall carbon nanotube},

pubstate = {published},

tppubtype = {article}

}