TY  - JOUR
AU  - Ji, Zhong-Hai
AU  - Zhang, Lili
AU  - Tang, Dai-Ming
AU  - Chen, Chien-Ming
AU  - Nordling, Torbjörn E. M.
AU  - Zhang, Zheng-De
AU  - Ren, Cui-Lan
AU  - Da, Bo
AU  - Li, Xin
AU  - Guo, Shu-Yu
AU  - Liu, Chang
AU  - Cheng, Hui-Ming
PY  - 2021
TI  - High-throughput screening and machine learning for the efficient growth of high-quality single-wall carbon nanotubes
JO  - Nano Research
SN  - 1998-0124
SP  - 4610
EP  - 4615
VL  - 14
IS  - 12
AB  - 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.
UR  - https://doi.org/10.1007/s12274-021-3387-y
DO  - 10.1007/s12274-021-3387-y