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 ...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.展开更多
基金This project is supported by the National Key Research and Development Program of China(No.2016YFA0200101)the National Natural Science Foundation of China(Nos.51522210,51972311,51625203,51532008,51761135122 and 52001322)JSPS KAKENHI Grant Number JP20K05281 and JP25820336,and MOST 108-2634-F-006-009 and MOST 109-2224-E-006-003.
文摘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.
