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

Zhong Hai Ji, Lili Zhang, Dai Ming Tang, Chien Ming Chen, Torbjörn E.M. Nordling, Zheng De Zhang, Cui Lan Ren, Bo Da, Xin Li, Shu Yu Guo, Chang Liu, Hui Ming Cheng

Research output: Contribution to journalArticlepeer-review

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. [Figure not available: see fulltext.].

Original languageEnglish
JournalNano Research
DOIs
Publication statusAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Atomic and Molecular Physics, and Optics
  • Materials Science(all)
  • Condensed Matter Physics
  • Electrical and Electronic Engineering

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