Machine Learning for Tailoring Optoelectronic Properties of Single-Walled Carbon Nanotube Films

Tutkimustuotos: Lehtiartikkeli

Tutkijat

Organisaatiot

  • Skolkovo Institute of Science and Technology

Kuvaus

A machine learning technique, namely, support vector regression, is implemented to enhance single-walled carbon nanotube (SWCNT) thin-film performance for transparent and conducting applications. We collected a comprehensive data set describing the influence of synthesis parameters (temperature and CO2 concentration) on the equivalent sheet resistance (at 90% transmittance in the visible light range) for SWCNT films obtained by a semi-industrial aerosol (floating-catalyst) CVD with CO as a carbon source and ferrocene as a catalyst precursor. The predictive model trained on the data set shows principal applicability of the method for refining synthesis conditions toward the advanced optoelectronic performance of multiparameter processes such as nanotube growth. Further doping of the improved carbon nanotube films with HAuCl4 results in the equivalent sheet resistance of 39 ω/ one of the lowest values achieved so far for SWCNT films.

Yksityiskohdat

AlkuperäiskieliEnglanti
Sivut6962-6966
Sivumäärä5
JulkaisuJournal of Physical Chemistry Letters
Vuosikerta10
Numero21
TilaJulkaistu - 1 tammikuuta 2019
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

ID: 39382347