Issue 9, 2023

Optimization of thermoelectric properties of carbon nanotube veils by defect engineering

Abstract

Carbon nanotubes (CNTs), with their combination of excellent electrical conductivity, Seebeck coefficient, mechanical robustness and environmental stability are highly desired as thermoelectric (TE) materials for a wide range of fields including Internet of Things, health monitoring and environmental remediation solutions. However, their high thermal conductivity (κ) is an obstacle to practical TE applications. Herein, we present a novel method to reduce the κ of CNT veils, by introducing defects, while preserving their Seebeck coefficient and electrical conductivity. Solid-state drawing of a CNT veil embedded within two polycarbonate films generates CNT veil fragments of reducing size with increasing draw ratio. A successive heat treatment, at above the polycarbonate glass-to-rubber transition temperature, spontaneously reconnects the CNT veils fragments electrically but not thermally. Stretching to a draw ratio of 1.5 and heat repairing at 170 °C leads to a dramatic 3.5-fold decrease in κ (from 46 to 13 W m−1 K−1), in contrast with a decrease in electrical conductivity of only 26% and an increase in Seebeck coefficient of 10%. To clarify the mechanism of reduction in thermal conductivity, a large-scale mesoscopic simulation of CNT veils under uniaxial stretching has also been used. This work shows that defect engineering can be a valuable strategy to optimize TE properties of CNT veils and, potentially, other thermoelectric materials.

Graphical abstract: Optimization of thermoelectric properties of carbon nanotube veils by defect engineering

Supplementary files

Article information

Article type
Communication
Submitted
06 Apr. 2023
Accepted
08 Jūn. 2023
First published
16 Jūn. 2023
This article is Open Access
Creative Commons BY-NC license

Mater. Horiz., 2023,10, 3601-3609

Optimization of thermoelectric properties of carbon nanotube veils by defect engineering

C. Zeng, P. Stenier, K. Chen, K. Wan, M. Dong, S. Li, C. Kocabas, M. J. Reece, D. G. Papageorgiou, A. N. Volkov, H. Zhang and E. Bilotti, Mater. Horiz., 2023, 10, 3601 DOI: 10.1039/D3MH00525A

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