Porous SnO2-Cu x O nanocomposite thin film on carbon nanotubes as electrodes for high performance supercapacitors.

Daneshvar, F., Aziz, A., Abdelkader, A.M., Zhang, T., Sue, H-J. and Welland, M.E., 2019. Porous SnO2-Cu x O nanocomposite thin film on carbon nanotubes as electrodes for high performance supercapacitors. Nanotechnology, 30 (1), 015401.

Full text available as:

[img] PDF
Daneshvar+et+al_2018_Nanotechnology_10.1088_1361-6528_aae5c6.pdf - Accepted Version
Restricted to Repository staff only until 25 October 2019.
Available under License Creative Commons Attribution Non-commercial No Derivatives.

1MB

DOI: 10.1088/1361-6528/aae5c6

Abstract

Metal oxides are promising materials for supercapacitors due to their high theoretical capacitance. However, their poor electrical conductivity is a major challenge. Hybridization with conductive nanostructured carbon-based materials such as carbon nanotubes (CNTs) has been proposed to improve the conductivity and increase the surface area. In this work, CNTs are used as a template for synthesizing porous thin films of SnO2-CuO-Cu2O (SnO2-Cu x O) via an electroless deposition technique. Tin, with its high wettability and electrical conductivity, acts as an intermediate layer between copper and the CNTs and provides a strong interaction between them. We also observed that by controlling the interfacial characteristics of CNTs and varying the composition of the electroless bath, the SnO2-Cu x O thin film morphology can be easily manipulated. Electrochemical characterizations show that CNT/SnO2-Cu x O nanocomposite possesses pseudocapacitive behavior that reaches a specific capacitance of 662 F g-1 and the retention is 94% after 5000 cycles, which outperforms any known copper and tin-based supercapacitors in the literature. This excellent performance is mainly attributed to high specific surface area, small particle size, the synergistic effect of Sn, and conductivity improvement by using CNTs. The combination of CNTs and metal oxides holds promise for supercapacitors with improved performance.

Item Type:Article
ISSN:0957-4484
Group:Faculty of Science & Technology
ID Code:31465
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:19 Nov 2018 09:42
Last Modified:19 Nov 2018 09:42

Downloads

Downloads per month over past year

More statistics for this item...
Repository Staff Only -