Published December 2019 | Version v1
Journal article

Sulphur doped iron cobalt oxide nanocaterpillars: An electrode for supercapattery with ultrahigh energy density and oxygen evolution reaction

  • 1. Department of Chemistry, University of Delhi, Delhi, 110007 (India)

Description

Highlights: • Sulphur doping in iron cobalt oxide significantly reduces charge transfer resistance. • Fabrication of Supercapattery using FCS (supercapacitor) and FCO (battery-like). • Ultrahigh high energy density (140 Wh kg−1) with a power density of 1434 W kg−1. • High roughness of FCS (factor, Rf = 1135) corroborated to low overpotential for OER. -- Abstract: Inclusion of sulphur in metal oxide nanostructure can alter the electrochemical performance by minimizing its resistance for charge transfer. Herein, We report the synthesis of sulphur doped iron cobalt oxide nanocaterpillars (FCS) and their utilization in supercapatteries and for oxygen evolution reaction (OER). These nanocaterpillars (FCS) demonstrate a high specific capacitance of 1809 F g−1 at 2 A g−1 along with an exceptional stability of 138% after 15,000 cycles in alkaline media. Considering them in the fabrication of a supercapattery (with iron cobalt oxide (FCO) as a negative electrode), we achieve an ultrahigh energy density of 140 Wh kg−1 at 1434 W kg−1 with an excellent cycling stability (95% till 5000 cycles). To our surprise, it even outshines as an electrocatalyst (OER) attaining a current density of 10 mA cm−2 at a low overpotential of 300 mV. As a consequence of the extremely high roughness factor (Rf = 1135) these nanocaterpillars deliver a best Tafel slope of 56 mV/dec. Henceforth, these sulphur doped iron cobalt oxide nanocaterpillars (FCS) display enormous potential in storing and converting energy.

Additional details

Identifiers

DOI
10.1016/j.electacta.2019.135076;
PII
S0013468619319474;

Publishing Information

Journal Title
Electrochimica Acta
Journal Volume
328
Journal Page Range
vp.
ISSN
0013-4686
CODEN
ELCAAV

Optional Information

Copyright
Copyright (c) 2019 Elsevier Ltd. All rights reserved.