Issue 23, 2023

Preparation and characterization of nanostructured Fe-doped CoTe2 electrocatalysts for the oxygen evolution reaction

Abstract

The key step for hydrogen production through water electrolysis is the development of highly efficient and inexpensive oxygen evolution reaction (OER) catalysts. In this work, we report the successful synthesis of a nanostructured Fe-doped cobalt-based telluride (Fe-doped CoTe2) catalyst on Co foam by a simple one-step hydrothermal synthesis method, which shows excellent OER performance. The influences of Fe doping amounts and reaction temperatures on the morphology, structure, composition, and the OER performance of cobalt-based tellurides have been systematically studied. The optimal sample Co@0.3 g FeCoTe2-200 exhibits a low overpotential of 300 mV at a current density of 10 mA cm−2, and a small Tafel slope of 36.99 mV dec−1, outperforming the undoped cobalt telluride catalysts (Co@CoTe2-200). The Co@0.3 g FeCoTe2-200 electrode also reveals a small overpotential degradation of around 26 mV after an 18-hour continuous OER process. These results unambiguously confirm that Fe doping helps improve the OER activity and long-term catalytic stability. The superior performance of nanostructured Fe-doped CoTe2 can be attributed to the porous structure and the synergistic effect of Co and Fe elements. This study provides a new approach for the preparation of bimetallic telluride catalysts with enhanced OER performance, and Fe-doped CoTe2 holds substantial promise for use as a high-efficiency, cost-effective catalyst for alkaline water electrolysis.

Graphical abstract: Preparation and characterization of nanostructured Fe-doped CoTe2 electrocatalysts for the oxygen evolution reaction

Supplementary files

Article information

Article type
Paper
Submitted
19 Apr 2023
Accepted
04 May 2023
First published
09 May 2023

Dalton Trans., 2023,52, 7906-7916

Preparation and characterization of nanostructured Fe-doped CoTe2 electrocatalysts for the oxygen evolution reaction

Z. Yang, H. Tan, Y. Qi, S. Ma, J. Bai, L. Liu and D. Xiong, Dalton Trans., 2023, 52, 7906 DOI: 10.1039/D3DT01179H

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