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Updated: Jun 12, 2025

Synthesis of Platinum-nickel Nanowires and Optimization for Oxygen Reduction Performance
Published on: April 27, 2018
Enhancing Oxygen Evolution Reaction Performance with rGO/CoNi-Prussian Blue-Derived Oxyhydroxide Nanocomposite
Pedro H S Borges1, Josué M Gonçalves2, Carmel B Breslin3,4
1Institute of Chemistry, Federal University of Uberlândia, 38400-902 Uberlândia, MG, Brazil.
Abstract:
Electrochemical water splitting is a promising approach in the development of renewable energy technologies, providing an alternative to fossil fuels. It has attracted considerable attention in recent years. The benchmark materials used in water splitting are precious metals that are expensive and scarce. Therefore, this work proposes a strategic electrochemical synthesis of a reduced graphene oxide and cobalt-nickel hexacyanoferrate (rGO/CoNiHCF)-derived composite (rGO/CoNiPBd-OOH) to achieve optimized OER performance. The optimum rGO/CoNiHCF was fabricated with the Co:Ni precursors in a 3:1 ratio with a ferricyanide solution of pH = 1.0. Using an alkaline electrochemical treatment, the well-distributed globular particles of CoNiHCF over rGO sheets were converted into layered frameworks of metallic (oxy)hydroxide species, giving the final rGO/CoNiPBd-OOH nanocomposite. This nanocomposite presented favorable kinetic activity resulting in a Tafel slope of 33 mV dec-1, while rGO, CoNiPBd-OOH, and RuO2 exhibited slopes of 80, 47, and 51 mV dec-1, respectively. Although the benchmark RuO2 electrocatalyst showed a lower overpotential (240 mV dec-1) at a current density of 10 mA cm-2, the rGO/CoNiPBd-OOH performed well with an overpotential of 346 mV, combined with superior stability compared to CoNiPBd-OOH and RuO2, maintaining a current density of 10 mA cm-2 for 15 h with an overpotential loss of 6.92%. This work successfully presents an "all-electrochemical" synthesis of a rGO/CoNiHCF-derived material with remarkable electrocatalytic activity for OER assisted by a strategic preparation methodology, which helped to understand the influence of synthetic parameters and choose their conditions to achieve the optimum OER performance.
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