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One-step electrodeposition of FeMn binary sulfide electrocatalysts on nickel foam for oxygen evolution reaction

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Author(s)
Islam, Md. ShafiulKhan, Md. RakibAsaduzzaman, AbuMatin, Mohammad A.Jeon, Young-HoonYousuf, Mohammad A.Kim, Bong-JoongAhammad, A.J. Saleh
Type
Article
Citation
Inorganic Chemistry Communications, v.191, no.P2
Issued Date
2026-09
Abstract
To move alkaline water-splitting technologies forward, it is important to make oxygen evolution reaction (OER) electrocatalysts that are both cheap and long-lasting. Herein, we report a binary iron–manganese sulfide (FeMnS) nanosheet electrode directly grown on nickel foam via a one-step potentiostatic electrodeposition route. Unlike previous FeMn sulfide studies employing potentiodynamic voltage scanning or multistep heterostructure construction, the present approach enables controlled single-phase FeMn sulfide growth with a Fe:Mn ratio of 2:1, yielding a highly interconnected nanosheet architecture with a large electrochemical surface area. XPS analyses before and after OER operation reveal the formation of FeMn binary sulfide and its partial surface reconstruction into catalytically active oxyhydroxide species during alkaline electrolysis. Electrochemical testing shows an overpotential of 321mV at 50mA·cm−2 and 384mV at 100mA·cm−2 in 1.0M KOH. The catalyst exhibits favorable OER kinetics together with excellent durability, maintaining almost 100% of the current for 10h of continuous use with very little degradation. After 200cycles, it shows only 8mV gap in overpotential, confirming its promising OER performance. The computational study indicates that FeMnS/NF exhibits enhanced stability and an intermediate band gap arising from partial metal–chalcogen d–p hybridization, in contrast to the corresponding monometallic sulfides. This study offers a straightforward and scalable method for producing high-performance FeMn sulfide OER electrocatalysts and enhances the comprehension of structure–activity relationships in electrodeposited bimetal sulfide systems. © 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Publisher
Elsevier B.V.
ISSN
1387-7003
DOI
10.1016/j.inoche.2026.117114
URI
https://scholar.gist.ac.kr/handle/local/34280
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