Al-Ghamdi Azza A, Sami Abdul, El-Bahy Salah M, Alsabban Merfat M, Sajjad Wajid, Al-Sulami Ahlam I, Waseem Fazal Muhammad, Aldahiri Reema H, Al-Sulami Fatimah Mohammad H, Khan Muhammad Ali, Akhtar Naeem
College of Science, Department of Chemistry, University of Jeddah, 21589, Jeddah, Saudi Arabia.
Institute of Chemical Sciences, Bahauddin Zakariya University (BZU), Multan, 60800, Pakistan.
Sci Rep. 2025 Mar 28;15(1):10677. doi: 10.1038/s41598-025-95130-7.
Wide range of noble metal free bimetallic and trimetallic based electrocatalysts have been synthesized to develop efficient oxygen evolution reaction (OER) systems to-date, however, to determine which metal part of bimetallic and trimetallic electrocatalysts plays a significant role in controlling OER efficacy remains very challenging. To address this issue, herein we have employed machine learning (ML) for the first time to determine OER efficacy controlling metal element, thus leading to the development of an optimized bimetallic electrocatalyst. Briefly, we have designed a novel, simple ML optimized sustainable OER electrocatalyst based on CoO/NiO popsicle sticks (CNPS) infused polyaniline/cellulose acetate (a biopolymer) (PNCA) electrospun nanofibers supported on nickel foam (NF). ML optimized CNPS infused PNCA (CNPS@PNCA) electrode offers maximum and homogenous exposition of active sites and shows high OER activity by exhibiting low onset potential (1.41 V vs. RHE), overpotential (237 mV at 10 mA cm) and Tafel slope of 62.1 mV dec. Additionally, it shows a better stability of more than 100 h and is consistent with the reported literature.
迄今为止，人们已经合成了多种不含贵金属的双金属和三金属基电催化剂，以开发高效的析氧反应（OER）体系，然而，要确定双金属和三金属电催化剂中的哪种金属部分在控制OER效率方面起重要作用仍然非常具有挑战性。为了解决这个问题，我们首次采用机器学习（ML）来确定控制OER效率的金属元素，从而开发出一种优化的双金属电催化剂。简而言之，我们基于负载在泡沫镍（NF）上的CoO/NiO棒冰棒（CNPS）注入聚苯胺/醋酸纤维素（一种生物聚合物）（PNCA）电纺纳米纤维，设计了一种新型、简单且经ML优化的可持续OER电催化剂。经ML优化注入CNPS的PNCA（CNPS@PNCA）电极提供了活性位点的最大且均匀暴露，并通过展现低起始电位（相对于可逆氢电极（RHE）为1.41 V）、过电位（在10 mA cm时为237 mV）和62.1 mV dec的塔菲尔斜率，显示出高OER活性。此外，它表现出超过100小时的更好稳定性，并且与已发表的文献一致。
