基于非晶多金属的氧进化反应催化剂。

Discover Materials Pub Date : 2024-01-01 Epub Date: 2024-06-25 DOI:10.1007/s43939-024-00087-5
Zishuai Zhang, Daniela Vieira, Jake E Barralet, Geraldine Merle
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引用次数: 0

摘要

我们需要开发高效、低成本的水分离电催化剂,通过利用太阳能和风能等低能耗、清洁但间歇性的能源产生可持续的氢来储存能源。在此,我们报告了通过简单的低温自燃工艺制备的三种金属(NiCoV)的独特组合所达到的高度持续的低过电位氧进化。这种非晶态多金属氧进化催化剂可通过摩擦化学粒子喷射法稳定地涂覆在不锈钢载体上,从而形成氧进化反应(OER)电极,在 10 mA cm-2 的条件下具有 230 mV 的低过电位和 40 mV dec-1 的低塔菲尔斜率。除了过电势低之外,这种氧进化电催化剂还能保持性能稳定,在技术相关的电流密度下工作 10 小时后,其表面形态没有发生任何改变。考虑到可持续制氢的重要性,这种新型氧进化催化剂的开发为消除水分离反应的一个关键技术瓶颈指明了方向,并为降低成本和减少电解槽制氢技术更广泛应用的障碍提供了途径:在线版本包含补充材料,可查阅 10.1007/s43939-024-00087-5。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Amorphous multimetal based catalyst for oxygen evolution reaction.

The development of efficient, low-cost water splitting electrocatalysts is needed to store energy by generating sustainable hydrogen from low power clean but intermittent energy sources such as solar and wind. Here, we report a highly sustained low overpotential for oxygen evolution reached by the unique combination of three metals (NiCoV) prepared from a simple low temperature auto-combustion process. The amorphous multimetal oxygen evolving catalyst could be stably coated on a stainless-steel support using a tribochemical particle blasting method to create an oxygen evolution reaction (OER) electrode with a low overpotential of 230 mV at 10 mA cm-2 and a low Tafel slope of 40 mV dec-1. In addition to their low overpotential, this oxygen evolving electrocatalyst preserved performance demonstrating a stability after 10 h at the technologically relevant current density and without any surface morphology alteration. Given the importance of sustainable hydrogen production, the development of this new OER catalyst points the way to removing a key technical bottleneck for the water splitting reaction and could offer a route to cost reduction and lowering hurdles to more widespread adaptation of electrolyser technologies for hydrogen production.

Supplementary information: The online version contains supplementary material available at 10.1007/s43939-024-00087-5.

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来源期刊
Discover Materials
Discover Materials materials-
CiteScore
3.30
自引率
0.00%
发文量
10
审稿时长
23 days
期刊介绍: Discover Materials is part of the Discover journal series committed to providing a streamlined submission process, rapid review and publication, and a high level of author service at every stage. It is a broad, open access journal publishing research from across all fields of materials research. Discover Materials covers all areas where materials are activators for innovation and disruption, providing cutting-edge research findings to researchers, academicians, students, and engineers. It considers the whole value chain, ranging from fundamental and applied research to the synthesis, characterisation, modelling and application of materials. Moreover, we especially welcome papers connected to so-called ‘green materials’, which offer unique properties including natural abundance, low toxicity, economically affordable and versatility in terms of physical and chemical properties. They are the activators of an eco-sustainable economy serving all innovation sectors. Indeed, they can be applied in numerous scientific and technological applications including energy, electronics, building, construction and infrastructure, materials science and engineering applications and pollution management and technology. For instance, biomass-based materials can be developed as a source for biodiesel and bioethanol production, and transformed into advanced functionalized materials for applications such as the transformation of chitin into chitosan which can be further used for biomedicine, biomaterials and tissue engineering applications. Green materials for electronics are also a key vector concerning the integration of novel devices on conformable, flexible substrates with free-of-form surfaces for innovative product development. We also welcome new developments grounded on Artificial Intelligence to model, design and simulate materials and to gain new insights into materials by discovering new patterns and relations in the data.
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