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高通量筛选双离子氧化还原材料用于化学循环氨合成。

High-Throughput Screening of Bicationic Redox Materials for Chemical Looping Ammonia Synthesis.

机构信息

Materials and Manufacturing Futures Institute, School of Material Science and Engineering, University of New South Wales, Sydney, New South Wales, 2052, Australia.

出版信息

Adv Sci (Weinh). 2022 Sep;9(27):e2202811. doi: 10.1002/advs.202202811. Epub 2022 Jul 24.

Abstract

Ammonia recently has gained increasing attention as a carrier for the efficient and safe usage of hydrogen to further advance the hydrogen economy. However, there is a pressing need to develop new ammonia synthesis techniques to overcome the problem of intense energy consumption associated with the widely used Haber-Bosch process. Chemical looping ammonia synthesis (CLAS) is a promising approach to tackle this problem, but the ideal redox materials to drive these chemical looping processes are yet to be discovered. Here, by mining the well-established MP database, the reaction free energies for CLAS involving 1699 bicationic inorganic redox pairs are screened to comprehensively investigate their potentials as efficient redox materials in four different CLAS schemes. A state-of-the-art machine learning strategy is further deployed to significantly widen the chemical space for discovering the promising redox materials from more than half a million candidates. Most importantly, using the three-step H O-CL as an example, a new metric is introduced to determine bicationic redox pairs that are "cooperatively enhanced" compared to their corresponding monocationic counterparts. It is found that bicationic compounds containing a combination of alkali/alkaline-earth metals and transition metal (TM)/post-TM/metalloid elements are compounds that are particularly promising in this respect.

摘要

氨作为一种载体,在提高氢气使用效率和安全性、进一步推进氢能经济方面引起了越来越多的关注。然而,开发新的氨合成技术以克服广泛使用的哈伯-博世工艺所带来的高能耗问题迫在眉睫。化学循环氨合成(CLAS)是解决这一问题的一种很有前途的方法,但要实现这一目标,仍需要发现理想的氧化还原材料来驱动这些化学循环过程。在这里,我们通过挖掘成熟的 MP 数据库,对涉及 1699 个双阳离子无机氧化还原对的 CLAS 的反应自由能进行了筛选,以全面研究它们在四种不同的 CLAS 方案中作为高效氧化还原材料的潜力。进一步部署了最先进的机器学习策略,以从超过 50 万个候选物中显著拓宽寻找有前途的氧化还原材料的化学空间。最重要的是,以三步 H2O-CL 为例,引入了一种新的度量标准来确定与相应的单阳离子相比具有“协同增强”作用的双阳离子氧化还原对。结果发现,含有碱/碱土金属与过渡金属(TM)/后过渡金属(post-TM)/类金属元素组合的双阳离子化合物在这方面特别有前途。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/733f/9507380/11fd2343f717/ADVS-9-2202811-g005.jpg

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