Kotob Esraa, Awad Mohammed Mosaad, Umar Mustapha, Taialla Omer Ahmed, Hussain Ijaz, Alsabbahen Shaima' Ibrahim, Alhooshani Khalid, Ganiyu Saheed A
Department of Chemistry, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.
Department of Chemical Sciences, Faculty of Science and Computing, North-Eastern University, P. M. B. 0198, Gombe, Gombe State, Nigeria.
iScience. 2025 Mar 28;28(6):112306. doi: 10.1016/j.isci.2025.112306. eCollection 2025 Jun 20.
SACs are transforming CO conversion and energy applications due to their high catalytic efficiency, unique electronic structures, and maximal atom utilization. They have shown great promise in CO electroreduction, hydrogenation, and dry reforming, yet challenges remain in their synthesis, stability, and scalable production. This review explores advances in SAC design, support interactions, and electronic tuning to enhance catalytic performance. It also analyzed state-of-the-art characterization techniques used to probe SAC structures and reaction mechanisms. Machine learning is emerging as a powerful tool for predicting SAC stability and optimizing reaction pathways. By examining recent breakthroughs and existing limitations, this work provides insights into the future of SACs in energy applications and CO utilization, highlighting their role in sustainable chemical transformations and carbon-neutral technologies.
单原子催化剂(SACs)因其高催化效率、独特的电子结构和最大的原子利用率,正在改变一氧化碳(CO)转化和能源应用。它们在CO电还原、加氢和干重整方面已展现出巨大潜力,但在其合成、稳定性和可规模化生产方面仍存在挑战。本综述探讨了SAC设计、载体相互作用和电子调谐方面的进展,以提高催化性能。它还分析了用于探测SAC结构和反应机理的先进表征技术。机器学习正成为预测SAC稳定性和优化反应途径的强大工具。通过研究近期的突破和现有局限性,这项工作为SACs在能源应用和CO利用方面的未来发展提供了见解,突出了它们在可持续化学转化和碳中和技术中的作用。
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