Chem Catalysis最新文献_第3页

Controllable partial self-sacrifice of metal-organic frameworks for enhancing nitrate electroreduction to ammonia 金属有机骨架的可控部分自我牺牲促进硝酸盐电还原制氨

IF 9.4

Chem Catalysis Pub Date : 2025-08-08 DOI: 10.1016/j.checat.2025.101484

Bin Lei, Xiao Liu, Bo Li, Haolin Lu, Xing-Yue He, Xiaowen Wang, Guankui Long, Jian-Gong Ma, Peng Cheng

{"title":"Controllable partial self-sacrifice of metal-organic frameworks for enhancing nitrate electroreduction to ammonia","authors":"Bin Lei, Xiao Liu, Bo Li, Haolin Lu, Xing-Yue He, Xiaowen Wang, Guankui Long, Jian-Gong Ma, Peng Cheng","doi":"10.1016/j.checat.2025.101484","DOIUrl":"https://doi.org/10.1016/j.checat.2025.101484","url":null,"abstract":"Electrochemical nitrate reduction to ammonia (NO3RR) is the most promising pathway for the value-added conversion of nitrate. However, the NO3RR process involves the transfer of multi-electrons and protons and hence suffers from slow kinetics, leading to an urgent need to develop high-performance NO3RR catalysts. Here, we prepare ultrafine Cu2O particles in situ generated and encapsulated in metal-organic frameworks (MOFs) containing coordination-unsaturated Cu2+ nodes by the controlled self-sacrifice of a selected part of the framework. The composite catalyst achieves the impressive catalytic performance for NO3RR, with an NH3 yield rate of 6.35 mmol h−1 mgcat−1 and corresponding Faraday efficiency of 98.6%. Density functional theory (DFT) calculations demonstrate that the synergistic effect between unsaturated Cu2+ nodes and nanoparticles markedly decreases the potential energy of all intermediates, thereby facilitating an efficient conversion of nitrate to ammonia.","PeriodicalId":53121,"journal":{"name":"Chem Catalysis","volume":"35 1","pages":""},"PeriodicalIF":9.4,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144797548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unitized bifunctional electrochemical hydrogen pumps for hydrogen-enriched natural gas 富氢天然气用一体化双功能电化学氢泵

IF 9.4

Chem Catalysis Pub Date : 2025-08-04 DOI: 10.1016/j.checat.2025.101464

Manjeet Chhetri, Prashant Sharan, Daniel Leonard, Sandip Maurya, Cortney Kreller, Yu Seung Kim

引用次数: 0

Surface structure effects on the methylcyclohexane dehydrogenation over Ni catalysts predicted by density functional theory 密度泛函理论预测Ni催化剂表面结构对甲基环己烷脱氢反应的影响

IF 9.4

Chem Catalysis Pub Date : 2025-08-04 DOI: 10.1016/j.checat.2025.101462

Wenqiang Yang, Bhawana Rayamajhi, Mubarak Bello, Andreas Heyden

引用次数: 0

Scaling up membrane electrode assemblies for industrial applications 扩大膜电极组件的工业应用

IF 9.4

Chem Catalysis Pub Date : 2025-07-28 DOI: 10.1016/j.checat.2025.101463

Rongfu Hong, Lixin Xing, Fusheng Huang, Yuanmei Chen, Pinqing Li, Xiaoyi Fang, Mingquan Zhao, Hong Ren, Zhun Dong, Yunsong Yang, Lei Du, Siyu Ye

{"title":"Scaling up membrane electrode assemblies for industrial applications","authors":"Rongfu Hong, Lixin Xing, Fusheng Huang, Yuanmei Chen, Pinqing Li, Xiaoyi Fang, Mingquan Zhao, Hong Ren, Zhun Dong, Yunsong Yang, Lei Du, Siyu Ye","doi":"10.1016/j.checat.2025.101463","DOIUrl":"https://doi.org/10.1016/j.checat.2025.101463","url":null,"abstract":"Membrane electrode assemblies (MEAs) are critical for hydrogen energy technologies, such as fuel cells and electrolyzers, yet their industrialization remains complex. Key challenges include the high cost of platinum-group metal (PGM) catalysts, performance gaps between lab-scale and industrial devices due to disparities in transport dynamics, and the need to optimize mass transport at triple-phase boundaries. Manufacturing hurdles involve unstable catalyst inks, difficulties with precision coating, and thermal and mechanical instability during hot pressing. PGM scarcity and weak links between academia and industry further impede progress. To cut costs and close performance gaps, the field can pivot toward non-precious-metal catalysts, establish closed-loop PGM recycling, and coordinate cross-disciplinary process optimization. Concurrently, it is essential to acknowledge the shifting competitive dynamics of MEAs in the energy market and strategically emphasize their strengths, such as fuel cells’ advantage over lithium-ion batteries in heavy-duty transport applications, to enhance market penetration and accelerate scalable deployment.","PeriodicalId":53121,"journal":{"name":"Chem Catalysis","volume":"68 1","pages":""},"PeriodicalIF":9.4,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144715650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine learning approaches for transition state prediction 过渡状态预测的机器学习方法

IF 9.4

Chem Catalysis Pub Date : 2025-07-23 DOI: 10.1016/j.checat.2025.101458

Xingyu Wang, Yu Mao, Ziyun Wang

{"title":"Machine learning approaches for transition state prediction","authors":"Xingyu Wang, Yu Mao, Ziyun Wang","doi":"10.1016/j.checat.2025.101458","DOIUrl":"https://doi.org/10.1016/j.checat.2025.101458","url":null,"abstract":"Searching for a transition state (TS) is crucial in understanding chemical reaction mechanisms and kinetics. While traditional computational methods, including single-ended and double-ended approaches, have provided valuable insights, they face significant computational cost and scalability limitations. This review comprehensively examines conventional computational approaches and the rapidly emerging machine learning (ML) methods for TS searching, highlighting the significant acceleration in ML method development since 2020. We first analyze traditional computational methods, discussing their theoretical foundations and practical limitations. We then systematically review available TS datasets that enable ML applications. The review explores the evolution of ML approaches from traditional methods like random forest and kernel ridge regression to advanced architectures such as graph neural networks, tensor field networks, and generative models. We examine current challenges, including data scarcity, computational constraints, and validation standards, while highlighting promising future directions. This comprehensive analysis provides insights into the field’s current state and outlines potential pathways for advancing TS searching methodologies.","PeriodicalId":53121,"journal":{"name":"Chem Catalysis","volume":"32 1","pages":""},"PeriodicalIF":9.4,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144684919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Toward revealing T-site distributions and resultant catalytic implications in MFI zeolites 揭示t位分布及其在MFI沸石中的催化意义

IF 9.4

Chem Catalysis Pub Date : 2025-07-17 DOI: 10.1016/j.checat.2025.101461

Cole W. Hullfish, Michele L. Sarazen

引用次数: 0

Radicals retain their memory in cross-coupling 自由基在交叉耦合中保持记忆

IF 9.4

Chem Catalysis Pub Date : 2025-07-17 DOI: 10.1016/j.checat.2025.101439

Tao Li, Haohua Huo

引用次数: 0

Scalable single-atom catalyst for high-performing and durable water electrolyzers 可扩展的单原子催化剂，用于高性能和耐用的水电解槽