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Corrigendum: Ethanol-controlled peroxidation in liquid-anode discharges (2019 J. Phys. D: Appl. Phys. 52 425205)

Journal of Physics D: Applied Physics 2021年第22期54卷

作者： Qiang Chen Jiao Lin Xinyi He Hailong Hu Junshuai Li Qing Xiong Bingyan Chen Zhengyong Song Hai Liu Qing Huo Liu Kostya (Ken) Ostrikov Institute of Electromagnetics and Acoustics Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Department of Electronic Science Xiamen University Xiamen 361005 People's Republic of China College of Physics and Information Engineering Fuzhou University Fuzhou 350116 People's Republic of China Key Laboratory of Special Function Materials and Structure Design of the Ministry of Education and School of Physical Science and Technology Lanzhou University Lanzhou 730000 People's Republic of China State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing University Chongqing 400044 People's Republic of China Department of Mathematics and Physics Hohai University Changzhou 213022 People's Republic of China School of Civil Engineering Guangzhou University Guangzhou 510006 People's Republic of China Department of Electrical and Computer Engineering Duke University Durham NC 27708 United States of America Institute for Future Environments and School of Chemistry Physics and Mechanical Engineering Queensland University of Technology Brisbane QLD 4000 Australia CSIRO-QUT Joint Sustainable Processes and Devices Laboratory Commonwealth Scientific and Industrial Research Organization PO Box 218 Lindfield NSW 2070 Australia

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The First LHAASO Catalog of Gamma-Ray Sources

arXiv

引用

arXiv 2023年

作者： Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, J.T. Cao, Q. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, E.S. Chen, Liang Chen, Lin Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, M.Y. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, X.T. Feng, Y.L. Gabici, S. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Geng, L.S. Giacinti, G. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. He, H.H. He, H.N. He, J.Y. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, Q. Hu, S.C. Huang, D.H. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Huang, Z.C. Ji, X.L. Jia, H.Y. Jia, K. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kang, M.M. Ke, T. Kuleshov, D. Kurinov, K. Li, B.B. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, Jian Li, Jie Li, K. Li, W.L. Li, W.L. Li, X.R. Li, Xin Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Lu, R. Luo, Q. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Mu, H.J. Nan, Y.C. Neronov, A. Ou, Z.W. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qi, Y.Q. Qiao, B.Q. Qin, J.J. Ruffolo, D. Sáiz, A. Semikoz, D. Shao, C.Y. Shao, L. Shchegolev, O. Sheng, X.D. Shu, F.W. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, Q.N. Sun, X.N. Sun, Z.B. Tam, P.H.T. Tang, Q.W. Tang, Z.B. Key Laboratory of Particle Astrophyics Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China Tianfu Cosmic Ray Research Center Sichuan Chengdu610000 China Dublin Institute for Advanced Studies 31 Fitzwilliam Place 2 Dublin Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany State Key Laboratory of Particle Detection and Electronics China University of Science and Technology of China Anhui Hefei230026 China School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Key Laboratory of Cosmic Rays Tibet University Ministry of Education Tibet Lhasa850000 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China School of Physics and Astronomy Sino-French Institute of Nuclear Engineering and Technology Sun Yat-sen University Guangdong Zhuhai519000 China School of Physics Sun Yat-sen University Guangdong Guangzhou510275 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Département de Physique Nucléaire et Corpusculaire Faculté de Sciences Université de Genève 24 Quai Ernest Ansermet Geneva121

We present the first catalog of very-high energy and ultra-high energy gamma-ray sources detected by the Large High Altitude Air Shower Observatory (LHAASO). The catalog was compiled using 508 days of data collected by the Water Cherenkov Detector Array (WCDA) from March 2021 to September 2022 and 933 days of data recorded by the Kilometer Squared Array (KM2A) from January 2020 to September 2022. This catalog represents the main result from the most sensitive large coverage gamma-ray survey of the sky above 1 TeV, covering declination from −20◦ to 80◦. In total, the catalog contains 90 sources with an extended size smaller than 2◦ and a significance of detection at > 5σ. Based on our source association criteria, 32 new TeV sources are proposed in this study. Among the 90 sources, 43 sources are detected with ultra-high energy (E > 100 TeV) emission at > 4σ significance level. We provide the position, extension, and spectral characteristics of all the sources in this catalog. Copyright © 2023, The Authors. All rights reserved.

关键词： Gamma rays

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一种静态漏电与NBTI效应协同优化模型

一种静态漏电与NBTI效应协同优化模型

引用

第七届中国测试学术会议

作者： Jin Song 靳松 Hart Yinhe 韩银和 Liu Li 刘立 Department of Electronic and Communication Engineering School of Electrical and Electronic Engineer 华北电力大学电气与电子工程学院电子与通信工程系保定071003 State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of 中国科学院计算技术研究所计算机体系结构国家重点实验室北京100190

随着制造工艺的不断进步，老化效应导致的动态参数偏差和漏电给集成电路的可靠性带来了严峻挑战。目前关于电路老化和漏电的协同优化方法或者以侵入式方法实现，或者采用输入向量控制方法(IVC)。然而，侵入式方法会增加设计复杂度，并... 详细信息

随着制造工艺的不断进步，老化效应导致的动态参数偏差和漏电给集成电路的可靠性带来了严峻挑战。目前关于电路老化和漏电的协同优化方法或者以侵入式方法实现，或者采用输入向量控制方法(IVC)。然而，侵入式方法会增加设计复杂度，并引入较大的时延和面积开销;而IVC方法则会随着电路规模的增大逐渐失去其优化效果。本文提出了一个新的协同优化模型以最小化电路处于待机模式时由于NBTI效应导致的时延偏差和静态漏电。提出的协同优化模型将NBTI效应导致的时延偏差和电路的平均静态漏电统一模型化为占空比的函数。将此模型应用于非线性规划中可以识别出电路处于待机模式时内部节点上的最优占空比集合。提出的协同优化模型通过一个多输入向量控制方法进行验证。实验结果表明，提出的模型可以有效地应用于NBTI效应和电路静态漏电的协同优化中。

关键词：集成电路负偏置温度不稳定性静态漏电协同优化模型

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Room-temperature Magnetocapacitance Spanning 97K Hysteresis in Molecular Material

引用

Angewandte Chemie 2024年第8期137卷

作者： Ling-Ao Gui Jiawei Chen Yi-Fan Zhang Long-He Li Dr. Jian-Rong Li Dr. Zhao-Bo Hu Prof. Shi-Yong Zhang Dr. Jinlei Zhang Zhenyi Zhang Prof. Heng-Yun Ye Dr. Yan Peng Prof. Jing Ma Prof. You Song Chaotic Matter Science Research Center Faculty of Materials Metallurgy and Chemistry Jiangxi University of Science and Technology Ganzhou 341000 P. R. China These authors contributed equally to this work Key Laboratory of Mesoscopic Chemistry of Ministry of Education School of Chemistry and Chemical Engineering Nanjing University Nanjing 210023 P. R. China Jiangxi Provincial Key Laboratory of Functional Crystalline Materials Chemistry Jiangxi University of Science and Technology Ganzhou 341000 P. R. China School of Chemistry and Chemical Engineering Gannan Normal University Ganzhou 341000 P. R. China School of Physical Science and Technology Suzhou University of Science and Technology Suzhou 215009 P. R. China Bruker Scientific Instruments (Shanghai) Co. Ltd Shanghai 200120 P. R. China State Key Laboratory of Coordination Chemistry School of Chemistry and Chemical Engineering Nanjing University Nanjing 210023 P. R. China

Magnetic capacitor, as a new type of device, has broad application prospects in fields such as magnetic field sensing, magnetic storage, magnetic field control, power electronics and so on. Traditional magnetic capacitors are mostly assembled by magnetic and capacitive materials. Magnetic capacitor made of a single material with intrinsic properties is very rare. This intrinsic property is magnetocapacitance (MC). The studies on MC effect have mainly focused on metal oxides so far. No study was reported in molecular materials. Herein, two complexes: (CETAB) 2 [CuCl 4 ] ( 1 ) and (CETAB) 2 [CuBr 4 ] ( 2 ) (CETAB=(2-chloroethyl)trimethylammonium) are reported. There exist strong H−Br and Br−Br interactions and other weak interactions in complex 2 , so the phase transition energy barrier is high, resulting in the widest thermal hysteresis loop on a molecular level to date. Furthermore, complexes 1 and 2 show large MC parameters of 0.247 and 1.614, respectively, which is the first time to observe MC effect in molecular material.

关键词： Magnetocapacitance Large Thermal Hysteresis Molecular Materials Magnetic Bistability Room Temperature

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Erratum: “Influence of heavy metal materials on magnetic properties of Pt/Co/heavy metal tri-layered structures” [Appl. Phys. Lett. 110, 012405 (2017)]

引用

Applied Physics Letters 2017年第10期110卷

作者： Boyu Zhang Anni Cao Junfeng Qiao Minghong Tang Kaihua Cao Xiaoxuan Zhao Sylvain Eimer Zhizhong Si Na Lei Zhaohao Wang Xiaoyang Lin Zongzhi Zhang Mingzhong Wu Weisheng Zhao 1Fert Beijing Research Institute Beihang University Beijing 100191 China 2Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) Beihang University Beijing 100191 China 3School of Electronic and Information Engineering Beihang University Beijing 100191 China 4Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education) Department of Optical Science and Engineering Fudan University Shanghai 200433 China 5Department of Physics Colorado State University Fort Collins Colorado 80523 USA

The following errors appeared in the above article.1

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Intelligent non-invasive elderly fall monitoring by designing software defined radio frequency sensing system

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Digital Communications and Networks 2024年

作者： Adeel Akram Muhammad Bilal Khan Najah Abed Abu Ali Qixing Zhang Awais Ahmad Muhammad Shahid Iqbal Syed Atif Moqurrab Laboratory for Intelligent Sensing and Computing Institute of Advanced Technology University of Science and Technology of China Hefei 230031 Anhui China State Key Laboratory of Fire Science University of Science and Technology of China Hefei 230026 Anhui China College of IT United Arab Emirates University Al-Ain 15551 United Arab Emirates Department of Electrical and Computer Engineering COMSATS University Islamabad Attock 43600 Pakistan College of Computer and Information Sciences Imam Muhammad Ibn Saud Islamic University (IMSIU) Riyadh 11432 Kingdom of Saudi Arabia Department of Computer Science and Information Technology Women University of AJ&K Bagh Pakistan School of Computing Gachon University 1342 Seongnam-daero Sujeong-gu Seongnam-si 13120 Korea

The global increase in life expectancy poses challenges related to the safety and well-being of the elderly population, especially in relation to falls. While falls can lead to significant cognitive impairments, timely intervention can mitigate their adverse effects. In this context, the need for non-invasive, efficient monitoring systems becomes paramount. Although wearable sensors have gained traction for monitoring health activities, they may cause discomfort during prolonged use, especially for the elderly. To address this issue, we present an intelligent, non-invasive Software-Defined Radio Frequency (SDRF) sensing system, tailored red for monitoring elderly people's falls during routine activities. Harnessing the power of deep learning and machine learning, our system processes the Wireless Channel state Information (WCSI) generated during regular and fall activities. By employing sophisticated signal processing techniques, the system captures unique patterns that distinguish falls from normal activities. In addition, we use statistical features to streamline data processing, thereby optimizing the computational efficiency of the system. Our experiments, conducted for a typical home environment while using treadmill, demonstrate the robustness of the system. The results show high classification accuracies of 92.5%, 95.1%, and 99.8% for three Artificial Intelligence (AI) algorithms. Notably, the SDRF-based approach offers flexibility, cost-effectiveness, and adaptability through software modifications, circumventing the need for hardware overhaul. This research attempts to bridge the gap in RF-based sensing for elderly fall monitoring, providing a solution that combines the benefits of non-invasiveness with the precision of deep learning and machine learning.

关键词： AI Elderly falls Intelligent learning SDRF WCSI

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Meeting the Challenges of Modeling Astrophysical Thermonuclear Explosions: Castro, Maestro, and the AMReX Astrophysics Suite

引用

Journal of Physics: Conference Series 2018年第1期1031卷

作者： M. Zingale A. S. Almgren M. G. Barrios Sazo V. E. Beckner J. B. Bell B. Friesen A. M. Jacobs M. P. Katz C. M. Malone A. J. Nonaka D. E. Willcox W. Zhang Department of Physics and Astronomy Stony Brook University Stony Brook NY 11794-3800 USA Center for Computational Sciences and Engineering Lawrence Berkeley National Lab Berkeley CA 94720 USA National Energy Research Scientific Computing Center Lawrence Berkeley National Lab Berkeley CA 94720 USA Department of Physics and Astronomy Michigan State University East Lansing Michigan 48824 USA NVIDIA Corporation 2788 San Tomas Expressway Santa Clara CA 95050 USA Los Alamos National Laboratory Los Alamos NM 87545 USA

We describe the AMReX suite of astrophysics codes and their application to modeling problems in stellar astrophysics. Maestro is tuned to efficiently model subsonic convective flows while Castro models the highly compressible flows associated with stellar explosions. Both are built on the block-structured adaptive mesh refinement library AMReX. Together, these codes enable a thorough investigation of stellar phenomena, including Type Ia supernovae and X-ray bursts. We describe these science applications and the approach we are taking to make these codes performant on current and future many-core and GPU-based architectures.

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Author Correction: A genomic catalogue of soil microbiomes boosts mining of biodiversity and genetic resources

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Nature communications 2023年第1期14卷 8079页

作者： Bin Ma Caiyu Lu Yiling Wang Jingwen Yu Kankan Zhao Ran Xue Hao Ren Xiaofei Lv Ronghui Pan Jiabao Zhang Yongguan Zhu Jianming Xu Institute of Soil and Water Resources and Environmental Science College of Environmental and Resource Sciences Zhejiang University Hangzhou 310058 China. Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment Zhejiang University Hangzhou 310058 China. ZJU-Hangzhou Global Scientific and Technological Innovation Center Hangzhou 311200 China. Department of Environmental Engineering China Jiliang University Hangzhou 310018 China. State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil Science Chinese Academy of Sciences Nanjing 210008 China. Research Center for Eco-environmental Sciences Chinese Academy of Sciences Beijing 100085 China. Institute of Soil and Water Resources and Environmental Science College of Environmental and Resource Sciences Zhejiang University Hangzhou 310058 China. jmxu@***. Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment Zhejiang University Hangzhou 310058 China. jmxu@***.

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A foundation model for atomistic materials chemistry

arXiv

引用

arXiv 2023年

作者： Batatia, Ilyes Benner, Philipp Chiang, Yuan Elena, Alin M. Kovács, Dávid P. Riebesell, Janosh Advincula, Xavier R. Asta, Mark Avaylon, Matthew Baldwin, William J. Berger, Fabian Bernstein, Noam Bhowmik, Arghya Blau, Samuel M. Cărare, Vlad Darby, James P. De, Sandip Pia, Flaviano Della Deringer, Volker L. Elijošius, Rokas El-Machachi, Zakariya Falcioni, Fabio Fako, Edvin Ferrari, Andrea C. Genreith-Schriever, Annalena George, Janine Goodall, Rhys E.A. Grey, Clare P. Grigorev, Petr Han, Shuang Handley, Will Heenen, Hendrik H. Hermansson, Kersti Holm, Christian Hofmann, Stephan Jaafar, Jad Jakob, Konstantin S. Jung, Hyunwook Kapil, Venkat Kaplan, Aaron D. Karimitari, Nima Kermode, James R. Kroupa, Namu Kullgren, Jolla Kuner, Matthew C. Kuryla, Domantas Liepuoniute, Guoda Margraf, Johannes T. Magdău, Ioan-Bogdan Michaelides, Angelos Harry Moore, J. Naik, Aakash A. Niblett, Samuel P. Norwood, Sam Walton O’Neill, Niamh Ortner, Christoph Persson, Kristin A. Reuter, Karsten Rosen, Andrew S. Schaaf, Lars L. Schran, Christoph Shi, Benjamin X. Sivonxay, Eric Stenczel, Tamás K. Svahn, Viktor Sutton, Christopher Swinburne, Thomas D. Tilly, Jules van der Oord, Cas Vargas, Santiago Varga-Umbrich, Eszter Vegge, Tejs Vondrák, Martin Wang, Yangshuai Witt, William C. Zills, Fabian Csányi, Gábor Engineering Laboratory University of Cambridge Trumpington St and JJ Thomson Ave Cambridge United Kingdom Berlin Germany Department of Materials Science and Engineering University of California BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Mathematics Department University of British Columbia 1984 Mathematics Rd VancouverBCV6T 1Z2 Canada Institute of Condensed Matter Theory and Solid State Optics Friedrich Schiller University Jena Germany Molecular Foundry Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Bayreuth Germany Fritz-Haber-Institute of the Max-Planck-Society Berlin Germany Energy Technologies Area Lawrence Berkeley National Laboratory BerkeleyCA94720 United States U. S. Naval Research Laboratory WashingtonDC20375 United States Yusuf Hamied Department of Chemistry University of Cambridge Lensfield Road Cambridge United Kingdom Cavendish Laboratory University of Cambridge J. J. Thomson Ave Cambridge United Kingdom Department of Materials Science and Metallurgy University of Cambridge 27 Charles Babbage Road CambridgeCB3 0FS United Kingdom Chemix Inc. SunnyvaleCA94085 United States Inorganic Chemistry Laboratory Department of Chemistry University of Oxford OxfordOX1 3QR United Kingdom Scientific Computing Department Science and Technology Facilities Council Daresbury Laboratory Keckwick Lane DaresburyWA4 4AD United Kingdom BASF SE Carl-Bosch-Straße 38 Ludwigshafen67056 Germany Kavli Institute for Cosmology University of Cambridge Madingley Road CambridgeCB3 0HA United Kingdom Department of Chemistry and Biochemistry University of South Carolina South Carolina29208 United States Lennard-Jones Centre University of Cambridge Trinity Ln CambridgeCB2 1TN United Kingdom Institute for Computational Physics University of Stuttgart Stuttgart70569 Germany Department of Chemistry–Ångström Uppsala University Box 538 Uppsala

Machine-learned force fields have transformed the atomistic modelling of materials by enabling simulations of ab initio quality on unprecedented time and length scales. However, they are currently limited by: (i) the significant computational and human effort that must go into development and validation of potentials for each particular system of interest;and (ii) a general lack of transferability from one chemical system to the next. Here, using the state-of-the-art MACE architecture we introduce a single general-purpose ML model, trained on a public database of 150k inorganic crystals, that is capable of running stable molecular dynamics on molecules and materials. We demonstrate the power of the MACE-MP-0 model — and its qualitative and at times quantitative accuracy — on a diverse set problems in the physical sciences, including the properties of solids, liquids, gases, chemical reactions, interfaces and even the dynamics of a small protein. The model can be applied out of the box and as a starting or "foundation model" for any atomistic system of interest and is thus a step towards democratising the revolution of ML force fields by lowering the barriers to entry. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

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Zeolite‐Encaged Pd–Mn Nanocatalysts for CO2Hydrogenation and Formic Acid Dehydrogenation

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Angewandte Chemie 2020年第45期132卷

作者： Qiming Sun Benjamin W. J. Chen Ning Wang Qian He Albert Chang Chia‐Min Yang Hiroyuki Asakura Tsunehiro Tanaka Max J. Hülsey Chi‐Hwa Wang Jihong Yu Ning Yan NUS Environmental Research Institute (NERI) National University of Singapore 138602 Singapore Singapore Department of Chemical and Biomolecular Engineering National University of Singapore 4 Engineering Drive 4 117585 Singapore Singapore Institute of High Performance Computing Agency for Science Technology and Research 1 Fusionopolis Way #16-16 Connexis Singapore 138632 Singapore State Key Laboratory of Inorganic Synthesis and Preparative Chemistry College of Chemistry International Center of Future Science Jilin University Changchun 130012 P. R. China Department of Materials Science and Engineering National University of Singapore 9 Engineering Drive 1 Singapore 117575 Singapore Department of Chemistry National Tsing Hua University Hsinchu 30013 Taiwan Department of Molecular Engineering Graduate School of Engineering Kyoto University Kyotodaigaku Katsura Nishikyo-ku Kyoto 615-8510 Japan

A CO 2 ‐mediated hydrogen storage energy cycle is a promising way to implement a hydrogen economy, but the exploration of efficient catalysts to achieve this process remains challenging. Herein, sub‐nanometer Pd–Mn clusters were encaged within silicalite‐1 (S‐1) zeolites by a ligand‐protected method under direct hydrothermal conditions. The obtained zeolite‐encaged metallic nanocatalysts exhibited extraordinary catalytic activity and durability in both CO 2 hydrogenation into formate and formic acid (FA) dehydrogenation back to CO 2 and hydrogen. Thanks to the formation of ultrasmall metal clusters and the synergic effect of bimetallic components, the PdMn 0.6 @S‐1 catalyst afforded a formate generation rate of 2151 mol formate mol Pd −1 h −1 at 353 K, and an initial turnover frequency of 6860 mol mol Pd −1 h −1 for CO‐free FA decomposition at 333 K without any additive. Both values represent the top levels among state‐of‐the‐art heterogeneous catalysts under similar conditions. This work demonstrates that zeolite‐encaged metallic catalysts hold great promise to realize CO 2 ‐mediated hydrogen energy cycles in the future that feature fast charge and release kinetics.

关键词： CO2 hydrogenation formic acid heterogeneous catalysis hydrogen storage zeolites

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