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Rational Manipulation of Stacking Arrangements in Three-Dimensional Zeolites Built from Two-Dimensional Zeolitic Nanosheets

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Angewandte Chemie 2020年第45期132卷

作者： Xu L. Ma T. Shen Y. Wang Y. Han L. Chaittisilp W. Yokoi T. Sun J. Wakihara T. Okubo T. Department of Chemical System Engineering The University of Tokyo Tokyo 113-8656 Japan College of Chemistry and Molecular Engineering Peking University Beijing 100871 China Chemical Resources Laboratory Tokyo Institute of Technology Yokohama 226-8503 Japan School of Chemical Science and Engineering Tongji University Shanghai 200092 China Research and Services Division of Materials Data and Integrated System National Institute for Materials Sciences (NIMS) Ibaraki 305-0044 Japan

Unit-cell-thin zeolitic nanosheets have emerged as fascinating materials for catalysis and separation. The controllability of nanosheet stacking is extremely challenging in the chemistry of two-dimensional zeolitic materials. To date, the organization of zeolitic nanosheets in hydrothermal synthesis has been limited by the lack of tunable control over the guest–host interactions between organic structure-directing agents (OSDAs) and zeolitic nanosheets. A direct synthetic methodology is reported that enables systematic manipulation of the aluminosilicate MWW-type nanosheet stacking. Variable control of guest–host interactions is rationally achieved by synergistically altering the charge density of OSDAs and synthetic silica-to-alumina composition. These finely controlled interactions allow successful preparation of a series of three-dimensional (3D) zeolites, with MWW-layer stacking in wide ranges from variably disorder to fully ordered, leading to tunable catalytic activity in the cracking reaction. These results highlight unprecedented opportunities to modulate zeolitic nanosheets arrangement in 3D zeolites whose structure can be tailored for catalysis and separation. © 2020 Wiley-VCH GmbH

关键词： catalysis crystal growth layered compounds structure transformations

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Quantifying material stocks in long-lived products: Challenges and improvements for informing sustainable resource use strategies

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Resources, Conservation and Recycling 2025年 221卷

作者： Jan Streeck André Baumgart Helmut Haberl Fridolin Krausmann Bowen Cai Tomer Fishman Maud Lanau Peter Berrill Zhi Cao Sebastiaan Deetman David Frantz Volker Krey Alessio Mastrucci Alessio Miatto Stefan Pauliuk Lola Sylvie Annie Rousseau Shoshanna Saxe Danielle Densley Tingley Gamze Ünlü Dominik Wiedenhofer BOKU University Institute of Social Ecology Department of Economics and Social Sciences Schottenfeldgasse 29 1070 Vienna Austria Wuhan University School of Remote Sensing and Information Engineering Wuhan Hubei Province 430079 China Leiden University Institute of Environmental Sciences (CML) 2333 CC Leiden Netherlands Chalmers University of Technology Department of Architecture and Civil Engineering Research Area Sustainable Built Environments Gothenburg 412 96 Sweden Universität Berlin Sustainability Economics of Human Settlements Berlin 10623 Germany College of Environmental Science and Engineering Nankai University Tianjin 300350 China School of Earth System Science Tianjin University Tianjin 300072 China Trier University Geoinformatics -Spatial Data Science Trier Rhineland-Palatinate 54296 Germany International Institute for Applied Systems Analysis (IIASA) Energy Climate and Environment (ECE) Program Schlossplatz 1 2361 Laxenburg Austria Commonwealth Scientific and Industrial Research Organisation (CSIRO) Canberra 2601 ACT Australia University of Freiburg Faculty of Environment and Natural Resources Freiburg Baden-Wuerttemberg D-79102 Germany Norwegian University of Science and Technology (NTNU) Industrial Ecology Programme Department of Energy and Process Engineering Trondheim 7034 Norway University of Toronto Department of Civil and Mineral Engineering Toronto Ontario M5S 1A1 Canada University of Sheffield School of Mechanical Aerospace and Civil Engineering Mappin Street Sheffield S1 3JD United Kingdom

Material stocks in long-lived products require over half of the annual global resource extraction for their construction and maintenance, and lock in energy use through their technical and geospatial characteristics. A thorough understanding of material stocks is therefore essential to inform sustainable resource use strategies. However, despite substantial advances in material stock research in recent decades, their robust quantification remains challenging and bears considerable uncertainties. We assess the (dis)agreement of material stock estimates from 32 recent studies across global, national, and urban scales, and propose recommendations for future work. Overall, we observe medium to high divergences between studies estimating the same material stocks. For end-use categories that aggregate multiple material stocks (e.g., buildings), most global-level estimates show divergences within 140 %. At the national level, most estimates for the USA diverge by <210 %, while those for China by <550 %. At the urban level, most estimates for Beijing fall within 90 %, and for Vienna, within 70 %. For low-income countries, non-residential buildings, and individual materials, the differences are often substantially higher, highlighting the need for an improved scientific basis for policy and planning. These disparities arise from differences in system boundaries, methodology, data sources, definitions, and lack of data to capture the diversity of material stock types. To robustly inform sustainable resource use strategies, the scientific community and practitioners should systematically assess and report sensitivity and uncertainty, and reduce the latter through transparent documentation, model intercomparisons, consensus and open-access databases, enhanced data collection, and comprehensive quantification of material stocks.

关键词： Material flow analysis Sustainable resource management Uncertainty analysis Socioeconomic metabolism Material stocks

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The 2021 report of the Lancet Countdown on health and climate change: code red for a healthy future

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The Lancet 2021年第10311期398卷 1619-1662页

作者： Romanello, Marina McGushin, Alice Di Napoli, Claudia Drummond, Paul Hughes, Nick Jamart, Louis Kennard, Harry Lampard, Pete Solano Rodriguez, Baltazar Arnell, Nigel Ayeb-Karlsson, Sonja Belesova, Kristine Cai, Wenjia Campbell-Lendrum, Diarmid Capstick, Stuart Chambers, Jonathan Chu, Lingzhi Ciampi, Luisa Dalin, Carole Dasandi, Niheer Dasgupta, Shouro Davies, Michael Dominguez-Salas, Paula Dubrow, Robert Ebi, Kristie L Eckelman, Matthew Ekins, Paul Escobar, Luis E Georgeson, Lucien Grace, Delia Graham, Hilary Gunther, Samuel H Hartinger, Stella He, Kehan Heaviside, Clare Hess, Jeremy Hsu, Shih-Che Jankin, Slava Jimenez, Marcia P Kelman, Ilan Kiesewetter, Gregor Kinney, Patrick L Kjellstrom, Tord Kniveton, Dominic Lee, Jason K W Lemke, Bruno Liu, Yang Liu, Zhao Lott, Melissa Lowe, Rachel Martinez-Urtaza, Jaime Maslin, Mark McAllister, Lucy McMichael, Celia Mi, Zhifu Milner, James Minor, Kelton Mohajeri, Nahid Moradi-Lakeh, Maziar Morrissey, Karyn Munzert, Simon Murray, Kris A Neville, Tara Nilsson, Maria Obradovich, Nick Sewe, Maquins Odhiambo Oreszczyn, Tadj Otto, Matthias Owfi, Fereidoon Pearman, Olivia Pencheon, David Rabbaniha, Mahnaz Robinson, Elizabeth Rocklöv, Joacim Salas, Renee N Semenza, Jan C Sherman, Jodi Shi, Liuhua Springmann, Marco Tabatabaei, Meisam Taylor, Jonathon Trinanes, Joaquin Shumake-Guillemot, Joy Vu, Bryan Wagner, Fabian Wilkinson, Paul Winning, Matthew Yglesias, Marisol Zhang, Shihui Gong, Peng Montgomery, Hugh Costello, Anthony Hamilton, Ian Institute for Global Health University College London London UK United Kingdom Institute for Sustainable Resources University College London London UK United Kingdom UCL Energy Institute University College London London UK United Kingdom Institute for Environmental Design and Engineering University College London London UK United Kingdom Department of Geography University College London London UK United Kingdom The Bartlett School of Sustainable Construction University College London London UK United Kingdom Centre for Human Health and Performance University College London London UK United Kingdom School of Agriculture Policy and Development University of Reading Reading United Kingdom Department of Meteorology University of Reading Reading United Kingdom The Walker Institute University of Reading Reading United Kingdom Department of Health Sciences University of York York United Kingdom Institute for Environment and Human Security United Nations University Bonn Germany Centre on Climate Change and Planetary Health London School of Hygiene & Tropical Medicine London UK United Kingdom Centre for Mathematical Modelling of Infectious Diseases London School of Hygiene & Tropical Medicine London UK United Kingdom Department of Public Health Environments and Society London School of Hygiene & Tropical Medicine London UK United Kingdom Department of Earth System Science Tsinghua University Beijing China Department of Environment Climate Change and Health World Health Organization Geneva Switzerland Institute for Environmental Sciences World Health Organization Geneva Switzerland Centre for Climate Change and Social Transformations School of Psychology Cardiff University Cardiff United Kingdom Yale Center on Climate Change and Health Yale University New Haven CT United States School of Government University of Birmingham Birmingham United Kingdom Economic analysis of Climate Impacts and Policy Centro Euro-Mediterraneo sui Cambiamenti Climatic

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Implementing the Matrix Multiplication with DFC on Kunlun Small Scale Computer

Implementing the Matrix Multiplication with DFC on Kunlun Sm...

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IEEE International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)

作者： Zheng Du Jing Zhang Shihao Sha Qiuming Luo Department of Computer Science and Technology Tsinghua University NHPCC/Guangdong Key Laboratory of popular UPC & Guangdong Province Engineering Center of China-made High Performance Data Computing System in College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

In this paper, we demonstrate a new dataflow platform of DFC, which can handle the successive dataflow computing passes with tagged data. By implementing the matrix multiplication in DFC, we show that DFC can exploit the parallelism automatically with a much simple dataflow graph constructed by DF functions of DFC. Different from the other dataflow execution platform, DFC support multiple worker threads for one dataflow node of DF functions. By running the matrix multiplication program of DFC on Kunlun system, it was verified that DFC get a reasonable speedup for large scale computing for thread number up to 512.

关键词： Distributed computing Handheld computers

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Front Cover: International Journal of Intelligent systems, Volume 37 Issue 11 November 2022

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International Journal of Intelligent systems 2022年第11期37卷

作者： Xianjia Meng Yong Yang Ximeng Liu Nan Jiang School of Information Science and Technology Northwest University Xi'an People's Republic of China Fujian Province Key Laboratory of Information Security of Network System and College of Computer and Data Science Fuzhou University Fuzhou People's Republic of China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen People's Republic of China Department of Internet of Things School of Information Engineering East China Jiao Tong University Nanchang People's Republic of China

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Accumulative time-based ranking method to reputation evaluation in information networks

arXiv

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arXiv 2019年

作者： Liao, Hao Liu, Qi-Xin Huang, Ze-Cheng Yeung, Chi Ho Zhang, Yi-Cheng National Engineering Laboratory on Big Data System Computing Technology Guangdong Province Engineering Center of China-made High Performance Data Computing System College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Institute of Big Data Intelligent Management and Decision Shenzhen University Shenzhen518060 China Department of Science and Environmental Studies The Education University of Hong Kong Hong Kong Hong Kong Department of Physics University of Fribourg Fribourg Switzerland

With the rapid development of modern technology, the Web has become an important platform for users to make friends and acquire information. However, since information on the Web is over-abundant, information filtering becomes a key task for online users to obtain relevant suggestions. As most Websites can be ranked according to users’ rating and preferences, relevance to queries, and recency, how to extract the most relevant item from the over-abundant information is always a key topic for researchers in various fields. In this paper, we adopt tools used to analyze complex networks to evaluate user reputation and item quality. In our proposed accumulative time-based ranking (ATR) algorithm, we incorporate two behavioral weighting factors which are updated when users select or rate items, to reflect the evolution of user reputation and item quality over time. We showed that our algorithm outperforms state-of-the-art ranking algorithms in terms of precision and robustness on empirical datasets from various online retailers and the citation datasets among research publications. Copyright © 2019, The Authors. All rights reserved.

关键词： Information filtering

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Health co-benefits of post-COVID-19 low-carbon recovery in Chinese cities

Nature Cities

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Nature Cities 2024年 695-705页

作者： Chenxi Lu Felix Creutzig Jiawei Hu Yingjian Huang Huibin Mo Ying Yu Yun Li Da Huo Xuanren Song Xiaoting Huang Zhu Liu Yun Sun Kai Liu Shaohui Zhang Karyn Morrissey Jinpyo Hong Zhu Deng Zhuanjia Du Mercator Research Institute on Global Commons and Climate Change Berlin Germany Sustainability Economics of Human Settlements Technical University Berlin Berlin Germany Belfer Center for Science and International Affairs Harvard Kennedy School Harvard University Cambridge MA USA Alibaba Cloud Hangzhou China Guangdong Key Laboratory for Urbanization and Geo-simulation School of Geography and Planning Sun Yat-sen University Guangzhou China Shenzhen Key Laboratory of Ecological Remediation and Carbon Sequestration Institute of Environment and Ecology Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China Data-Driven EnviroLab Department of Public Policy University of North Carolina at Chapel Hill Chapel Hill NC USA Department of Environmental Sciences and Engineering Gillings School of Global Public Health University of North Carolina at Chapel Hill Chapel Hill NC USA Faculty of Engineering Science Department of Architecture KU Leuven Leuven Belgium Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University Beijing China Department of Civil & Mineral Engineering University of Toronto Toronto Ontario Canada School of Environmental Science and Engineering Tianjin University Tianjin China Department of Geography King’s College London London UK Guangdong Holdings Limited Guangzhou China International Institute for Applied Systems Analysis (IIASA) Laxenburg Austria Division for Climate and Energy Policy Department of Technology Management and Economics Technical University of Denmark Lyngby Denmark School of Engineering Brown University Providence RI USA Department of Geography University of Hong Kong Hong Kong SAR China Department of Political Science Columbia University New York NY USA

Post-pandemic green recovery is pivotal in achieving global sustainable development goals by simultaneously revitalizing economies and reducing greenhouse gas emissions, air pollution and improving public welfare. However, subnational and city-level understanding of green recovery, its efficacy and its alignment with public health is poorly understood. Here we focus on post-COVID-19 low-carbon recovery—economic growth combined with reduced carbon emissions—and explore health co-benefits in Chinese cities. A novel near-real-time daily carbon emission dataset of 48 cities in China is developed, coupled with detailed health and economic municipal statistics and models. We find that, on average, six low-carbon-recovery cities, mainly megacities, saved 1.2 times as many lives per 100,000 population compared with the 42 other cities, and their annual monetary avoided premature deaths per 100,000 population was 1.5 times more than the 42 other cities. The accumulated monetary health co-benefits for low-carbon-recovery cities were US$ 4.2 billion (95% confidence interval, 2.1–6.3) during the post-COVID-19 period. We show that government spending on electric vehicles increases the likelihood of achieving low-carbon recovery in Chinese cities. Our results underscore the significant health co-benefits of low-carbon recovery, pointing to synergies between advancing local welfare and global environmental objectives.

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Towards deployment-centric multimodal AI beyond vision and language

arXiv

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arXiv 2025年

作者： Liu, Xianyuan Zhang, Jiayang Zhou, Shuo van der Plas, Thijs L. Vijayaraghavan, Avish Grishina, Anastasiia Zhuang, Mengdie Schofield, Daniel Tomlinson, Christopher Wang, Yuhan Li, Ruizhe van Zeeland, Louisa Tabakhi, Sina Demeocq, Cyndie Li, Xiang Das, Arunav Timmerman, Orlando Baldwin-McDonald, Thomas Wu, Jinge Bai, Peizhen Al Sahili, Zahraa Alwazzan, Omnia Do, Thao N. Suvon, Mohammod N.I. Wang, Angeline Cipolina-Kun, Lucia Moretti, Luigi A. Farndale, Lucas Jain, Nitisha Efremova, Natalia Ge, Yan Varela, Marta Lam, Hak-Keung Celiktutan, Oya Evans, Ben R. Coca-Castro, Alejandro Wu, Honghan Abdallah, Zahraa S. Chen, Chen Danchev, Valentin Tkachenko, Nataliya Lu, Lei Zhu, Tingting Slabaugh, Gregory G. Moore, Roger K. Cheung, William K. Charlton, Peter H. Lu, Haiping Centre for Machine Intelligence University of Sheffield Sheffield United Kingdom School of Computer Science University of Sheffield Sheffield United Kingdom The Alan Turing Institute London United Kingdom Department of Metabolism Digestion and Reproduction Imperial College London London United Kingdom Department of Applied AI Simula Research Laboratory Oslo Norway Information School University of Sheffield Sheffield United Kingdom NHS England Leeds United Kingdom Institute of Health Informatics University College London London United Kingdom Department of Engineering King’s College London London United Kingdom Department of Computing Science University of Aberdeen Aberdeen United Kingdom School of Informatics University of Edinburgh Edinburgh United Kingdom School of Engineering Mathematics and Technology University of Bristol Bristol United Kingdom Department of Informatics King’s College London London United Kingdom Department of Earth Sciences University of Cambridge Cambridge United Kingdom Department of Computer Science University of Manchester Manchester United Kingdom Department of Computer Science Queen Mary University of London London United Kingdom Digital Environment Research Institute Queen Mary University of London London United Kingdom Department of Computer Science University of Bath Bath United Kingdom Department of Classics King’s College London London United Kingdom School of Electrical Electronic and Mechanical Engineering University of Bristol Bristol United Kingdom School of Engineering University of the West of England Bristol United Kingdom Cancer Research UK Scotland Institute Glasgow United Kingdom School of Business and Management Queen Mary University of London London United Kingdom City St George’s University of London London United Kingdom British Antarctic Survey Cambridge United Kingdom School of Health and Wellbeing University of Glasgow Glasgow United Kingdom Chief Data & AI Office Lloyds Banking Group Lo

Multimodal artificial intelligence (AI) integrates diverse types of data via machine learning to improve understanding, prediction, and decision-making across disciplines such as healthcare, science, and engineering. However, most multimodal AI advances focus on models for vision and language data, while their deployability remains a key challenge. We advocate a deployment-centric workflow that incorporates deployment constraints early to reduce the likelihood of undeployable solutions, complementing data-centric and model-centric approaches. We also emphasise deeper integration across multiple levels of multimodality and multidisciplinary collaboration to significantly broaden the research scope beyond vision and language. To facilitate this approach, we identify common multimodal-AI-specific challenges shared across disciplines and examine three real-world use cases: pandemic response, self-driving car design, and climate change adaptation, drawing expertise from healthcare, social science, engineering, science, sustainability, and finance. By fostering multidisciplinary dialogue and open research practices, our community can accelerate deployment-centric development for broad societal impact. © 2025, CC BY.

关键词： Decision making

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Highly enhanced visible-light photocatalytic NOx purification and conversion pathway on self-structurally modified g-C-3N4 nanosheets

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science Bulletin 2018年第10期63卷 609-620页

作者： Yuhan Li Yanjuan Sun Wingkei Ho Yuxin Zhang Hongwei Huang Qiang Cai Fan Dong Chongqing Key Laboratory of Catalysis and New Environmental Materials Engineering Research Center for Waste Oil Recovery Technology and Equipment Ministry of Education College of Environment and Resources Chongqing Technology and Business University Chongqing 400067. China Department of Science and Environmental Studies. The Center for Education in Environmental Sustainability The Education University of Hong Kong. Hong Kong China College of Materials Science and Engineering National Key Laboratory of Fundamental Science of Micro/Nano-Devices and System Technology. Chongqing University.Chongqing 400044. China School of Materials Science and Technology National Laboratory of Mineral Materials China University of Geosciences Beijing 100083 China

The unmodified graphitic carbon nitride(g-C_3N_4) suffers from low photocatalytic activity because of the unfavourable *** the present work,we reported a simple self-structural modification strategy to optimize the microstructure of g-C_3N_4 and obtained graphene-like g-C_3N_4 nanosheets with porous *** contrast to traditional thermal pyrolysis preparation of g-C_3N_4,the present thermal condensation was improved via pyrolysis of thiourea in an alumina crucible without a cover,followed by secondary heat *** popcorn-like formation and layer-by-layer thermal exfoliation of graphene-like porous g-C_3N_4 was proposed to explain the formation *** photocatalytic removal performance of both NO and NO_2 with the graphene-like porous g-C_3N_4 for was significantly enhanced by selfstructural *** experiments and in-situ diffuse reflectance infrared fourier transform spectroscopy(DRIFTS) measurement were conducted to detect the active species during photocatalysis and the conversion pathway of g-C_3N_4 photocatalysis for NO_x purification was *** photocatalytic activity of graphene-like porous g-C_3N_4 was highly enhanced due to the improved charge separation and increased oxidation capacity of the ·O_2^- radicals and *** work could not only provide a novel self-structural modification for design of highly efficient photocatalysts,but also offer new insights into the mechanistic understanding of g-C_3N_4 photocatalysis.

关键词： Self-structural modification g-C3N4 Visible light photocatalysis In-situ DRIFTS Conversion pathway

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Flexible, ultrathin bioelectronic materials and devices for chronically stable neural interfaces

Brain‐X

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Brain‐X 2023年第4期1卷

作者： Lianjie Zhou Zhongyuan Wu Mubai Sun Jaejin Park Mengdi Han Ming Wang Junsheng Yu Zengfeng Di Yongfeng Mei Wubin Bai Xinge Yu Ki Jun Yu Enming Song Shanghai Frontiers Science Research Base of Intelligent Optoelectronics and Perception Institute of Optoelectronics Fudan University Shanghai China Contribution: Investigation Visualization Writing - original draft Institute of Agro-food Technology Jilin Academy of Agricultural Sciences Changchun Jilin China Contribution: Data curation Investigation Functional Bio-integrated Electronics and Energy Management Lab School of Electrical and Electronic Engineering Yonsei University Seoul Korea Department of Biomedical Engineering College of Future Technology Peking University Beijing China Contribution: Resources Validation State Key Laboratory of Integrated Chips and Systems Frontier Institute of Chip and System Fudan University Shanghai China State Key Laboratory of Electronic Thin Films and Integrated Devices School of Optoelectronic Science and Engineering University of Electronic Science and Technology of China (UESTC) Chengdu Sichuan China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China Department of Material Science Fudan University Shanghai China Department of Applied Physical Sciences University of North Carolina at Chapel Hill Chapel Hill North Carolina USA Department of Biomedical Engineering City University of Hong Kong Hong Kong SAR China Wubin Bai Xinge Yu Ki Jun Yu and Enming Song. Contribution: Conceptualization Methodology Resources Supervision Writing - review & editing

Advanced technologies that can establish intimate, long-lived functional interfaces with neural systems have attracted increasing interest due to their wide-ranging applications in neuroscience, bioelectronic medicine, and the associated treatment of neurodegenerative diseases. A critical challenge of significance remains in the development of electronic platforms that offer conformal contact with soft brain tissue for the sensing or stimulation of brain activities and chronically stable operation in vivo, at scales that range from cellular-level resolution to macroscopic areas. This review summarizes recent advances in this field, with an emphasis on the use of demonstrated concepts, constituent materials, engineered designs, and system integration to address the current challenges. The article begins with an overview of recent bioelectronic platforms with unique form factors, ranging from filamentary probes to conformal sheets and three-dimensional frameworks for alleviating the mechanical mismatch between interface materials and neural tissues. Next, active interfaces which utilize inorganic/organic semiconductor-enabled devices are reviewed, highlighting various working principles of recording mechanisms including capacitively and conductively coupled sensing enabled by high transistor matrices at high spatiotemporal resolution. The subsequent section presents approaches to biological integration which use active materials for multiplexed addressing, local amplification and multimodal operation with high-channel-count and large-scale electronic systems in a safe fashion that provides multi-decade stable performance in both animal models and human subjects. The advances summarized in this review will guide the future direction of this technology and provide a basis for next-generation chronic neural interfaces with long-lived high-performance operation.

关键词： neural interface flexibility passive electrodes active recording barrier materials chronic stability

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