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Improve the Conversion Rate and Selectivity of Aromatic Monomers in Lignin Photocatalytic Conversion Reaction Via Phase Junction Cadmium Sulphide Nanoparticles and External Hydrogen Supplier

SSRN

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SSRN 2023年

作者： Yue, Zongyang Shao, Shibo Yu, Jialin Lu, Guanchu Wei, Wenjing Huang, Yi Zhang, Kai Wang, Ke Fan, Xianfeng Institute for Materials and Processes School of Engineering The University of Edinburgh EdinburghEH9 3BF United Kingdom Petrochemical Research Institute PetroChina Company Limited Beijing102206 China Beijing Key Laboratory of Ionic Liquids Clean Process CAS Key Laboratory of Green Process and Engineering State Key Laboratory of Multiphase Complex Systems Innovation Academy for Green Manufacture Institute of Process Engineering Chinese Academy of Sciences Beijing100190 China Longzihu New Energy Laboratory Zhengzhou Institute of Emerging Industrial Technology Henan University Zhengzhou450000 China Beijing Key Laboratory of Emission Surveillance and Control for Thermal Power Generation North China Electric Power University Beijing102206 China

Photocatalysis has the potential for lignin valorisation to generate functionalised aromatic monomers, but its large-scale application has been limited by the slow conversion rate and the low selectivity to desirable products. In this work, record-high lignin model compound Cβ-O bonds cleavage rate and excellent selectivity to aromatic monomers has been achieved over the development of new hexagonal-cubic phase junction CdS nanoparticles, enhanced external hydrogen donor supply and hydrogen transfer, and improved separation efficiency of photo-excited electron-hole pairs. Water is employed as external hydrogen supplier, resulting in significant increase in the conversion rate of 2-Phenoxy-1-phenylethanol (PP-ol) as lignin model from 21.8% to 100% and the selectivity of aromatic monomers from ~45% to ~94% under 1h of visible light irradiation. 2-phenoxyacetophenone (PP-one) as by-product from PP-ol conversion can be further completely cleaved to aromatic monomers with assistance of TEA and reaction time is reduced from 2-12 hrs to10 min under visible-light irradiation. © 2023, The Authors. All rights reserved.

关键词： Lignin

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new Sensing Technologies for Monitoring Machinery,Structures,and Manufacturing processes

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Journal of Dynamics, Monitoring and Diagnostics 2023年第2期2卷 69-88页

作者： JDMD Editorial Office Zhaoyan Fan Robert X.Gao Qingbo He Yi Huang Tianxi Jiang Zhike Peng Luc Thévenaz Yuyong Xiong Shuncong Zhong Editorial Office of JDMD Chongqing University of TechnologyChongqingPeople’s Republic of China Department of Mechanical Industrialand Manufacturing EngineeringOregon State UniversityCorvallisORUSA Department of Mechanical and Aerospace Engineering Case Western Reserve UniversityClevelandOHUSA State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong UniversityShanghaiPeople’s Republic of China Fujian Provincial Key Laboratory of Terahertz Functional Devices and Intelligent Sensing School of Mechanical Engineering and AutomationFuzhou UniversityFuzhouPeople’s Republic of China Institute of Precision Instrument and Intelligent Measurement&Control Fuzhou UniversityFuzhouPeople’s Republic of China School of Mechanical Engineering Ningxia UniversityYinchuanPeople’s Republic of China EPFL Ecole Polytechnique Fédérale de Lausanne Group for Fibre OpticsSCI-STI-LT Station 111015 LausanneSwitzerland

Sensing is the fundamental technique for sensor data acquisition in monitoring the operation condition of the machinery,structures,and manufacturing *** this paper,we briefly discuss the general idea and advances of various new sensing technologies,including multiphysics sensing,smart materials and metamaterials sensing,microwave sensing,fiber optic sensors,and terahertz sensing,for measuring vibration,deformation,strain,acoustics,temperature,spectroscopic,*** on the observations from the state of the art,we provide comprehensive discussions on the possible opportunities and challenges of these new sensing technologies so as to steer future development.

关键词： fiber optic sensor metamaterials sensing microwave sensing multiphysics sensing terahertz sensing

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Corrigendum to “Optimization control for the coordinated system of an ultra-supercritical unit based on stair-like predictive control algorithm” [control Eng. Pract. 82 (2019) 185–200]

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control Engineering Practice 2019年 83卷 213-213页

作者： Deliang Zeng Yaokui Gao Yong Hu Jizhen Liu State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Changping District 102206 Beijing China Key Laboratory of Measurement and Control New Technology and System for Industrial Process North China Electric Power University Changping District 102206 Beijing China

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Fidelity-based probabilistic q-learning for control of quantum systems

arXiv

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arXiv 2018年

作者： Chen, Chunlin Dong, Daoyi Li, Han-Xiong Chu, Jian Tarn, Tzyh-Jong Department of Control and System Engineering Nanjing University Nanjing210093 China Department of Chemistry Princeton University PrincetonNJ08544 United States School of Information Technology and Electrical Engineering University of New South Wales Australian Defence Force Academy CanberraACT2600 Australia Institute of Cyber-Systems and Control State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China Department of Systems Engineering and Engineering Management City University of Hong Kong Hong Kong Department of Electrical and Systems Engineering Washington University in St. Louis St. LouisMO63130 United States

The balance between exploration and exploitation is a key problem for reinforcement learning methods, especially for Q-learning. In this paper, a fidelity-based probabilistic Qlearning (FPQL) approach is presented to naturally solve this problem and applied for learning control of quantum systems. In this approach, fidelity is adopted to help direct the learning process and the probability of each action to be selected at a certain state is updated iteratively along with the learning process, which leads to a natural exploration strategy instead of a pointed one with configured parameters. A probabilistic Q-learning (PQL) algorithm is first presented to demonstrate the basic idea of probabilistic action selection. Then the FPQL algorithm is presented for learning control of quantum systems. Two examples (a spin- 1/2 system and a Λ-type atomic system) are demonstrated to test the performance of the FPQL algorithm. The results show that FPQL algorithms attain a better balance between exploration and exploitation, and can also avoid local optimal policies and accelerate the learning process. Copyright © 2018, The Authors. All rights reserved.

关键词： Reinforcement learning

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Energy Consumption Data Prediction Analysis based on EEMD-ARMA Model

Energy Consumption Data Prediction Analysis based on EEMD-AR...

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IEEE International Conference on Mechatronics and Automation

作者： Ji Li Xudong Jiang Lei Shao Hongli Liu Chao Chen Gang Wang Dongdong Du Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Tianjin University of Technology Binshui Xidao 391 Tianjin China Tianjin Economic and Technological Development Zone Binhai New Area Continental Automotive (System) Tianjin Co. Ltd. Continental Automotive (System) Tianjin Co. Ltd No.2 Bohai Road Tianjin China China Academy of Industrial Internet Institute of Data management and application Beijing Block B East Lake International Center No. 6 Lize West Street Chaoyang District Beijing

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

Energy consumption data prediction model based on EEMD-ARMA prediction model is used to predict the nonlinear and non-stationary characteristics of industrial energy consumption data, which leads to the difficulty of data prediction and low prediction accuracy. This paper improves empirical mode decomposition (empirical mode decomposition, EMD) by ensemble empirical mode decomposition (ensemble empirical mode decomposition, EEMD) and predicts energy consumption by combining auto regressive sliding average model (auto regressive and moving average model, ARMA). Finally, the historical data of a certain system is selected as a sample, and the comparative analysis of EMD-ARMA and EEMD-ARMA prediction results is used to verify that the EEMD-ARMA algorithm used is more accurate than the prediction algorithm, and its prediction results have certain reference value for the control of energy consumption data in enterprises.

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Sequential Power Flow Calculation of AC/DC Hybrid system Considering Converter Station Loss and Its Capacity Constraints

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IOP Conference Series: Earth and Environmental Science 2019年第4期300卷

作者： Yuxin Zhu Xiaolan Wang Lu Zhang Naixing He Key Laboratory of Advanced Industrial Process Control of Gansu Province Lanzhou University of Technology Lanzhou China School of Electrical Engineering and Information Lanzhou University of Technology Lanzhou China Lanzhou New District Municipal Investment Management Co. Ltd. Lanzhou China

In the power flow calculation of AC/DC hybrid distribution network, the loss and capacity of the converter station have a great influence on the power flow calculation, and the AC/DC alternately solves the power flow, which has the disadvantage of poor convergence performance. In this paper, the power flow model of AC/DC hybrid distribution network with VSC is established. The loss mechanism of the converter station is analyzed and the capacity limit of the converter station is fully considered. The sequential power flow algorithm with VSC AC-DC hybrid distribution network is proposed. Its convergence performance is improved. In the improved 33-node AC/DC hybrid power distribution network with multiple types of DG, the correctness, effectiveness and algorithm fastness of the algorithm are verified.

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Implementing urban agriculture as nature-based solutions in China: Challenges and global lessons

Soil and Environmental Health

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Soil and Environmental Health 2024年第1期2卷

作者： Zhu, Zhe Chan, Faith Ka Shun Li, Gang Xu, Mengxia Feng, Meili Zhu, Yong-Guan Key Laboratory of Urban Environment and Health Institute of Urban Environment Chinese Academy of Sciences Xiamen 361021 China Department of Chemical and Environmental Engineering University of Nottingham Ningbo China Ningbo 315100 China Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control CAS Haixi Industrial Technology Innovation Center in Beilun Ningbo 315830 China School of Geographical Sciences University of Nottingham Ningbo China Ningbo 315100 China Water@Leeds School of Geography University of Leeds Leeds LS2 9JT United Kingdom Research Base for Shenzhen Municipal Policy & Development Southern University of Science and Technology Shenzhen 518000 China New Materials Institute University of Nottingham Ningbo China Ningbo 315100 China Key Laboratory for Carbonaceous Wastes Processing and Process Intensification of Zhejiang Province University of Nottingham Ningbo China Ningbo 315100 China State Key Lab of Urban and Regional Ecology Research Center for Eco-Environmental Sciences Chinese Academy of Sciences Beijing 100085 China

Urban agriculture, the cultivation, processing and distribution of plants and animals within urban or suburban boundaries, is becoming an increasingly relevant strategy to enhance food safety for urban residents. With the rapid urbanization in the past decades, three forms of urban agriculture have emerged in China: small-scale, capital-intensive, and tourism-type. Each form presents unique benefits and challenges, and their development has been largely shaped by the local socio-economic context and government policies. The benefits of urban agriculture are numerous including the improvement of community cohesion, promotion of physical health, enhancement of environmental sustainability, and creation of new economic opportunities. However, it also faces many challenges such as land availability, capital shortage, market access, and governmental support. This review summarized the benefits of urban agriculture and its current limitations and challenges, and proposed strategies to overcome these hurdles in China based on successful examples in the Global North. Four specific recommendations were provided to promote urban agriculture in China: integration of urban agriculture into cities, solutions for land and resource use, government support and reactions, as well as planning and regulations. Adoption these recommendations is expected to result in healthier cities and a more robust agricultural system in China. © 2024 The Author(s)

关键词： Environmental health Environmental sustainability Food security Global North Green city One health Urban farming Urban planning

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Front Cover: Revealing the Roles of Guanidine Hydrochloride Ionic Liquid in Ion Inhibition and Defects Passivation for Efficient and Stable Perovskite Solar Cells (ChemSusChem 14/2024)

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ChemSusChem 2024年第14期17卷

作者： Aamir Saeed Dr. Liang Wang Zhaoyang Chen Junhui Fang Iqbal Hussain Lin Yuan Shuai Wang Jianwei Zhao Prof. Dr. Haitao Zhang Prof. Dr. Qingqing Miao CAS Key Laboratory of Green Process and Engineering Institute of Process Engineering Chinese Academy of Sciences Beijing 100190 P.R. China University of Chinese Academy of Sciences Beijing 100049 China Info-Powered Energy System Research Center (i-PERC) The University of Electro-Communications Tokyo 182-8585 Longzihu New Energy Laboratory Zhengzhou Institute of Emerging Industrial Technology Henan University Zhengzhou 450000 P. R. China Shenzhen Huasuan Technology Co. Ltd Shenzhen 518055 P.R. China Langfang Technological Centre of Green Industry Langfang 065001 P.R. China

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Correction to: 2021 Taxonomic update of phylum Negarnaviricota (Riboviria: Orthornavirae), including the large orders Bunyavirales and Mononegavirales

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Archives of virology 2021年第12期166卷 3567-3579页

作者： Jens H Kuhn Scott Adkins Bernard R Agwanda Rim Al Kubrusli Sergey V Alkhovsky Gaya K Amarasinghe Tatjana Avšič-Županc María A Ayllón Justin Bahl Anne Balkema-Buschmann Matthew J Ballinger Christopher F Basler Sina Bavari Martin Beer Nicolas Bejerman Andrew J Bennett Dennis A Bente Éric Bergeron Brian H Bird Carol D Blair Kim R Blasdell Dag-Ragnar Blystad Jamie Bojko Wayne B Borth Steven Bradfute Rachel Breyta Thomas Briese Paul A Brown Judith K Brown Ursula J Buchholz Michael J Buchmeier Alexander Bukreyev Felicity Burt Carmen Büttner Charles H Calisher Mengji Cao Inmaculada Casas Kartik Chandran Rémi N Charrel Qi Cheng Yuya Chiaki Marco Chiapello Il-Ryong Choi Marina Ciuffo J Christopher S Clegg Ian Crozier Elena Dal Bó Juan Carlos de la Torre Xavier de Lamballerie Rik L de Swart Humberto Debat Nolwenn M Dheilly Emiliano Di Cicco Nicholas Di Paola Francesco Di Serio Ralf G Dietzgen Michele Digiaro Olga Dolnik Michael A Drebot J Felix Drexler William G Dundon W Paul Duprex Ralf Dürrwald John M Dye Andrew J Easton Hideki Ebihara Toufic Elbeaino Koray Ergünay Hugh W Ferguson Anthony R Fooks Marco Forgia Pierre B H Formenty Jana Fránová Juliana Freitas-Astúa Jingjing Fu Stephanie Fürl Selma Gago-Zachert George Fú Gāo María Laura García Adolfo García-Sastre Aura R Garrison Thomas Gaskin Jean-Paul J Gonzalez Anthony Griffiths Tony L Goldberg Martin H Groschup Stephan Günther Roy A Hall John Hammond Tong Han Jussi Hepojoki Roger Hewson Jiang Hong Ni Hong Seiji Hongo Masayuki Horie John S Hu Tao Hu Holly R Hughes Florian Hüttner Timothy H Hyndman M Ilyas Risto Jalkanen Dàohóng Jiāng Gilda B Jonson Sandra Junglen Fujio Kadono Karia H Kaukinen Michael Kawate Boris Klempa Jonas Klingström Gary Kobinger Igor Koloniuk Hideki Kondō Eugene V Koonin Mart Krupovic Kenji Kubota Gael Kurath Lies Laenen Amy J Lambert Stanley L Langevin Benhur Lee Elliot J Lefkowitz Eric M Leroy Shaorong Li Longhui Li Jiànróng Lǐ Huazhen Liu Igor S Lukashevich Piet Maes William Marciel de Souza Marco Marklewitz Sergio H Marshall Shin-Yi L Marzano Se Integrated Research Facility at Fort Detrick National Institute of Allergy and Infectious Diseases National Institutes of Health Frederick MD USA. kuhnjens@mail.nih.gov. United States Department of Agriculture Agricultural Research Service US Horticultural Research Laboratory Fort Pierce FL USA. Zoology Department National Museums of Kenya Nairobi Kenya. Jomo Kenyatta University of Agriculture & Technology Nairobi Kenya. Division Phytomedicine Faculty of Life Sciences Humboldt-Universität zu Berlin Berlin Germany. D.I. Ivanovsky Institute of Virology of N.F. Gamaleya National Center on Epidemiology and Microbiology of Ministry of Health of Russian Federation Moscow Russia. Department of Pathology and Immunology Washington University School of Medicine St. Louis MO USA. Faculty of Medicine University of Ljubljana Ljubljana Slovenia. Centro de Biotecnología y Genómica de Plantas Universidad Politécnica de Madrid-Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria Campus de Montegancedo Pozuelo de Alarcón Madrid Spain. Departamento de Biotecnología-Biología Vegetal Escuela Técnica Superior de Ingeniería Agronómica Alimentaria y de Biosistemas Universidad Politécnica de Madrid Madrid Spain. Center for Ecology of Infectious Diseases Department of Infectious Diseases Department of Epidemiology and Biostatistics Insitute of Bioinformatics University of Georgia Athens GA USA. Institute of Novel and Emerging Infectious Diseases Friedrich-Loeffler-Institut Federal Research Institute for Animal Health Greifswald-Insel Riems Germany. Department of Biological Sciences Mississippi State University Mississippi State MS USA. Center for Microbial Pathogenesis Institute for Biomedical Sciences Georgia State University Atlanta GA USA. Edge BioInnovation Consulting and Mgt Frederick MD USA. Institute of Diagnostic Virology Friedrich-Loeffler-Institut Greifswald-Insel Riems Germany. UFyMA INTA-CONICET Cordoba Argentina. Genomics and Bioinformatics D

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