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NH_(4)Cl-assisted synthesis of TaON nanoparticle applied to photocatalytic hydrogen and oxygen evolution from water

Journal of Energy Chemistry 2024年第7期94卷 541-550页

作者： Xu Kaiwei Liu Jifang Zhang Boyang Zhang Jiaming Zhang Ke Shi Haifeng Wang guijun ma of Physical Science and Technology Shanghaitech UniversityShanghai 201210China

Oxynitride semiconductors are promising photocatalyst materials for visible light-driven water splitting,while the synthesis of well crystalized oxynitride still remains challenge.In present work,narrow-bandgap TaON nanoparticles are synthesized via heating a vacuum-sealed mixture of KTaO_(3),Ta and NHaCl.This method possesses multiple advantages in terms of lower calcination parameter,higher N conversion efficiency and superior photocatalytic activity in comparison with the traditional thermal ammonolysis using NH_(3) gas as a nitrogen source.Through the analysis of intermediates produced upon the elevation of heating temperature,a gas-solid-phase reaction between TaCl_(5) and Ta_(2)O_(5) is demon-strated as the final step,which is conducive to decreasing thermal energy barrier and accelerating nitri-dation process.Precise control of preparation conditions,including calcination temperature and duration,allows for the regulation of surface O/N ratio of TaoN particles to unity,resulting in optimized photocat-alytic activity.Photoelectrochemical assessment and intensity modulated photocurrent spectroscopy provide convincing evidence for improved charge transfer effciency of photoexcited holes at TaoN sur-face.A Z-scheme overall water splitting is accomplished by employing the TaON as an effective oxygen evolution photocatalyst,SrTiO_(3):Rh as a hydrogen evolution photocatalyst,and reduced graphene oxide(rGO)as a solid-state electron mediator.This work presents a promising strategy for the synthesis of high-quality oxynitride materials in application to photocatalytic water splitting.

关键词： TaON Oxynitride synthesis Photocatalyst Water splitting,Hydrogen Z-scheme

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Estimating the State of Health for Lithium-ion Batteries:A Particle Swarm Optimization-Assisted Deep Domain Adaptation Approach

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IEEE/CAA Journal of Automatica Sinica 2023年第7期10卷 1530-1543页

作者： guijun ma Zidong Wang Weibo Liu Jingzhong Fang Yong Zhang Han Ding Ye Yuan The School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China the College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China the Department of Computer Science Brunel University LondonUxbridgeMiddlesexUB83PHUnited Kingdom the School of Information Science and Engineering Wuhan University of Science and TechnologyWuhan 430081China the School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China

The state of health(SOH)is a critical factor in evaluating the performance of the lithium-ion batteries(LIBs).Due to various end-user behaviors,the LIBs exhibit different degradation modes,which makes it challenging to estimate the SOHs in a personalized way.In this article,we present a novel particle swarm optimization-assisted deep domain adaptation(PSO-DDA)method to estimate the SOH of LIBs in a personalized manner,where a new domain adaptation strategy is put forward to reduce cross-domain distribution discrepancy.The standard PSO algorithm is exploited to automatically adjust the chosen hyperparameters of developed DDA-based method.The proposed PSODDA method is validated by extensive experiments on two LIB datasets with different battery chemistry materials,ambient temperatures and charge-discharge configurations.Experimental results indicate that the proposed PSO-DDA method surpasses the convolutional neural network-based method and the standard DDA-based method.The Py Torch implementation of the proposed PSO-DDA method is available at https://***/mxt0607/PSO-DDA.

关键词： Deep transfer learning domain adaptation hyperparameter selection lithium-ion batteries(LIBs) particle swarm optimization state of health estimation(SOH)

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Staged-probability strategy of processing shotgun proteomic data to discover more functionally important proteins

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Protein & Cell 2012年第2期3卷 140-147页

作者： Hong Xu guijun ma Qingqiao Tan Qiang Zhou Wen Su Rongxiu Li State Key Laboratory of Microbial Metabolism(Shanghai Jiao Tong University)and School of Life Sciences&Biotechnology Shanghai Jiao Tong UniversityShanghai 200240China Department of Bioinformatics and Biostatistics College of Life Sciences&BiotechnologyShanghai Jiao Tong UniversityShanghai 200240China Software Engineering Institute East China Normal UniversityShanghai 200062China

Biologically important proteins related to membrane receptors,signal transduction,regulation,transcription,and translation are usually low in abundance and identified with low probability in mass spectroscopy(MS)-based analyses.Most valuable proteomics information on them were hitherto discarded due to the application of excessively strict data filtering for accurate identification.In this study,we present a stagedprobability strategy for assessing proteomic data for potential functionally important protein clues.MS-based protein identifications from the second(L2)and third(L3)layers of the cascade affinity fractionation using the Trans-Proteomic Pipeline software were classified into three probability stages as 1.00–0.95,0.95–0.50,and 0.50–0.20 according to their distinctive identification correctness rates(i.e.100%–95%,95%–50%,and 50%–20%,respectively).We found large data volumes and more functionally important proteins located at the previously unacceptable lower probability stages of 0.95–0.50 and 0.50–0.20 with acceptable correctness rate.More importantly,low probability proteins in L2 were verified to exist in L3.Together with some MS spectrogram examples,comparisons of protein identifications of L2 and L3 demonstrated that the stagedprobability strategy could more adequately present both quantity and quality of proteomic information,especially for researches involving biomarker discovery and novel therapeutic target screening.

关键词： lower minimum probability threshold probability grade TPP low-abundant protein

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A general end-to-end diagnosis framework for manufacturing systems

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National Science Review 2020年第2期7卷 418-429页

作者： Ye Yuan guijun ma Cheng Cheng Beitong Zhou Huan Zhao Hai-Tao Zhang Han Ding School of Artificial Intelligence and Automation MOE Key Lab of Intelligent Control and Image Processing Huazhong University of Science and Technology State Key Laboratory of Digital manufacturing Equipment and Technology Huazhong University of Science and Technology School of Mechanical Science and Engineering Huazhong University of Science and Technology

The manufacturing sector is envisioned to be heavily influenced by artificial-intelligence-based technologies with the extraordinary increases in computational power and data volumes. A central challenge in the manufacturing sector lies in the requirement of a general framework to ensure satisfied diagnosis and monitoring performances in different manufacturing applications. Here, we propose a general data-driven,end-to-end framework for the monitoring of manufacturing systems. This framework, derived from deep-learning techniques, evaluates fused sensory measurements to detect and even predict faults and wearing conditions. This work exploits the predictive power of deep learning to automatically extract hidden degradation features from noisy, time-course data. We have experimented the proposed framework on 10 representative data sets drawn from a wide variety of manufacturing applications. Results reveal that the framework performs well in examined benchmark applications and can be applied in diverse contexts,indicating its potential use as a critical cornerstone in smart manufacturing.

关键词： manufacturing systems deep learning diagnosis and monitoring

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Formation of multifaceted nano-groove structure on rutile TiO_(2) photoanode for efficient electron-hole separation and water splitting

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Journal of Energy Chemistry 2022年第2期31卷 19-25页

作者： Xiaoyi Zhan Yaling Luo Ziyu Wang Yao Xiang Zheng Peng Yong Han Hui Zhang Ruotian Chen Qin Zhou Hongru Peng Hao Huang Weimin Liu Xin Ou guijun ma Fengtao Fan Fan Yang Can Li Zhi Liu State Key Laboratory of Functional materials for Informatics Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai 200050China School of Physical Science and Technology Shanghai Tech UniversityShanghai 201210China State Key Laboratory of Catalysis Dalian National Laboratory for Clean EnergyDalian Institute of Chemical PhysicsChinese Academy of SciencesDalian 116023LiaoningChina Center of materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China

Photoelectrochemical(PEC)water-splitting using solar energy holds great promise for the renewable energy future,and a key challenge in the development of industry viable PEC devices is the unavailability of high-efficient photoanodes.Herein,we designed a TiO_(2) model photocatalyst with nano-groove pattern and different surface orientation using low-energy Ar+irradiation and photoetching of TiO_(2),and significantly improved the intrinsic activity for PEC water oxidation.High-resolution transmission electron microscopy directly manifests that the grooves consist of highly stepped surface withsteps and well-crystallized.Transient absorption spectroscopy reveals the groove surface that allows for increased recovery lifetime,which ensures promoted electron-hole separation efficiency.Surface photovoltage directly shows the carrier separation and transportation behaviors,verified by selective photodeposition,demonstrating the groove surface on TiO_(2) contributes to electron-hole separation.This work proposes an efficient and scalable photoanode strategy,which potentially can open new opportunities for achieving efficient PEC water oxidation performance.

关键词： TiO_(2)nano-grooves Oxygen vacancies Photoetching Water oxidation

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Data-Driven Discovery of Stochastic Differential Equations

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Engineering 2022年第10期17卷 244-252页

作者： Yasen Wang Huazhen Fang Junyang Jin guijun ma Xin He Xing Dai Zuogong Yue Cheng Cheng Hai-Tao Zhang Donglin Pu Dongrui Wu Ye Yuan Jorge Gonçalves Jürgen Kurths Han Ding School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China State Key Laboratory of Digital manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China Department of Mechanical Engineering University of KansasLawrenceKS 66045USA HUST-Wuxi Research Institute Wuxi 214174China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Department of Plant Sciences University of CambridgeCambridge CB23EAUK Luxembourg Centre for Systems Biomedicine University of LuxembourgBelvaux 4367Luxembourg Department of Physics Humboldt University of BerlinBerlin 12489Germany Department of Complexity Science Potsdam Institute for Climate Impact ResearchPotsdam 14473Germany

Stochastic differential equations(SDEs)are mathematical models that are widely used to describe complex processes or phenomena perturbed by random noise from different sources.The identification of SDEs governing a system is often a challenge because of the inherent strong stochasticity of data and the complexity of the system’s dynamics.The practical utility of existing parametric approaches for identifying SDEs is usually limited by insufficient data resources.This study presents a novel framework for identifying SDEs by leveraging the sparse Bayesian learning(SBL)technique to search for a parsimonious,yet physically necessary representation from the space of candidate basis functions.More importantly,we use the analytical tractability of SBL to develop an efficient way to formulate the linear regression problem for the discovery of SDEs that requires considerably less time-series data.The effectiveness of the proposed framework is demonstrated using real data on stock and oil prices,bearing variation,and wind speed,as well as simulated data on well-known stochastic dynamical systems,including the generalized Wiener process and Langevin equation.This framework aims to assist specialists in extracting stochastic mathematical models from random phenomena in the natural sciences,economics,and engineering fields for analysis,prediction,and decision making.

关键词： Data-driven method System identification Sparse Bayesian learning Stochastic differential equations Random phenomena

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Diatom-inspired multiscale mineralization of patterned protein–polysaccharide complex structures

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National Science Review 2021年第8期8卷 186-197页

作者： Ke Li Yingfeng Li Xinyu Wang Mengkui Cui Bolin An Jiahua Pu Jintao Liu Boyang Zhang guijun ma Chao Zhong School of Physical Science and Technology Shanghai Tech University Center for materials Synthetic Biology Shenzhen Institute of Synthetic BiologyShenzhen Institutes of Advanced TechnologyChinese Academy of Sciences CAS Key Laboratory of Quantitative Engineering Biology Shenzhen Institute of Synthetic BiologyShenzhen Institutes of Advanced TechnologyChinese Academy of Sciences

marine diatoms construct their hierarchically ordered, three-dimensional(3D) external structures called frustules through precise biomineralization processes. Recapitulating the remarkable architectures and functions of diatom frustules in artificial materials is a major challenge that has important technological implications for hierarchically ordered composites. Here, we report the construction of highly ordered,mineralized composites based on fabrication of complex self-supporting porous structures—made of genetically engineered amyloid fusion proteins and the natural polysaccharide chitin—and performing in situ multiscale protein-mediated mineralization with diverse inorganic materials, including SiO2, TiO2and Ga2O3. Subsequently, using sugar cubes as templates, we demonstrate that 3D fabricated porous structures can become colonized by engineered bacteria and can be functionalized with highly photoreactive minerals,thereby enabling co-localization of the photocatalytic units with a bacteria-based hydrogenase reaction for a successful semi-solid artificial photosynthesis system for hydrogen evolution. Our study thus highlights the power of coupling genetically engineered proteins and polysaccharides with biofabrication techniques to generate hierarchically organized mineralized porous structures inspired by nature.

关键词： biomimetic mineralization patterned porous structure genetic engineering amyloid protein artificial photosynthesis

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通信机房防雷接地保护研究

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通信电源技术 2024年第8期41卷 225-227页

作者：刘伟马桂军金立标李启龙丁杰中国联合网络通信有限公司徐州市分公司江苏徐州221000 中国联合网络通信有限公司江苏省分公司江苏南京210000

文章分析了雷电的形成及其对通信机房的危害,探讨通信机房防雷接地存在的隐患,并给出了解决隐患的可行性技术措施。通过介绍传统的防雷接地技术,提出防雷接地技术的创新发展方向,为通信机房的防雷接地保护提供有力的技术支持。

关键词：通信机房防雷接地巡检维护

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Fusion of VNIR and Thermal Infrared Remote Sensing Data Based on GA-SOFM Neural Network

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Geo-Spatial Information Science 2009年第4期12卷 271-280页

作者： WANG Chongchang YANG guijun ma Zhen li XING Zhurong School of Geomatics Liaoning Technical University 47 Zhonghua Road Fuxin 123000 China

多来源数据熔化方法很少目前涉及 VNIR 和热红外线的遥感。因此，二种数据的潜在的优点足够地还没被拍了，它导致与陆地表面温度联系的参数的低计算精确。在的地方，一个新熔化方法被提出 VNIR 的高空间的分辨率的特征(可见、近红外线)... 详细信息

多来源数据熔化方法很少目前涉及 VNIR 和热红外线的遥感。因此，二种数据的潜在的优点足够地还没被拍了，它导致与陆地表面温度联系的参数的低计算精确。在的地方，一个新熔化方法被提出 VNIR 的高空间的分辨率的特征(可见、近红外线) 数据和热红外线的数据的高时间的分辨率充分在这篇论文被探索。非线性的熔化被实现在在借助于 GA-SOFM (基因算法与自我组织的特征地图) 从 VNIR 数据和热红外线的数据估计的陆地表面参数之间的高空间的分辨率和高时间的分辨率获得陆地表面温度 -ANN (人工的神经网络) 。最后，方法被翠菊卫星数据验证。结果证明方法简单、方便并且能很快与高精确捕获更高的分辨率的陆地表面温度分发。

关键词： SOFM神经网络热红外遥感可见光近红外数据基高空间分辨率时间分辨率 GA

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Mutual feedback between algal blooming and global warming

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Journal of Oceanology and Limnology 2024年第3期42卷 787-801页

作者： Jianrong ma guijun YANG Xianfu ZHAO Boqiang QIN Kun SHAN Botian ZHOU Yan ZENG Jingfu WANG Jingan CHEN Chongqing Institute of Green and Intelligent Technology Chinese Academy of SciencesChongqing 400714China State Key Laboratory of Environmental Geochemistry Institute of GeochemistryChinese Academy of SciencesGuiyang 550002China School of Environment and Ecology Jiangnan UniversityWuxi 214122China Key Laboratory of Ministry of Water Resources for Ecological Impacts of Hydraulic-projects and Restoration of Aquatic Ecosystem Institute of HydroecologyMinistry of Water Resources&Chinese Academy of SciencesWuhan 430079China State Key Laboratory of Lake Science and Environment Nanjing Institute of Geography and LimnologyChinese Academy of SciencesNanjing 210008China

Global warming and algal blooms have been two of the most pressing problems faced by the world today.In recent decades,numerous studies indicated that global warming promoted the expansion of algal blooms.However,research on how algal blooms respond to global warming is scant.Global warming coupled with eutrophication promoted the rapid growth of phytoplankton,which resulted in an expansion of algal blooms.Algal blooms are affected by the combined effects of global warming,including increases in temperatures,CO_(2)concentration,and nutrient input to aquatic systems by extreme weather events.Since the growth of phytoplankton requires CO_(2),they appear to act as a carbon sink.Unfortunately,algal blooms will release CH4,CO_(2),and inorganic nitrogen when they die and decompose.As substrate nitrogen increases from decompose algal biomass,more N2O will be released by nitrification and denitrification.In comparison to CO_(2),CH4has 28-fold and N2O has 265-fold greenhouse effect.Moreover,algal blooms in the polar regions may contribute to melting glaciers and sea ice(will release greenhouse gas,which contribute to global warming)by reducing surface albedo,which consequently would accelerate global warming.Thus,algal blooms and global warming could form feedback loops which prevent human survival and development.Future researches shall examine the mechanism,trend,strength,and control strategies involved in this mutual feedback.Additionally,it will promote global projects of environmental protection combining governance greenhouse gas emissions and algal blooms,to form a geoengineering for regulating the cycles of carbon,nitrogen,and phosphorus.

关键词： climate change carbon neutrality eutrophication greenhouse gas glaciers melting geoengineering

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