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ITO@TiO_(2)nanoarray:An efficient and robust nitrite reduction reaction electrocatalyst toward NH_(3)production under ambient conditions

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escience 2022年第4期2卷 382-388页

作者： Shaoxiong Li Jie Liang Peipei Wei Qian Liu Lisi Xie Yonglan Luo Xuping Sun Chemical Synthesis and Pollution Control Key Laboratory of Sichuan Province School of Chemistry and Chemical EngineeringChina West Normal UniversityNanchong637002SichuanChina Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu610054SichuanChina Institute for Advanced Study Chengdu UniversityChengdu610106SichuanChina

Ambient electrochemical nitrite(NO_(2)^(-))reduction is viewed as an effective and sustainable approach for simul-taneously removing NO_(2)^(-)and producing ammonia(NH_(3)).However,the complex multi-electron transfer steps involved in the NO_(2)^(-)reduction reaction(NO_(2)^(-)RR)lead to sluggish kinetics and low product selectivity toward NH_(3),underscoring the need for NH_(3)synthesis electrocatalysts with high activity and ***,we report amorphous indium-tin oxide sputtered on a TiO_(2)nanobelt array on a Ti plate(ITO@TiO_(2)/TP)as a 3D NH_(3)-producing catalyst for the NO_(2)^(-).In 0.5 M LiClO_(4)with 0.1 M NO_(2)^(-),it shows greatly boosted NO_(2)^(-)RR activity toward NH_(3)production,with excellent selectivity,achieving a large NH_(3)yield of 411.3μmol h^(-1)cm^(-2)and a high Faradaic efficiency of 82.6%.It also shows high durability for continuous electrolysis.A Zn-NO_(2)^(-)battery with ITO@TiO_(2)/TP cathode offers an NH_(3)yield of 23.1μmol h^(-1)cm^(-2)and a peak power density of 1.22 m.

关键词： ITO Electrocatalysis Magnetron sputtering Nitrite reduction reaction NH_(3)electrosynthesis

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Electronic structures of crystalline and amorphous GeSe and GeSbTe compounds using machine learning empirical pseudopotentials

arXiv

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arXiv 2025年

作者： Kang, Sungmo Kim, Rokyeon Han, Seungwu Son, Young-Woo School of Computational Sciences Korea Institute for Advanced Study Seoul02455 Korea Republic of Center for AI and Natural Sciences Korea Institute for Advanced Study Seoul02455 Korea Republic of Department of Materials Science and Engineering Research Institute of Advanced Materials Seoul National University Seoul08826 Korea Republic of

The newly developed machine learning (ML) empirical pseudopotential (EP) method overcomes the poor transferability of the traditional EP method with the help of ML techniques while preserving its formal simplicity and computational efficiency. We apply the new method to binary and ternary systems such as GeSe and Ge-Sb-Te (GST) compounds, well-known materials for non-volatile phase-change memory and related technologies. Using a training set of ab initio electronic energy bands and rotation-covariant descriptors for various GeSe and GST compounds, we generate transferable EPs for Ge, Se, Sb, and Te. We demonstrate that the new ML model accurately reproduces the energy bands and wavefunctions of structures outside the training set, closely matching first-principles calculations. This accuracy is achieved with significantly lower computational costs due to the elimination of self-consistency iterations and the reduced size of the plane-wave basis set. Notably, the method maintains accuracy even for diverse local atomic environments, such as amorphous phases or larger systems not explicitly included in the training set. Copyright © 2025, The Authors. All rights reserved.

关键词： Tellurium compounds

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Anomaly-Resistant Decentralized State Estimation Under Minimum Error Entropy With Fiducial Points for Wide-Area Power Systems

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 74-87页

作者： Bogang Qu Zidong Wang Bo Shen Hongli Dong Hongjian Liu the College of Automation Engineering Shanghai University of Electric PowerShanghai 200090China IEEE the Department of Computer Science Brunel University LondonUxbridgeMiddiesex UB83PHUK the College of Information Science and Technology Donghua UniversityShanghai 200051 the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of EducationShanghai 201620China the Artificial Intelligence Energy Research Institute Northeast Petroleum UniversityDaqing 163318 the Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast PetroleumDaqing 163318 the Sanya Offshore Oil&Gas Research Institute Northeast Petroleum UniversitySanya 572024China the Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment Ministry of EducationAnhui Polytechnic UniversityWuhu 2410000 the School of Mathematics and Physics Anhui Polytechnic UniversityWuhu 241000China

This paper investigates the anomaly-resistant decentralized state estimation(SE) problem for a class of wide-area power systems which are divided into several non-overlapping areas connected through transmission lines. Two classes of measurements(i.e., local measurements and edge measurements) are obtained, respectively, from the individual area and the transmission lines. A decentralized state estimator, whose performance is resistant against measurement with anomalies, is designed based on the minimum error entropy with fiducial points(MEEF) criterion. Specifically, 1) An augmented model, which incorporates the local prediction and local measurement, is developed by resorting to the unscented transformation approach and the statistical linearization approach;2) Using the augmented model, an MEEF-based cost function is designed that reflects the local prediction errors of the state and the measurement;and 3) The local estimate is first obtained by minimizing the MEEF-based cost function through a fixed-point iteration and then updated by using the edge measuring information. Finally, simulation experiments with three scenarios are carried out on the IEEE 14-bus system to illustrate the validity of the proposed anomaly-resistant decentralized SE scheme.

关键词： Decentralized state estimation(SE) measurements with anomalies minimum error entropy unscented Kalman filter wide-area power systems

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Research on Lightweight Object Detection Based on Improved YOLOv8n for Experimental Scenarios

Research on Lightweight Object Detection Based on Improved Y...

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Artificial Intelligence and Automation control (AIAC), International Conference on

作者： Runqi Lin Jiafan Zhuang Zhifeng Hao Zhun Fan Department of Electronic Engineering Shantou University Shantou China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China ShenZhen China

ISBN: (数字)9798331529147

ISBN: (纸本)9798331529154

Lightweight object detection algorithms, represented by YOLOv8n, have achieved significant optimization in terms of model size and parameter count. However, the neck network of YOLOv8n is not efficient at fusing contextual information, and the backbone network's feature extraction capabilities are limited. This results in challenges such as missed and incorrect detections in complex images, occluded objects, or scenes with significant background noise, making it difficult to meet the requirements of end-to-end industrial deployments. To address these issues, this paper proposes a lightweight object detection algorithm based on an enhanced YOLOv8n architecture. By integrating FasterBlock, GhostConv, and BiFPN modules, the model's detection performance and feature extraction capabilities are significantly improved, while the overall model size is reduced. Additionally, we constructed a dataset based on experimental scenarios to validate the effectiveness of the proposed model. Experimental results show that the modified model improves detection accuracy by approximately 7% compared to the original YOLOv8n, reduces the parameter count from 3.01 million to 1.73 million. This significantly enhances detection accuracy while substantially reducing the model's size. The improved YOLOv8n model not only demonstrates superior detection performance but also effectively reduces model complexity and resource consumption. This research lays a solid foundation for future applications of AI-driven automated scoring systems in experimental environments and provides a new direction for end-to-end industrial deployment.

关键词： Solid modeling Analytical models Accuracy Target recognition Computational modeling Scalability Object detection Solids Feature extraction Complexity theory

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锂硫电池中能同时实现多硫化锂的物理阻挡与化学电催化作用的隔膜修饰层

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science China Materials 2024年第1期67卷 107-115页

作者：吴庆柱李莉媛郑雨爽彭波徐杰吴孔林夏爱林马连波 School of Materials Science and Engineering Anhui University of TechnologyMa’anshan 243002China Carbon Cycle and Emission Control Research Center of Low-Carbon Research Institute Institute of Clean Energy and Advanced NanocatalysisSchool of Chemistry and Chemical EngineeringAnhui University of TechnologyMa’anshan 243002China Department of Mechanical and Aerospace Engineering The Hong Kong University of Science and TechnologyHong Kong 999077China

锂硫电池的实际应用仍受制于一些挑战,包括氧化还原动力学缓慢和由此引发的穿梭效应等.为解决这些问题,我们巧妙合成了一种由FeS_(2)和分等级多孔碳结构(PCF)组成的隔膜修饰层.这种新颖的结构能同时实现对多硫化锂的物理阻挡与化学电催... 详细信息

锂硫电池的实际应用仍受制于一些挑战,包括氧化还原动力学缓慢和由此引发的穿梭效应等.为解决这些问题,我们巧妙合成了一种由FeS_(2)和分等级多孔碳结构(PCF)组成的隔膜修饰层.这种新颖的结构能同时实现对多硫化锂的物理阻挡与化学电催化效应.多硫化锂扩散实验证实PCF修饰的隔膜能够阻挡多硫化锂的渗透,而飞行时间二次离子质谱表明FeS_(2)能催化多硫化锂快速转化.因此,修饰后的锂硫电池表现出优异的倍率性能(5 C时比容量达764 mA h g^(-1))及显著的长循环稳定性(1 C时循环500次后比容量为698 mA h g^(-1)).值得注意的是,修饰后的锂硫电池最高面积容量为7.52 mA h cm^(-2),并且能够在较宽温域(-20至60°C)保持高循环稳定性.本研究为隔膜修饰层的高效应用提供了有价值的见解.

关键词：飞行时间二次离子质谱锂硫电池隔膜电催化作用分等级循环稳定性氧化还原动力学高效应用

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Deep Reinforcement Learning Based Multi-UAV Collision Avoidance with Causal Representation Learning

Deep Reinforcement Learning Based Multi-UAV Collision Avoida...

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International Conference on Big Data and Information Analytics (BigDIA)

作者： Gaofei Han Qingling Wu Boxi Wang Che Lin Jiafan Zhuang Wenji Li Zhifeng Hao Zhun Fan Department of Electronic Engineering Shantou University Shantou China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China

ISBN: (数字)9798350354621

ISBN: (纸本)9798350354638

The deep reinforcement learning-based multi-UAV collision avoidance and navigation methods have made significant progress. However, the fundamental challenge of those methods is their restricted capability to generalize beyond the specific scenario in which they are trained on. We find that the cause of the generalization failures is attributed to spurious correlation. To solve this generalization problem, we propose a causal representation learning method to identify the causal representations from images. Specifically, our method can neglect factors of variation that are irrelevant to the deep reinforcement learning task through causal intervention. Subsequently, the causal representations are fed into the policy network for action prediction. Extensive testing reveals that our proposed method exhibits better generalization results compared to state-of-the-art method in different testing scenes.

关键词： Representation learning Navigation Shape Image color analysis Deep reinforcement learning Trajectory Decorrelation Collision avoidance Mutual information Testing

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Enhancing electrocatalytic N_(2)-to-NH_(3) fixation by suppressing hydrogen evolution with alkylthiols modified Fe_(3)P nanoarrays

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Nano Research 2022年第2期15卷 1039-1046页

作者： Tong Xu Jie Liang Yuanyuan Wang Shaoxiong Li Zhaobai Du Tingshuai Li Qian Liu Yonglan Luo Fang Zhang Xifeng Shi Bo Tang Qingquan Kong Abdullah M.Asiri Chun Yang Dongwei Ma Xuping Sun Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 610054China College of Chemistry and Materials Science Sichuan Normal UniversityChengdu 610068China Key Laboratory for Special Functional Materials of Ministry of Education and School of Materials Science and EngineeringHenan UniversityKaifeng 475004China Institute for Advanced Study Chengdu UniversityChengdu 610106China National Engineering Research Center for Nanotechnology No.28 East Jiang Chuan RoadShanghai 200241China College of Chemistry Chemical Engineering and Materials ScienceShandong Normal UniversityJinan 250014China Chemistry Department Faculty of Science&Center of Excellence for Advanced Materials ResearchKing Abdulaziz UniversityP.O.Box 80203Jeddah 21589Saudi Arabia

Electrocatalytic N_(2) reduction provides an attractive alternative to Haber-Bosch process for artificial NH_(3) *** difficulty of suppressing competing proton reduction,however,largely impedes its practical ***,we design a hydrophobic octadecanethiol-modified Fe_(3)P nanoarrays supported on carbon paper(C18@Fe_(3)P/CP)to effectively repel water,concentrate N_(2),and enhance N_(2)-to-NH_(3) *** catalyst achieves an NH_(3) yield of 1.80×10^(-10) mol s^(-1)·cm^(-2) and a high Faradaic efficiency of 11.22%in 0.1 M Na_(2)SO_(4),outperforming the non-modified Fe_(3)P/CP(2.16×10^(-11) mol s^(-1)·cm^(-2),0.9%)***,C18@Fe_(3)P/CP renders steady Nrfixing activlty/selectivity in cycling test and exhibits durability for at least 25 ***-principles calculations suggest that the surface electronic structure and chemical activity of Fe_(3)P can be well tuned by the thiol modification,which facilitates N_(2) electroreduction activity and catalytic formation of NH_(3).

关键词： Fe_(3)P nanoarrays octadecanethiol surface modification ambient N_(2)reduction first-principles calculation

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Enhanced Dye Adsorption and Bacterial Removal of Magnetic Nanoparticle-Functionalized Bacterial Cellulose Acetate Membranes

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Journal of Renewable Materials 2024年第9期12卷 1605-1624页

作者： Heru Suryanto Daimon Syukri Fredy Kurniawan Uun Yanuhar Joseph Selvi Binoj Sahrul Efendi Fajar Nusantara Jibril Maulana Nico Rahman Caesar Komarudin Komarudin Centre of Advanced Material for Renewable Energy(CAMRY) Universitas Negeri MalangMalang65145Indonesia Department of Mechanical and Industrial Engineering Universitas Negeri MalangMalang65145Indonesia Department of Food and Agricultural Product Technology Faculty of Agricultural TechnologyAndalas UniversityPadang25163Indonesia Department of Chemistry Institut Teknologi Sepuluh NopemberSurabaya60111Indonesia Study Program of Aquatic Resources Management Faculty of Fisheries and Marine ScienceBrawijaya UniversityMalang65145Indonesia Institute of Mechanical Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical Sciences(SIMATS)Saveetha UniversityChennai602105India Faculty of Vocational Universitas Negeri MalangMalang65145Indonesia

Utilizing biomass waste as a potential resource for cellulose production holds promise in mitigating environmental *** current study aims to utilize pineapple biowaste extract in producing bacterial cellulose acetate-based membranes with magnetic nanoparticles(Fe_(3)O_(4)nanoparticles)through the fermentation and esterification process and explore its *** bacterial cellulose fibrillation used a high-pressure homogenization procedure,and membranes were developed incorporating 0.25,0.50,0.75,and 1.0 wt.%of Fe3O4 nanoparticles as magnetic nanoparticle for *** membrane characteristics were measured in terms of Scanning Electron Microscope,X-ray diffraction,Fourier Transform Infrared,Vibrating Sample Magnetometer,antibacterial activity,bacterial adhesion and dye adsorption *** results indicated that the surface morphology of membrane changes where the bacterial cellulose acetate surface looks *** crystallinity index of membrane increased from 54.34%to 68.33%,and the functional groups analysis revealed that multiple peak shifts indicated alterations in membrane functional ***,adding Fe_(3)O_(4)-NPs into membrane exhibits paramagnetic behavior,increases tensile strength to 73%,enhances activity against *** and ***,and is successful in removing bacteria from wastewater of the river to 67.4%and increases adsorption for anionic dyes like Congo Red and Acid Orange.

关键词： Bacterial cellulose dye adsorption Fe_(3)O_(4)nanoparticles membrane pineapple waste

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Online Detection of Anomalies in Temporal Knowledge Graphs with Interpretability

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Proceedings of the ACM on Management of Data 2024年第6期2卷 1-26页

作者： Jiasheng Zhang Rex Ying Jie Shao School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu CN Department of Computer Science Yale University New Haven CT USA Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen CN

Temporal knowledge graphs (TKGs) are valuable resources for capturing evolving relationships among entities, yet they are often plagued by noise, necessitating robust anomaly detection mechanisms. Existing dynamic graph anomaly detection approaches struggle to capture the rich semantics introduced by node and edge categories within TKGs, while TKG embedding methods lack interpretability, undermining the credibility of anomaly detection. Moreover, these methods falter in adapting to pattern changes and semantic drifts resulting from knowledge updates. To tackle these challenges, we introduce AnoT, an efficient TKG summarization method tailored for interpretable online anomaly detection in TKGs. AnoT begins by summarizing a TKG into a novel rule graph, enabling flexible inference of complex patterns in TKGs. When new knowledge emerges, AnoT maps it onto a node in the rule graph and traverses the rule graph recursively to derive the anomaly score of the knowledge. The traversal yields reachable nodes that furnish interpretable evidence for the validity or the anomalous of the new knowledge. Overall, AnoT embodies a detector-updater-monitor architecture, encompassing a detector for offline TKG summarization and online scoring, an updater for real-time rule graph updates based on emerging knowledge, and a monitor for estimating the approximation error of the rule graph. Experimental results on four real-world datasets demonstrate that AnoT surpasses existing methods significantly in terms of accuracy and interoperability. All of the raw datasets and the implementation of AnoT are provided in https://***/zjs123/ANoT.

关键词： anomaly detection graph summarization temporal knowledge graph

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Electronegativity-assisted optimized electronic structure of functionalized-Pt catalysts for boosting oxygen reduction kinetics

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Rare Metals 2024年第5期43卷 1965-1976页

作者： Jin Long Kai-Wen Zhuang Wei Liao Yan An Bin Wang Chen-Zhong Wu Jian-Xin Cao Qing Wang Jun-Song Chen Qing-Mei Wang Guizhou University Key Laboratory of Green Chemical and Clean Energy Technology School of Chemistry and Chemical EngineeringGuizhou UniversityGuiyang 550025China Guizhou University Engineering Research Center of Effcient Utilization for Industrial Waste Guizhou UniversityGuiyang 550025China Institute of Dual-Carbon and New Energy Technology Innovation and Development of Guizhou Province Guiyang 550025China Department of Dermatology West China HospitalSichuan UniversityChengdu610041China Institute for Catalysis Hokkaido UniversitySapporo 001-0021Japan School of Materials and Energy University of Electronic Science and Technology of ChinaChengdu 611731China Interdisciplinary Materials Research Center Institute for Advanced StudyChengdu UniversityChengdu 610106China

Opportunity to harmonize aspects of oxygen reduction reaction(ORR)performance and structure,and morphology,as well as composition,is urgent for the commercialization of proton exchange membrane fuel ***,we demonstrate the design and synthesis of a functionalized-supported-Pt catalyst(Pt@HNC)featuring a hollow nitrogen-modified dodecahedral carbon substrate obtained by a stress-induced-shrink tailoring *** as-obtained Pt@HNC catalyst possesses enhanced ORR per-formance,in particular with half-wave potential,mass activity(MA)and specific activity,which greatly exceed the commercial Pt/*** density functional theory(DFT)calculations further confirmed that the charge redistribution induced by the electronegativity differences improved the electron interaction between Pt and HNC *** optimized electronic structure of Pt weakens the reaction energy barrier on the Pt@HNC surface and adsorption of^(*)OH species,thus cooperatively improving the intrinsic activity toward ***,our work indeed provides a guide for the future design of functional nanomaterials in the field of catalysts and clean energy.

关键词： Proton exchange membrane fuel cell(PEMFC) Oxygen reduction catalysis Supported Pt-based catalyst Stability

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