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Gallium-based liquid metals for lithium-ion batteries

Interdisciplinary Materials 2022年第3期1卷 354-372页

作者： Bin-Wei Zhang Long Ren Yun-Xiao Wang Xun Xu Yi Du Shi-Xue Dou College of Chemistry and Chemical Engineering Chongqing UniversityChongqingChina Center of Advanced Energy Technology and Electrochemistry Chongqing UniversityChongqingChina Institute for Superconducting and Electronic Materials Australian Institute of Innovative MaterialsUniversity of WollongongNorth WollongongNew South WalesAustralia State Key Laboratory of Advanced Technology for Materials Synthesis and Processing International School of Materials Science and EngineeringWuhan University of TechnologyWuhanChina BUAA-UOW Joint Research Centre and School of Physics Beihang UniversityBeijingChina

Lithium-ion batteries(LIBs)are one of the most exciting inventions of the 20th century and have been widely employed in modern *** have powered many of our electronics,such as laptop computers,smartphones,and even large-scale energy storage *** the development of modern technology,next-generation LIBs with higher energy density are in demand.A number of electrode materials with high theoretical capacity,including Sn,Si,Li metal anode,and S cathode materials,have been ***,they usually suffer from structural or interface failure during cycling,limiting their practical ***-based liquid metals(LMs)possess self-healing capability,fluidity,and metallic advantages so they have been employed as self-healing skeletons or interfacial protective layers to minimize the negative impact of volume expansion or dendritic growth on the electrode ***,the features of Ga-based LMs are briefly discussed to indicate their potential for battery *** addition,recent developments on Ga-based LMs applied in LIBs have been summarized,including from the aspects of anodes,cathodes,and ***,future opportunities and challenges for the development of Ga-based LMs in LIBs are highlighted.

关键词： anode cathode electrolyte gallium-based liquid metals lithium-ion batteries

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Strongly coupled Te-SnS_(2)/MXene superstructure with self-autoadjustable function for fast and stable potassium ion storage

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Journal of Energy Chemistry 2021年第10期30卷 416-424,I0011页

作者： Hongyang Sun Yelong Zhang Xiaodan Xu Jianwen Zhou Fan Yang Hao Li Hao Chen Yucheng Chen Zheng Liu Zhenping Qiu Da Wang Lipo Ma Jiawei Wang Qingguang Zeng Zhangquan Peng School of Applied Physics and Materials Wuyi UniversityJiangmen 529020GuangdongChina Jiangmen Advanced Battery Material Engineering and Technology Research Center Jiangmen 529020GuangdongChina Laboratory of Advanced Spectro-Electrochemistry and Lithium-Ion Batteries Dalian National Laboratory for Clean EnergyDalian Institute of Chemical PhysicsChinese Academy of SciencesDalian 116023LiaoningChina

Potassium-ion batteries(PIBs)are a promising candidate for next-generation electric energy storage applications because of the abundance and low cost of ***,the development of PIBs is limited by sluggish kinetics and huge volume expansion of anodes,leading to poor rate capability and cycling ***,an advanced superstructure anode,including Te-doped SnS_(2) nanosheets uniformly anchored on MXene surface(Te-SnS_(2)/MXene),is rationally designed for the first time to boost K^(+)storage *** with strong interface interaction and self-autoadjustable interlayer spacings,the Te-SnS_(2)/MXene can efficiently accelerate electron/ion transfer,accommodate volume expansion,inhibit crack formation,and improve pseudocapacitive contribution during ***,the novel Te-SnS_(2)/MXene anode delivers a high reversible capacity(343.2 mAh g^(-1) after 50 cycles at0.2 A g^(-1)),outstanding rate capability(186.4 mAh g^(-1) at 20 A g^(-1)),long cycle stability(165.8 mAh g^(-1)after 5000 cycles at 10 A g^(-1) with a low electrode swelling rate of only 15.4%),and reliable operation in flexible full *** present Te-SnS_(2)/MXene becomes among the best transition metal-based anode materials for PIBs reported to date.

关键词： Tin disulfide MXene Tellurium doping Potassium-ion battery

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A Cross-Modal Interactive Memory Network Based on Fine-Grained Medical Feature Extraction for Radiology Report Generation

A Cross-Modal Interactive Memory Network Based on Fine-Grain...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Xitong Ma Yuansen Kuang Lin Yuan Cheng Tian Yijie Zeng Song Liu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Radiology report generation is an essential task in the medical field, which aims to automate the generation of medical terminology descriptions of radiology images. However, this task currently suffers from several problems: 1) existing methods need to manually build knowledge graphs or templates (consuming time and effort) to introduce medical or prior knowledge to assist in report generation; 2) previous models cannot handle the problem of data bias well (anomaly reports and anomaly descriptions make up only a tiny portion of the dataset), causing the models to ignore the learning of anomaly descriptions easily; 3) existing approaches cannot robustly supervise the model, resulting in incomplete and inconsistent reports being generated. To address these issues, we propose a cross-modal interactive memory network based on fine-grained medical feature extraction. In our model, we design a cross-modal interactive memory network to automatically store and remember the required medical text knowledge and use this medical knowledge to help generate reports. Furthermore, we design an abnormal medical knowledge enhancement module to enhance the learning of abnormal fine-grained knowledge through the interaction of disease topics and their states to interact with text features. In addition, we design a cross-modal joint semantic loss unit to reduce semantic differences between different features and improve the visual representation ability of the model. We experimented and evaluated our model on MIMIC-CXR and IU-Xray datasets to compare with other baseline models.

关键词： Knowledge engineering Visualization Terminology Semantics MIMICs Memory management Radiology Feature extraction Data models Biomedical imaging

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Advancing Human Fall Detection Through Motion Data from Smart Wearable Sensors and CNN-BiGRU Model

Advancing Human Fall Detection Through Motion Data from Smar...

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IEEE International Conference on Software Engineering and Service sciences (ICSESS)

作者： Sakorn Mekruksavanich Wikanda Phaphan Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350389166

ISBN: (纸本)9798350389173

Falls pose a significant health risk, especially for older adults, resulting in serious injuries, limited movement, and higher medical expenses. Precise and fast identification of falls is essential for immediate healthcare treatment and avoidance of additional complications. This paper presents a new method to improve the identification of individual falls by using movement inputs from smart wearable sensors and a hybrid deep learning model that combines convolutional neural networks (CNN) and bidirectional gated recurrent units (BiGRU). Intelligent wearable sensors, including accelerometers and gyroscopes, are used to collect real-time movement data from different parts of the human body. The movement signals gathered are subjected to preprocessing and segmentation into windows of a particular length to extract pertinent information. An advanced neural network structure comprising CNN layers for extracting spatial features and BiGRU layers for representing temporal sequences is specifically intended to acquire the distinctive patterns related to fall occurrences efficiently. The proposed CNN-BiGRU model is trained on a varied dataset that includes simulated falls and activities of daily living conducted by several people. The model's effectiveness is assessed using established measures, such as accuracy, sensitivity, specificity, and F1-score. The experimental findings demonstrate that the CNN-BiGRU model has a notable level of accuracy in recognizing instances of falls. Combining in-telligent wearable sensors and advanced deep-learning algorithms creates a dependable and highly effective system for detecting falls. The suggested strategy can significantly enhance the well-being of those prone to falls by offering prompt aid and mitigating the impact of injuries caused by falls.

关键词： Accuracy Sensitivity Neural networks Feature extraction Real-time systems Convolutional neural networks Older adults Wearable sensors Injuries Software engineering

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ERC DMSP: Emotion Recognition in Conversation Based on Dynamic Modeling of Speaker Personalities

ERC DMSP: Emotion Recognition in Conversation Based on Dynam...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xiaoyang Li Zhenyu Yang Zhijun Li Yiwen Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Emotion Recognition in Conversations (ERC) is a widely applicable task, whether in emotional chatbots or recommender systems for dialog scenarios. The ability of systems to accurately identify human emotions is a crucial component in ERC success. In everyday conversations, a speaker’s emotional expression is closely tied to their individual characteristics and personality traits. Furthermore, individuals exhibit significant variations in their expressions of the same emotion. Traditional methods for ERC often overlook these personality differences and dynamic changes, leading to unreliable results. To address this issue, we propose a conversation emotion recognition model based on dynamic modeling of speaker’s personality (ERC DMSP). In our approach, to extract a speaker’s personality profile, we design a personality capture module to model the personalities of individuals based on their historical utterances. Moreover, a bidirectional influence exists between a speaker’s personality tendencies and their linguistic behavior. The speaker’s personality profile is thus continually updated based on the ongoing dialog. Additionally, since the personalities of other speakers in the conversation can also affect the form and intensity of a speaker’s emotional expression, we construct a conversation emotion representation utilizing group personality traits. We then comprehensively incorporate the speaker’s personality into the emotion representation of each utterance at both the group and individual levels through the personality engagement module. Throughout this process, the representation of dialog and the speaker’s personality undergo hierarchical updates, leading to more accurate conversation emotion recognition. We conducted extensive experiments on four commonly used public benchmark datasets for ERC to evaluate the effectiveness of our proposed ERC DMSP model. The results demonstrate its efficacy, as it exhibits a significant improvement over other methods in popular

关键词： Measurement Emotion recognition Accuracy Codes Neural networks Oral communication Color

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Effect of Mg addition on temper embrittlement in 2.25Cr–1Mo steel doped with 0.056% P–Mg segregation behavior at grain boundary

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Journal of Iron and Steel research International 2021年第10期28卷 1259-1267页

作者： Xiao-bing Li Xin Dong Peng-xiang Zhao Wei-wei Xing Lei-lei Ding Nan-nan Zhang Ying-che Ma Kui Liu Shi-changxu Innovation Center for Advanced Materials Institute of Metal ResearchChinese Academy of ScienceShenyang110016LiaoningChina CAS Key Laboratory of Nuclear Materials and Safety Assessment Shenyang110014LiaoningChina School of Materials Science and Engineering Shenyang University of TechnologyShenyang110870LiaoningChina School of Materials Science and Engineering University of Science and Technology of ChinaHefei230026AnhuiChina

To verify the microalloying function and segregation behavior of trace Mg at grain boundary in steel,the 2.25Cr–1Mo steel doped with 0.056%P containing different Mg contents was refined with a vacuum-induction *** effects of trace Mg addition on the temper embrittlement susceptibility of 2.25Cr–1Mo steel were studied by step-cooling test and the segregation behavior of Mg at grain boundary was explored by Auger electron *** is shown that P-induced temper embrittlement susceptibility can be reduced after subjecting to step-cooling treatment with trace Mg addition,mainly benefited from the segregation of Mg at grain *** segregation can decrease the segregation amounts of P and S,especially for P,and increase the grain boundary cohesion,reducing the adverse effect on temper embrittlement caused by P and S.

关键词： 2.25Cr-1Mo steel Temper embrittlement Mg P Segregation Auger electron spectroscopy

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Chebyshev-Amplitude Distribution in X/Ku-Band Shared Aperture Antenna Array With Open, Short, and Load-ended Series Feed for Airborne Synthetic Aperture Radar Applications

Chebyshev-Amplitude Distribution in X/Ku-Band Shared Apertur...

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Space, Aerospace and Defence Conference (SPACE), IEEE

作者： Praveena Kati Venkata Kishore Kothapudi Department of Electronics and Communication Engineering Center of Excellence Advanced RF Microwave & Wireless Communications Vignan’s Foundation for Science Technology and Research (VFSTR) Andhra Pradesh India

ISBN: (数字)9798350367386

ISBN: (纸本)9798350367393

This article presents a dual-band shared aperture antenna array for ASAR applications, comprising eight elements in Chebyshev configurations for X/Ku-Bands. Achieving a scanning angle of ±25 degrees, it uses 0.7λ inter-element spacing to mitigate mutual coupling and ensure good isolation. The array demonstrates SLL performances > -25 dB and quantified isolation between bands and conditions. Chebyshev synthesis yields SLL values of -26/-24 dB (X-band) and -27/-26.8 dB (Ku-band). Measured gains for Chebyshev synthesis are approximately 16.2/16.5 dBi (Open-Ended), 13.1/14.2 dBi (Short-Ended), and 15.3/15.2 dBi (Load-Ended) across X/Ku-Bands. Impedance bandwidth is wide, at 100 MHz (X-Band) and 230 MHz (Ku-Band).

关键词： Dual band Bandwidth Chebyshev approximation Gain measurement Radar antennas Aperture antennas Impedance

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Understanding the intrinsic mechanism of the giant magnetostriction in binary and alloyed FeGa solid solutions

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Physical Review B 2024年第1期109卷 014417-014417页

作者： Jiejue Niu Keyu Yan Yichen Xu Yuye Wu Enke Liu Zhongheng Fu Jiheng Li Xuexu Gao Xing Mu Bingjie Liu Xiaoxiao Wang Yunquan Li Jingmin Wang Chengbao Jiang School of Materials Science and Engineering Beihang University Beijing 100191 People's Republic of China State Key Laboratory for Magnetism Institute of Physics Chinese Academy of Sciences Beijing 100190 People's Republic of China Beijing Advanced Innovation Center for Materials Genome Engineering Institute for Advanced Materials and Technology University of Science and Technology Beijing Beijing 100083 People's Republic of China State Key Laboratory for Advanced Metals and Materials University of Science and Technology Beijing Beijing 100083 People's Republic of China Department of Functional Material Research Central Iron and Steel Research Institute Beijing 100081 People's Republic of China

Doping nonmagnetic Ga atoms into Fe leads to the enhancement in magnetostriction by ∼10 times in the FeGa solid solutions; the fundamental mechanism of the anomalous enhancement has attracted substantial attention. However, current experimental methods are difficult to reveal the origin because of their inability in the electronic and atomic scales. In this work, we utilized first-principles calculations to unveil the origin of giant magnetostriction in FeGa solid solutions. Ga doping results in the random substitution of Fe by Ga in the disordered A2 matrix and the formation of L60 nanoheterogeneities simultaneously. The former weakens the strength of Fe–Fe metallic bonding framework, thus leading to the lattice softening as presented by the sharp reduction in elastic constant c′ [c′=(c11−c12)/2]. The latter strengthens the magnetoelastic coupling effect by regulating the density of states of 3d orbits of Fe atoms inside and adjacent to the nanoheterogeneities, resulting in the 3 times larger magnetoelastic coupling coefficient −b1. The two effects synergistically offer the giant magnetostriction in FeGa solid solutions based on the relationship of λ001=−(b1/3c′). Furthermore, the influence of elemental doping, including Co, Ni, P, and Tb, on magnetostriction is systematically studied. It is demonstrated that Tb is the sole alloying element which can enhance the magnetoelastic coupling effect strongly, allowing FeGa-Tb supercell to present an ultrahigh magnetostriction of 738 ppm, which is over 2 times larger than that of the FeGa supercells. This work offers insights into the origin of giant heterogeneous magnetostriction in Fe-based solid solutions, which benefits the development of high-performance FeGa-based magnetostrictive materials.

关键词： Density of states First-principles calculations Magnetoelastic effect Magnetostriction

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Harnessing the power of artificial intelligence for human living organoid research

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Bioactive Materials 2024年第12期42卷 140-164页

作者： Hui Wang Xiangyang Li Xiaoyan You Guoping Zhao Master Lab for Innovative Application of Nature Products National Center of Technology Innovation for Synthetic BiologyTianjin Institute of Industrial BiotechnologyChinese Academy of Sciences(CAS)Tianjin300308PR China Henan Engineering Research Center of Food Microbiology College of food and bioengineeringHenan University of Science and TechnologyLuoyang471023PR China Haihe Laboratory of Synthetic Biology Tianjin300308PR China CAS-Key Laboratory of Synthetic Biology CAS Center for Excellence in Molecular Plant SciencesInstitute of Plant Physiology and EcologyChinese Academy of SciencesShanghai200032PR China CAS Key Laboratory of Quantitative Engineering Biology Shenzhen Institute of Synthetic BiologyShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055PR China Engineering Laboratory for Nutrition Shanghai Institute of Nutrition and HealthChinese Academy of SciencesShanghai200031PR China

As a powerful paradigm,artificial intelligence(AI)is rapidly impacting every aspect of our day-to-day life and scientific research through interdisciplinary *** human organoids(LOs)have a great potential for in vitro reshaping many aspects of in vivo true human organs,including organ development,disease occurrence,and drug *** date,AI has driven the revolutionary advances of human organoids in life science,precision medicine and pharmaceutical science in an unprecedented ***,we provide a forward-looking review,the frontiers of LOs,covering the engineered construction strategies and multidisciplinary technologies for developing LOs,highlighting the cutting-edge achievements and the prospective applications of AI in LOs,particularly in biological study,disease occurrence,disease diagnosis and prediction and drug screening in preclinical ***,we shed light on the new research trends harnessing the power of AI for LO research in the context of multidisciplinary *** aim of this paper is to motivate researchers to explore organ function throughout the human life cycle,narrow the gap between in vitro microphysiological models and the real human body,accurately predict human-related responses to external stimuli(cues and drugs),accelerate the preclinical-to-clinical transformation,and ultimately enhance the health and well-being of patients.

关键词： Artificial intelligence Human living organoids Organoid intelligence In vitro miniature organ models Multidisciplinary techniques Preclinical-to-clinical transformation

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Spatiotemporal Visualization of Cell Membrane with Amphiphilic Aggregation-Induced Emission-Active Sensor

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CCS Chemistry 2022年第5期4卷 1619-1632页

作者： Youheng Zhang Qi Wang Zhirong Zhu Weijun Zhao Chenxu Yan Zhenxing Liu Ming Liu Xiaolei Zhao He Tian Wei-Hong Zhu Key Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering Feringa Nobel Prize Scientist Joint Research CenterShanghai Key Laboratory of Functional Materials ChemistryInstitute of Fine ChemicalsFrontiers Science Center for Materiobiology and Dynamic ChemistrySchool of Chemistry and Molecular EngineeringEast China University of Science&TechnologyShanghai 200237

High-fidelity spatiotemporal monitoring of the cell membrane is critically ***,commercial fluorescence probes are stalked by the aggregation-caused quenching(ACQ)effect,and the reported aggregation-induced emission(AIE)-active probes are always limited by nonspecific aggregations in the biological ***,we report the rational molecular design of a stateof-the-art amphiphilic AIE luminogen(AIEgen),membrane tracker QMC12,using a core quinolinemalononitrile(QM)structure to suppress the ACQ effect,incorporate a positively charged pyridinium to regulate dispersity and strengthen the binding affinity to the negatively charged cell membrane,and extend the alky chain to improve the anchoring ability to the cell *** membrane tracker QMC12,which disperses well in both hydrophilic and lipophilic environments,not only achieves minimal background interference and high signal-to-noise(S/N)ratio in the“ultrafast”visualization of the cell membrane,but also endows a“wash-free”***,it realizes a spatial three dimensional(3D)view in a multicellular spheroid model and morphology changes over ***,QMC12 avoids false staining and signal loss and unprecedentedly achieves the direct observation of the cell membrane’s microstructure,which could elucidate spatiotemporal 3D model studies of the intercellular information exchange.

关键词： aggregation-induced emission amphiphilicity cell membrane imaging high-fidelity spatiotemporal dimensions

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