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Addressing electrode degradation issue in high negative to positive electrode capacity ratio lithium-ion batteries using water-in-salt electrolyte

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advanced Composites and Hybrid Materials 2025年第3期8卷 1-17页

作者： Zhanadilov, Orynbay Akhmetova, Aktilek Son, Junehyuk Yu, Jun Ho Kim, Mingony Chung, Kyung Yoon Kim, Hee Jae Bakenov, Zhumabay Yashiro, Hitoshi Myung, Seung-Taek Department of Nanotechnology and Advanced Materials Engineering & Sejong Battery Institute Hybrid Materials Research Center Sejong University Seoul Republic of Korea Energy Storage Research Center Korea Institute of Science and Technology Seoul Republic of Korea Division of Energy and Environment Technology KIST School Korea University of Science and Technology Seoul Republic of Korea Department of Chemical and Materials Engineering School of Engineering and Digital Sciences Nazarbayev University Astana Kazakhstan National Laboratory Astana Nazarbayev University Astana Kazakhstan Department of Chemistry and Bioengineering Iwate University Morioka Iwate Japan

In this study, we investigate the underlying causes of degradation in Li4Ti5O12//LiMn2O4 cells with an N/P ratio of 0.9 utilizing 19.44 m LiN(SO2CF3)2 and 8.33 m LiN(SO2CF2CF3)2 (Li(TFSI)0.7(BETI)0.3∙2H2O), a hydrate-melt electrolyte. We identify the formation of hydrofluoric acid (HF) during electrochemical reactions as a key factor leading to both Mn dissolution from the LiMn2O4 cathode and rapid self-discharge at room temperature. To address these challenges, we apply a calcium fluoride (CaF2) coating to the electrodes, designed to scavenge HF by reacting to form calcium bifluoride (Ca(HF2)2). This reaction underscores the unique quasi-non-aqueous nature of Li(TFSI)0.7(BETI)0.3∙2H2O, which facilitates chemical processes not possible in traditional aqueous electrolytes. Electrochemical evaluations demonstrate that the CaF2-coated electrode exhibits improved capacity retention and higher coulombic efficiency than their uncoated counterparts. Despite these enhancements, the rapid self-discharge issue remains, indicating that additional factors contribute to this phenomenon and require further investigation. Our findings highlight the potential of water-in-salt systems, particularly the Li(TFSI)0.7(BETI)0.3∙2H2O electrolyte, in advancing lithium-ion battery technology by leveraging their distinct chemical environment. This study provides insights into the mechanisms affecting the stability and performance of hydrate-melt electrolyte for exploiting quasi-non-aqueous systems in energy-storage applications.

关键词： CaF2 coating HF formation Hydrate-melt electrolyte Lithium-ion batteries Mn dissolution Water-in-salt electrolyte

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Mind the Õ: asymptotically better, but still impractical, quantum distributed algorithms

arXiv

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arXiv 2023年

作者： Kerger, Phillip A. Bernal Neira, David E. Izquierdo, Zoe Gonzalez Rieffel, Eleanor G. Department of Applied Mathematics and Statistics Johns Hopkins University United States Research Institute of Advanced Computer Science USRA United States Quantum Artificial Intelligence Laboratory NASA Ames Research Center United States Davidson School of Chemical Engineering Purdue University United States

The CONGEST and CONGEST-CLIQUE models have been carefully studied to represent situations where the communication bandwidth between processors in a network is severely limited. Messages of only O(log(n)) bits of information each may be sent between processors in each round. The quantum versions of these models allow the processors instead to communicate and compute with quantum bits under the same bandwidth limitations. This leads to the following natural research question: What problems can be solved more efficiently in these quantum models than in the classical ones? Building on existing work, we contribute to this question in two ways. Firstly, we present two algorithms in the Quantum CONGEST-CLIQUE model of distributed computation that succeed with high probability;one for producing an approximately optimal Steiner Tree, and one for producing an exact directed minimum spanning tree, each of which uses Õ(n1/4) rounds of communication and Õ(n9/4) messages, where n is the number of nodes in the network. The algorithms thus achieve a lower asymptotic round and message complexity than any known algorithms in the classical CONGEST-CLIQUE model. At a high level, we achieve these results by combining classical algorithmic frameworks with quantum subroutines. An existing framework for using a distributed version of Grover’s search algorithm to accelerate triangle finding lies at the core of the asymptotic speedup. Secondly, we carefully characterize the constants and logarithmic factors involved in our algorithms as well as related algorithms, otherwise commonly obscured by Õ notation. The analysis shows that some improvements are needed to render both our and existing related quantum and classical algorithms practical, as their asymptotic speedups only help for very large values of n. © 2023, CC BY.

关键词： Bandwidth

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Efficient Batch Verification and Privacy-Preserving Data Aggregation Scheme in V2G Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Wang, Congcong Wang, Chen Shen, Jian Vasilakos, Athanasios V. Wang, Bin Wang, Weizheng Department of Software Engineering Nanjing University of Information Science and Technology Nanjing210044 China the Zhejiang Key Laboratory of Digital Fashion and Data Governance the Zhejiang Provincial International Cooperation Base for Science and Technology on Cloud Computing Security and Data Aggregation Zhejiang Sci-Tech University Hangzhou310018 China Zhejiang Provincial Innovation Center of Advanced Textile Technology Shaoxing312000 China College of Computer Science and Information Technology IAU Saudi Arabia Grimstad4879 Norway Network and Data Security Hangzhou310053 China Computer Science Department City University of Hong Kong Hong Kong the Division of Computer Science University of Aizu Fukushima965–8580 Japan

In recent years, against the backdrop of smart grids, vehicle-to-grid (V2G) interaction has become a hot topic. With the increasing number of new energy vehicles, handling massive amounts of electricity transaction data has become an urgent problem in need of resolution. Ensuring the security of the vast quantities of electricity transaction data has emerged as a significant challenge. Therefore, two data aggregation schemes are proposed in this paper, both supporting batch verification and privacy protection in centralized and distributed scenarios, respectively. Firstly, an improved Paillier cryptosystem is utilized to achieve lightweight encryption in the proposed schemes. In addition, a novel distributed data aggregation structure that combines a large number of geographically distributed fog nodes with centralized cloud servers is introduced. This structure not only meets the requirements of lightweight encryption and resistance against single point failures but also reduces the communication complexity of distributed data aggregation to O(n). According to the security proofs and experimental analysis, it can be demonstrated that both proposed schemes are effective and secure, meeting the lightweight requirements of modern smart grid systems. © 2025 IEEE. All rights reserved,

关键词： Data aggregation

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EMC of Linear and Non-Linear Loads in Different EM Environments;An Issue of Power Quality

EMC of Linear and Non-Linear Loads in Different EM Environme...

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Asia-Pacific Symposium on Electromagnetic Compatibility (APEMC)

作者： Agung Cahyadi Putra Ilman Sulaeman Deny Hamdani Niek Moonen School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Electrical Engineering Mathematics and Computer Science (EEMCS) University of Twente Enschede the Netherlands School of Electrical Engineering and Informatics Research Center of New and Renewable Energy ITB Institut Teknologi Bandung Bandung Indonesia

ISBN: (数字)9784885523472

ISBN: (纸本)9798350349498

Renewable energy sources (RES) are being widely implemented in electricity systems to decarbonize energy supplies, including those in the most remote areas. In the context of the rural electrification effort in Indonesia, the impact of several common household appliances on grid voltages was determined. Modifications to the supply impedance have emulated the contextual issues that were discussed in previous literature, like extremely long distribution cables or ad-hoc modifications of the RES. The voltage rms fluctuation and voltage total harmonic distortion were examined. The results show that voltage deviation, noise, and harmonic were highly affected in the case of nonlinear loads. In the case of linear loads, as expected, the voltage quality will depend on the supply. Therefore, understanding the source impedance characteristics can be used to help maintain the EMC while using power-efficient nonlinear loads.

关键词： Total harmonic distortion Renewable energy sources Voltage fluctuations Noise Power quality Power system harmonics Electromagnetic compatibility

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Electron charge qubit with 0.1 millisecond coherence time

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Nature Physics 2024年第1期20卷 116-122页

作者： Zhou, Xianjing Li, Xinhao Chen, Qianfan Koolstra, Gerwin Yang, Ge Dizdar, Brennan Huang, Yizhong Wang, Christopher S. Han, Xu Zhang, Xufeng Schuster, David I. Jin, Dafei Center for Nanoscale Materials Argonne National Laboratory LemontIL United States Pritzker School of Molecular Engineering University of Chicago ChicagoIL United States Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions CambridgeMA United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA United States James Franck Institute and Department of Physics University of Chicago ChicagoIL United States Department of Electrical and Computer Engineering Northeastern University BostonMA United States Department of Applied Physics Stanford University StanfordCA United States Department of Physics and Astronomy University of Notre Dame Notre DameIN United States

Electron charge qubits are compelling candidates for solid-state quantum computing because of their inherent simplicity in qubit design, fabrication, control and readout. However, electron charge qubits built on conventional semiconductors and superconductors suffer from severe charge noise that limits their coherence time to the order of one microsecond. Here we report electron charge qubits that exceed this limit, based on isolated single electrons trapped on an ultraclean solid neon surface in a vacuum. Quantum information is encoded in the motional states of an electron that is strongly coupled with microwave photons in an on-chip superconducting resonator. The measured relaxation and coherence times are both on the order of 0.1 ms, surpassing all existing charge qubits and rivalling state-of-the-art superconducting transmon qubits. The simultaneous strong coupling of two qubits with a common resonator is also demonstrated, as the first step towards two-qubit entangling gates for universal quantum computing. © 2023, The Author(s), under exclusive licence to Springer Nature Limited.

关键词： Superconducting resonators

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MMST-LSTM: Leveraging Radar Echo Prediction for Emerging Consumer Applications in Edge Computing

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IEEE Transactions on Consumer Electronics 2025年

作者： Wu, Mengjia Xiao, Bo Yang, Zhiyun Sun, Jiawei Liu, Qi Zhang, Yonghong Liu, Xiaodong Nanjing University of Information Science and Technology School of Software Nanjing210044 China Nanjing University of Information Science and Technology School of Chemistry and Materials Science Nanjing210044 China Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing210044 China Nanjing University of Information Science and Technology School of Automation Nanjing210044 China Edinburgh Napier University School of Computing EdinburghEH10 5DT United Kingdom

With the increasing frequency of extreme weather events, there is a growing demand from the public for rapid and accurate short-term heavy precipitation forecasts. This study proposes a lightweight deep learning model, MMST-LSTM, which integrates Multiscale Context Feature Fusion Mechanism (MCFFM) and Mixed-Domain Attention Fusion Mechanism (MAFUM). While maintaining high prediction accuracy, MMST-LSTM significantly improves forecast speed. The MMST-LSTM model is particularly suitable for deployment in Mobile Edge Computing (MEC) environments, enabling fast localized forecasting. Experimental results demonstrate MMST-LSTM’s excellent predictive performance on two radar echo datasets, particularly in rapid response and handling localized data. Moreover, leveraging Smart Data-Driven Modeling (SDDM) technology with consumer-generated data enhances its application potential in smart consumer electronics products, providing an efficient tool for disaster weather alerts. This study introduces an innovative meteorological forecasting method and provides robust technical support for accurate weather warning systems, offering consumers timely and reliable weather information. This enables them to make more informed decisions, effectively reducing the potential risks and economic losses caused by extreme climate events. © 1975-2011 IEEE.

关键词： Deep learning

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T cell epitopes in SARS-CoV-2 proteins are substantially conserved in the Omicron variant

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Cellular & Molecular Immunology 2022年第3期19卷 447-448页

作者： Seong Jin Choi Dong-Uk Kim Ji Yun Noh Sangwoo Kim Su-Hyung Park Hye Won Jeong Eui-Cheol Shin Division of Infectious Disease Department of Internal MedicineSeoul National University HospitalSeoulRepublic of Korea Graduate School of Medical Science and Engineering Korea Advanced Institute of Science and Technology(KAIST)DaejeonRepublic of Korea Division of Infectious Diseases Department of Internal MedicineKorea University College of MedicineSeoulRepublic of Korea Department of Biomedical Systems Informatics Graduate School of Medical ScienceBrain Korea 21 ProjectYonsei University College of MedicineSeoulRepublic of Korea Department of Internal Medicine Chungbuk National University College of MedicineCheongjuRepublic of Korea The Center for Viral Immunology Korea Virus Research InstituteInstitute for Basic Science(IBS)DaejeonRepublic of Korea

In November 2021,the Omicron variant(B.1.1.529)emerged and was designated a variant of concern(VOC)by the World Health ***,Omicron was reported to extensively escape neutralizing antibodies elicited by COVID-19 vaccination or natural infection[1,2,3].However,whether Omicron evades the T cell immunity elicited by COVID-19 vaccination or natural infection remains to be *** address this issue,we analyzed the amino acid sequences of T cell epitopes identified from the original SARS-CoV-2 strain(Wuhan-Hu-1)in the Omicron variant(hCoV-19/South Africa/CERI-KRISP-K032284/2021).

关键词： neutral conserved elucidated

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Underwater Image Enhancement Utilizing Adaptive Color Correction and Model Conversion for Dehazing

SSRN

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SSRN 2023年

作者： Li, Yiming Li, Daoyu Gao, Zhijie Wang, Shuai Jiao, Qiang Bian, Liheng School of Information and Electronics Advanced Research Institute of Multidisciplinary Science Beijing Institute of Technology Beijing100081 China Center for Strategic Research on Frontier and Interdisciplinary Engineering Science and Technology Beijing Institute of Technology Beijing100081 China School of Computer Science and Engineering Beihang University Beijing100083 China Ministry of Public Security Information and Communication Center Beijing100741 China

Underwater imaging is typically affected by absorption and scattering, which can lead to color distortion and image blurring. However, no universal color constancy algorithm for correcting underwater images exists due to the wide variation in underwater scenes. Furthermore, methods based on learning to improve underwater image quality often suffer from a lack of ground truth, which can compromise the validity and authenticity of restored images. In this study, we propose an adaptive underwater image enhancement technique that corrects image color and reduces haze using a valid dataset with ground truth. Specifically, we first use hue channel statistics from underwater images to build a dataset of color-corrected images from different underwater scenes. We use this dataset to train an Unet-like network for adaptive color correction. After color correction, underwater images often exhibit characteristics similar to hazy terrestrial images. We refer to this phenomenon as "model conversion". The hazy terrestrial images have corresponding ground truth. Therefore, we train a Transformer-like network with hazy terrestrial image datasets to remove haze in underwater images. Our method is more robust for different underwater scenes and elegantly solves the problem of lacking ground truth. A series of experiments demonstrate that our method achieves superior performance compared to state-of-the-art methods in terms of both visual quality and quantitative metrics. © 2023, The Authors. All rights reserved.

关键词： Underwater imaging

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Predicting candidate genes from phenotypes, functions and anatomical site of expression

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Bioinformatics 2021年第6期37卷 853-860页

作者： Chen, Jun Althagafi, Azza Hoehndorf, Robert Computational Bioscience Research Center (CBRC) Computer Electrical & Mathematical Sciences and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955 Saudi Arabia Computer Science Department College of Computers and Information Technology Taif University Taif 26571 Saudi Arabia

Motivation: Over the past years, many computational methods have been developed to incorporate information about phenotypes for disease–gene prioritization task. These methods generally compute the similarity between a patient’s phenotypes and a database of gene-phenotype to find the most phenotypically similar match. The main limitation in these methods is their reliance on knowledge about phenotypes associated with particular genes, which is not complete in humans as well as in many model organisms, such as the mouse and fish. Information about functions of gene products and anatomical site of gene expression is available for more genes and can also be related to phenotypes through ontologies and machine-learning models. Results: We developed a novel graph-based machine-learning method for biomedical ontologies, which is able to exploit axioms in ontologies and other graph-structured data. Using our machine-learning method, we embed genes based on their associated phenotypes, functions of the gene products and anatomical location of gene expression. We then develop a machine-learning model to predict gene–disease associations based on the associations between genes and multiple biomedical ontologies, and this model significantly improves over state-of-the-art methods. Furthermore, we extend phenotype-based gene prioritization methods significantly to all genes, which are associated with phenotypes, functions or site of expression. Availability and implementation: Software and data are available at https://***/bio-ontology-researchgroup/DL2Vec. © 2021 Oxford University Press. All rights reserved.

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Patch Target Guided Dual-Branch Deep Multiple Instance Learning for 3D MRI Analysis

Patch Target Guided Dual-Branch Deep Multiple Instance Learn...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Mingzhe Dai Xiaoshuang Shi Xiaofeng Zhu Tingrui Pan Kang Li School of Software Engineering University of Science and Technology of China Hefei China West China Biomedical Big Data Center West China Hospital of Sichuan University Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Sichuan Artificial Intelligence Research Institute Yibin China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Deep multiple instance learning (MIL) has attracted considerable attention in medical image analysis, since it only requires image-level labels for model training without using fine-grained (or patch) annotations. Unfortunately, MIL-based methods might lose some significant patch features. Although pseudo-label-based methods, which assign a pre-defined label to each patch, can explore more patch-level features, they might bring label noise and make the patch-level features lose diversity, thereby possibly restricting the model performance. To overcome this issue, we propose a novel gradient-based patch target generation (PTG) module to dynamically produce a feature vector for each patch as its target. Additionally, based on the PTG module, we propose a patch-target guided dual-branch deep MIL framework for 3D MRI data analysis, where both the two branches consist of a CNN model to extract patch-level features, an attention module to interpret the significance of patches, and a bag-level classifier, while the second branch also contains the PTG module to generate patch targets of patches. Moreover, the two branches are alternatively updated in our framework, resulting in a bi-level optimization problem, and thus we design a bi-level optimization algorithm to solve our proposed objective function. Extensive experiments demonstrate the superior classification and interpretation performance of the proposed framework over recent state-of-the-art methods. Codes are available at https://***/daimz1213/mil2024.

关键词： Training Analytical models Solid modeling Three-dimensional displays Annotations Magnetic resonance imaging Feature extraction Vectors Optimization Biomedical imaging

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