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Recent development in electrode design for wide-temperature supercapacitors

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science China Chemistry 2025年第1期68卷 96-117页

作者： Jianhong Liu Qiang Zhou Yuxiao Lin Xinsheng Zhao Guangmin Zhou Xiaoxiao Li school of Physics and Electronic Engineering Jiangsu Normal University Tsinghua-Berkeley shenzhen institute & Tsinghua shenzhen International Graduate school Tsinghua University

An ever-increasing market demand has stimulated the soaring enthusiasm of researchers to develop wide-temperature supercapacitors(SCs). The active electrode is one of the most important parts of SC, which is directly related to the energy density,power transmission and long-term cyclability of the device in the wide-temperature environment. Compared with the SC electrodes aimed for room-temperature application, the SC electrodes for operating at wide-temperature scene often face greater challenges. In this review, the main challenges of SC electrodes under various temperature conditions, including low, high and cross-fade temperatures, are summarized. The relevant performance decay and failure mechanisms of wide-temperature SC electrodes are analyzed. In addition, this review deals with the recent studies and developments in robust wide-temperature SC electrodes with respect to the rational design of electrode structures and the exploitation of advanced active materials. Finally, the future directions for exploring reliable wide-temperature SCs are also proposed.

关键词： supercapacitor wide-temperature electrode design active materials

A Multi-Objective Particle Swarm Optimization Algorithm Based on Decomposition and Multi-Selection Strategy

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Computers, Materials & Continua 2025年第1期82卷 997-1026页

作者： Li Ma Cai Dai Xingsi Xue Cheng Peng school of Computer science Shaanxi Normal UniversityXi’an710119China Fujian Provincial Key Laboratory of big data Mining and Applications Fujian University of TechnologyFuzhou350118China Information Construction and Management Center and institute of Artificial Intelligence and Educational New Productivity Ningxia Normal UniversityGuyuan756099China

The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization *** the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search ***,two update strategies based on decomposition are used to update the evolving population and external archive,***,a multiselection strategy is *** first strategy is for the subspace without a non-dominated *** the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global *** second strategy is for the subspace with a non-dominated *** the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local *** third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated *** the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to ***,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search ***,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test *** results show that the proposed algorithm has better performance.

关键词： Multi-objective optimization multi-objective particle swarm optimization decomposition multi-selection strategy

COMPrompter: reconceptualized segment anything model with multiprompt network for camouflaged object detection

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science China(Information sciences) 2025年第1期68卷 189-203页

作者： Xiaoqin ZHANG Zhenni YU Li ZHAO Deng-Ping FAN Guobao XIAO Zhejiang Province Key Laboratory of Intelligent Informatics for Safety and Emergency Wenzhou University Nankai International Advanced research institute (shenzhen FUTIAN) College of Computer science Nankai University school of Computer science and Technology Tongji University

We rethink the segment anything model(SAM) and propose a novel multiprompt network called COMPrompter for camouflaged object detection(COD). SAM has zero-shot generalization ability beyond other models and can provide an ideal framework for COD. Our network aims to enhance the single prompt strategy in SAM to a multiprompt strategy. To achieve this, we propose an edge gradient extraction module, which generates a mask containing gradient information regarding the boundaries of camouflaged objects. This gradient mask is then used as a novel boundary prompt, enhancing the segmentation process. Thereafter, we design a box-boundary mutual guidance module, which fosters more precise and comprehensive feature extraction via mutual guidance between a boundary prompt and a box prompt. This collaboration enhances the model's ability to accurately detect camouflaged objects. Moreover, we employ the discrete wavelet transform to extract high-frequency features from image embeddings. The high-frequency features serve as a supplementary component to the multiprompt ***, our COMPrompter guides the network to achieve enhanced segmentation results, thereby advancing the development of SAM in terms of COD. Experimental results across COD benchmarks demonstrate that COMPrompter achieves a cutting-edge performance, surpassing the current leading model by an average positive metric of 2.2% in COD10K. In the specific application of COD, the experimental results in polyp segmentation show that our model is superior to top-tier methods as well. The code will be made available at https://***/guobaoxiao/COMPrompter.

关键词： segment anything model camouflaged object detection boundary prompt

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The effects of exercise interventions on brain-derived neurotrophic factor levels in children and adolescents:a meta-analysis

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Neural Regeneration research 2025年第5期20卷 1513-1520页

作者： Xueyun Shao Longfei He Yangyang Liu Physical Education school Shenzhen UniversityShenzhenGuangdong ProvinceChina shenzhen institute of Neuroscience ShenzhenGuangdong ProvinceChina

Brain-derived neurotrophic factor is a crucial neurotrophic factor that plays a significant role in brain health. Although the vast majority of meta-analyses have confirmed that exercise interventions can increase brain-derived neurotrophic factor levels in children and adolescents, the effects of specific types of exercise on brain-derived neurotrophic factor levels are still controversial. To address this issue, we used meta-analytic methods to quantitatively evaluate, analyze, and integrate relevant studies. Our goals were to formulate general conclusions regarding the use of exercise interventions, explore the physiological mechanisms by which exercise improves brain health and cognitive ability in children and adolescents, and provide a reliable foundation for follow-up research. We used the Pub Med, Web of science, science Direct, Springer, Wiley Online Library, Weipu, Wanfang, and China National Knowledge Infrastructure databases to search for randomized controlled trials examining the influences of exercise interventions on brain-derived neurotrophic factor levels in children and adolescents. The extracted data were analyzed using Review Manager 5.3. According to the inclusion criteria, we assessed randomized controlled trials in which the samples were mainly children and adolescents, and the outcome indicators were measured before and after the intervention. We excluded animal experiments, studies that lacked a control group, and those that did not report quantitative results. The mean difference(MD;before versus after intervention) was used to evaluate the effect of exercise on brain-derived neurotrophic factor levels in children and adolescents. Overall, 531 participants(60 children and 471 adolescents, 10.9–16.1 years) were included from 13 randomized controlled trials. Heterogeneity was evaluated using the Q statistic and I^(2) test provided by Review Manager software. The meta-analysis showed that there was no heterogeneity among the studies(P = 0.67,

关键词： adolescents brain-derived neurotrophic factor children exercise meta-analysis randomized controlled trials

Sulfolane‑Based Flame‑Retardant Electrolyte for High‑Voltage Sodium‑Ion Batteries

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Nano-Micro Letters 2025年第2期17卷 498-516页

作者： Xuanlong He Jie Peng Qingyun Lin Meng Li Weibin Chen Pei Liu Tao Huang Zhencheng Huang Yuying Liu Jiaojiao Deng Shenghua Ye Xuming Yang Xiangzhong Ren Xiaoping Ouyang Jianhong Liu Biwei Xiao Jiangtao Hu Qianling Zhang Graphene Composite research Center College of Chemistry and Environmental EngineeringShenzhen UniversityShenzhen 518060People’s Republic of China Center of Electron Microscopy State Key Laboratory of Silicon and Advanced Semiconductor MaterialsSchool of Materials Science and EngineeringZhejiang UniversityHangzhou 310027People’s Republic of China GRINM(Guangdong)research institute for Advanced Materials and Technology FoshanGuangdong 528051People’s Republic of China College of Energy Engineering Zhejiang UniversityHangzhouZhejiang 310027People’s Republic of China school of Materials science and Engineering Xiangtan UniversityXiangtan 411105People’s Republic of China shenzhen Eigen-Equation Graphene Technology Co.Ltd Shenzhen 518000People’s Republic of China

Sodium-ion batteries hold great promise as next-generation energy storage ***,the high instability of the electrode/electrolyte interphase during cycling has seriously hindered the development of *** particular,an unstable cathode–electrolyte interphase(CEI)leads to successive electrolyte side reactions,transition metal leaching and rapid capacity decay,which tends to be exacerbated under high-voltage ***,constructing dense and stable CEIs are crucial for high-performance *** work reports localized high-concentration electrolyte by incorporating a highly oxidation-resistant sulfolane solvent with non-solvent diluent 1H,1H,5H-octafluoropentyl-1,1,2,2-tetrafluoroethyl ether,which exhibited excellent oxidative stability and was able to form thin,dense and homogeneous *** excellent CEI enabled the O3-type layered oxide cathode NaNi_(1/3)Mn_(1/3)Fe_(1/3)O_(2)(NaNMF)to achieve stable cycling,with a capacity retention of 79.48%after 300 cycles at 1 C and 81.15%after 400 cycles at 2 C with a high charging voltage of 4.2 *** addition,its nonflammable nature enhances the safety of *** work provides a viable pathway for the application of sulfolane-based electrolytes on SIBs and the design of next-generation high-voltage electrolytes.

关键词： Sodium-ion batteries Sulfolane-based electrolyte High voltage Layered oxide cathode Flame retardant

Review of precipitation strengthening in ultrahigh-strength martensitic steel

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International Journal of Minerals,Metallurgy and Materials 2025年第2期32卷 256-269页

作者： Zhihao Tian Chunlei Shang Chaolei Zhang Xiaoye Zhou Honghui Wu Shuize Wang Guilin Wu Junheng Gao Jiaming Zhu Xinping Mao institute for Carbon Neutrality University of Science and Technology BeijingBeijing 100083China institute of Steel Sustainable Technology Liaoning Academy of MaterialsShenyang 110004China Department of Materials science and Engineering Shenzhen MSU-BIT UniversityShenzhen 518172China institute of Materials Intelligent Technology Liaoning Academy of MaterialsShenyang 110004China school of Civil Engineering Shandong UniversityJinan 250061China

Martensite is an important microstructure in ultrahigh-strength steels,and enhancing the strength of martensitic steels often involves the introduction of precipitated phases within the martensitic *** considerable research efforts devoted to this area,a systematic summary of these advancements is *** review focuses on the precipitates prevalent in ultrahigh-strength martensitic steel,primarily carbides(e.g.,MC,M_(2)C,and M_(3)C)and intermetallic compounds(e.g.,Ni Al,Ni_(3)X,and Fe_(2)Mo).The precipitation-strengthening effect of these precipitates on ultrahigh-strength martensitic steel is discussed from the aspects of heat treatment processes,microstructure of precipitate-strengthened martensite matrix,and mechanical ***,a perspective on the development of precipitation-strengthened martensitic steel is presented to contribute to the advancement of ultrahigh-strength martensitic *** review highlights significant findings,ongoing challenges,and opportunities in the development of ultrahigh-strength martensitic steel.

关键词： ultrahigh-strength martensitic steel precipitation strengthening mechanical property carbide intermetallic compound

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材料科学中小样本量的机器学习策略（英文）

science China Materials 2025年

作者：陶秋伶于金鑫穆翔宇贾雪施荣佩姚志富王翠萍张海军刘兴军 school of Materials science and Engineering and Institute of Materials Genome & Big Data Harbin Institute of Technology school of Computer science & Technology Harbin Institute of Technology Advanced institute for Materials research (WPI-AIMR) Tohoku University Department of Materials science and Engineering College of Materials Xiamen University State Key Laboratory of Advanced Welding and Joining Harbin Institute of Technology

机器学习(ML)因其较低的计算成本和强大的预测能力,已广泛应用于新材料的设计与开发.近年来,机器学习在材料科学中的不足逐渐显现,其中最主要的问题是数据稀缺.在有限数据的情况下,构建可靠且准确的机器学习模型是一项挑战.此外,由于材...

机器学习(ML)因其较低的计算成本和强大的预测能力,已广泛应用于新材料的设计与开发.近年来,机器学习在材料科学中的不足逐渐显现,其中最主要的问题是数据稀缺.在有限数据的情况下,构建可靠且准确的机器学习模型是一项挑战.此外,由于材料数据积累的速度较慢,小样本量问题将长期存在于材料科学中.因此,回顾并分析针对小样本学习的策略,对促进机器学习在材料科学中的发展具有重要意义.本文系统地梳理了材料科学中小样本学习策略的研究进展,包括集成学习、无监督学习、主动学习和迁移学习.并提出了未来研究的方向,如少样本学习和虚拟样本生成.更为重要的是,我们强调了将材料领域知识嵌入机器学习的重要性,并阐述了实现该策略的基本思路.

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Dual‑Donor‑Induced Crystallinity Modulation Enables 19.23% Efficiency Organic Solar Cells

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Nano-Micro Letters 2025年第3期17卷 376-388页

作者： Anhai Liang Yuqing Sun Sein Chung Jiyeong Shin Kangbo Sun Chaofeng Zhu Jingjing Zhao Zhenmin Zhao Yufei Zhong Guangye Zhang Kilwon Cho Zhipeng Kan Center On Nanoenergy research Institute of Science and Technology for Carbon Peak&NeutralitySchool of Physical Science&TechnologyGuangxi UniversityNanning 530004People’s Republic of China Department of Chemical Engineering Pohang University of Science and TechnologyPohang 37673South Korea College of New Materials and New Energies Shenzhen Technology UniversityShenzhen 518118People’s Republic of China Zhejiang Engineering research Center for Fabrication and Application of Advanced Photovoltaic Materials School of Materials Science and EngineeringNingboTech UniversityNingbo 315100People’s Republic of China State Key Laboratory of Featured Metal Materials and Life-Cycle Safety for Composite Structures Nanning 530004People’s Republic of China

Trap-assisted charge recombination is one of the primary limitationsof restricting the performance of organic solar cells. However, effectivelyreducing the presence of traps in the photoactive layer remains ***, wide bandgap polymer donor PTzBI-dF is demonstrated as an effectivemodulator for enhancing the crystallinity of the bulk heterojunction active layerscomposed of D18 derivatives blended with Y6, leading to dense and orderedmolecular packings, and thus, improves photoluminescence quenching *** a result, the photovoltaic devices exhibit reduced trap-assisted charge recombinationlosses, achieving an optimized power conversion efficiency of over 19%.Besides the efficiency enhancement, the devices comprised of PTzBI-dF as athird component simultaneously attain decreased current leakage, improved chargecarrier mobilities, and suppressed bimolecular charge recombination, leading toreduced energy losses. The advanced crystalline structures induced by PTzBI-dFand its characteristics, such as well-aligned energy level, and complementaryabsorption spectra, are ascribed to the promising performance *** findings suggest that donor phase engineering is a feasible approach to tuning the molecular packings in the active layer, providingguidelines for designing effective morphology modulators for high-performance organic solar cells.

关键词： Trap-assisted charge recombination Photoluminescence Miscibility Current leakage Power conversion efficiency

Domain generalization with semi-supervised learning for people-centric activity recognition

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science China(Information sciences) 2025年第1期68卷 171-188页

作者： Jing LIU Wei ZHU Di LI Xing HU Liang SONG Academy for Engineering & Technology Fudan University Shanghai East-bund research institute on Networking Systems of AI school of Optoelectronic Information and Computer Engineering University of Shanghai for Science & Technology

People-centric activity recognition is one of the most critical technologies in a wide range of real-world applications,including intelligent transportation systems, healthcare services, and brain-computer interfaces. Large-scale data collection and annotation make the application of machine learning algorithms prohibitively expensive when adapting to new tasks. One way of circumventing this limitation is to train the model in a semi-supervised learning manner that utilizes a percentage of unlabeled data to reduce the labeling burden in prediction tasks. Despite their appeal, these models often assume that labeled and unlabeled data come from similar distributions, which leads to the domain shift problem caused by the presence of distribution gaps. To address these limitations, we propose herein a novel method for people-centric activity recognition,called domain generalization with semi-supervised learning(DGSSL), that effectively enhances the representation learning and domain alignment capabilities of a model. We first design a new autoregressive discriminator for adversarial training between unlabeled and labeled source domains, extracting domain-specific features to reduce the distribution gaps. Second, we introduce two reconstruction tasks to capture the task-specific features to avoid losing information related to representation learning while maintaining task-specific consistency. Finally, benefiting from the collaborative optimization of these two tasks, the model can accurately predict both the domain and category labels of the source domains for the classification task. We conduct extensive experiments on three real-world sensing datasets. The experimental results show that DGSSL surpasses the three state-of-the-art methods with better performance and generalization.

关键词： activity recognition deep learning domain generalization semi-supervised learning adversarial training

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