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Influence of nitrogen implantation into HSS substrate on internal stress and adhesion strength of c-BN films

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中国有色金属学会会刊：英文版 2004年第Z1期14卷 286-290页

作者： ZHANG ping cai zhi-hai TAN Jun XIONG Wan-quan National Key Laboratory for Remanufacturing Academy of Armored Force Engineering Beijing 100072 China National Key Laboratory for Remanufacturing Academy of Armored Force Engineering Beijing 100072 China National Key Laboratory for Remanufacturing Academy of Armored Force Engineering Beijing 100072 China National Key Laboratory for Remanufacturing Academy of Armored Force Engineering Beijing 100072 China

Cubic boron nitride(c-BN) films were deposited on HSS substrate implanted with nitrogen ion by RF-magnetron sputtering. The films were analyzed by bending beam method, scratch test, XRD and AFM. The results show that the implantation of N ion can reduce the internal stress and improve the adhesion strength of the *** critical load comes to 16.92N, compared to 1.75N of c-BN film on the unimplanted HSS. AFM shows that the surface of the c-BN film on the implanted HSS is low in roughness and small in grain size. The phase structure of the nitrogen implanted layer was analyzed by XRD. The influence of nitrogen implanted layer on the internal stress and adhesion strength of c-BN films were also investigated.

关键词： cubic boron nitride film HSS ion implantation internal stress adhesion

High‑Entropy Electrode Materials:Synthesis,Properties and Outlook

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Nano-Micro Letters 2025年第1期17卷 472-506页

作者： Dongxiao Li Chang Liu Shusheng Tao Jieming cai Biao Zhong Jie Li Wentao Deng Hongshuai Hou Guoqiang Zou Xiaobo Ji College of Chemistry and Chemical Engineering Central South UniversityChangsha 410083People’s Republic of China School of Chemistry and Chemical Engineering Hunan Institute of EngineeringXiangtan 411104People’s Republic of China

High-entropy materials represent a new category of high-performance materials,first proposed in 2004 and extensively investigated by researchers over the past two *** definition of high-entropy materials has continuously *** the last ten years,the discovery of an increasing number of high-entropy materials has led to significant advancements in their utilization in energy storage,electrocatalysis,and related domains,accompanied by a rise in techniques for fabricating high-entropy electrode ***,the research emphasis has shifted from solely improving the performance of high-entropy materials toward exploring their reaction mechanisms and adopting cleaner preparation ***,the current definition of high-entropy materials remains relatively vague,and the preparation method of high-entropy materials is based on the preparation method of single metal/low-or medium-entropy *** should be noted that not all methods applicable to single metal/low-or medium-entropy materials can be directly applied to high-entropy *** this review,the definition and development of high-entropy materials are briefly ***,the classification of high-entropy electrode materials is presented,followed by a discussion of their applications in energy storage and catalysis from the perspective of synthesis ***,an evaluation of the advantages and disadvantages of various synthesis methods in the production process of different high-entropy materials is provided,along with a proposal for potential future development directions for high-entropy materials.

关键词： High-entropy Energy storage Electrode materials

Establishment of human cerebral organoid systems to model early neural development and assess the central neurotoxicity of environmental toxins

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Neural Regeneration Research 2025年第1期20卷 242-252页

作者： Daiyu Hu Yuanqing Cao Chenglin cai Guangming Wang Min Zhou Luying Peng Yantao Fan Qiong Lai Zhengliang Gao Fundamental Research Center Shanghai Yangzhi Rehabilitation Hospital(Shanghai Sunshine Rehabilitation Center)School of MedicineTongji UniversityShanghaiChina Institute of Geriatrics(Shanghai University) Affiliated Nantong Hospital of Shanghai University(The Sixth People’s Hospital of Nantong)Shanghai University School of MedicineNantongJiangsu ProvinceChina Shanghai Engineering Research Center of Organ Repair Shanghai University School of MedicineShanghaiChina Key Laboratory of Arrhythmias Ministry of EducationShanghai East HospitalSchool of MedicineTongji UniversityShanghaiChina

Human brain development is a complex process,and animal models often have significant *** address this,researchers have developed pluripotent stem cell-derived three-dimensional structures,known as brain-like organoids,to more accurately model early human brain development and *** enable more consistent and intuitive reproduction of early brain development,in this study,we incorporated forebrain organoid culture technology into the traditional unguided method of brain organoid *** involved embedding organoids in matrigel for only 7 days during the rapid expansion phase of the neural epithelium and then removing them from the matrigel for further cultivation,resulting in a new type of human brain organoid *** cerebral organoid system replicated the temporospatial characteristics of early human brain development,including neuroepithelium derivation,neural progenitor cell production and maintenance,neuron differentiation and migration,and cortical layer patterning and formation,providing more consistent and reproducible organoids for developmental modeling and toxicology *** a proof of concept,we applied the heavy metal cadmium to this newly improved organoid system to test whether it could be used to evaluate the neurotoxicity of environmental *** organoids exposed to cadmium for 7 or 14 days manifested severe damage and abnormalities in their neurodevelopmental patterns,including bursts of cortical cell death and premature *** exposure caused progressive depletion of neural progenitor cells and loss of organoid integrity,accompanied by compensatory cell proliferation at ectopic *** convenience,flexibility,and controllability of this newly developed organoid platform make it a powerful and affordable alternative to animal models for use in neurodevelopmental,neurological,and neurotoxicological studies.

关键词： cadmium cell death cell proliferation cortical development environmental toxins neural progenitor cells neurogenesis neurotoxicology organoids stem cells

On the A-Dependence of the Okamoto-Nolen-Schiffer Anomaly

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Chinese Physics Letters 1996年第5期13卷 343-346页

作者： SHEN Hong NING ping-zhi cai Chong-hai WANG Hong-yu Department of Physics Nankai UniversityTianjin 300071

A new version of the in-medium quark condensate is applied to study the Okamoto Nolen-Schiffer anomaly-The results show that the theoretical mass difference of mirror nuclei due to the neutron-proton mass diffei-ence ... 详细信息

关键词： Schiff mirror quark

Application of the improved dung beetle optimizer,muti-head attention and hybrid deep learning algorithms to groundwater depth prediction in the Ningxia area,China

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Atmospheric and Oceanic Science Letters 2025年第1期18卷 18-23页

作者： Jiarui cai Bo Sun Huijun Wang Yi Zheng Siyu Zhou Huixin Li Yanyan Huang Peishu Zong Collaborative Innovation Center on forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster Ministry of Education/Joint International Research Laboratory of Climate and Environment ChangeNanjing University of Information Science and TechnologyNanjingChina Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) ZhuhaiChina China Meteorological Administration Key Laboratory of Transportation MeteorologyNanjingChina Jiangsu Meteorological Observatory NanjingChina

Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical *** learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many *** this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,*** two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction *** addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.

关键词： Groundwater depth Multi-head attention Improved dung beetle optimizer CNN-LSTM CNN-GRU Ningxia

On the Red Edge of the δ Scuti Instability Strip

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Chinese Journal of Astronomy and Astrophysics 2002年第5期2卷 441-448页

作者： Yan Xu zhi-ping Li Li-cai Deng Da-Run Xiong National Astronomical Observatories Chinese Academy of SciencesBeijing 100012 Department of Physics Tianjin Normal UniversityTianjin 300073 Purple Mountain Observatory Chinese Academy of SciencesNanjing 210008

The δ Scuti star catalogue is used to derive the observational locations of such stars on the HR diagram. The theoretical and observational instability strips are compared to check the theoretical red edge obtained by considering nonlocal time-dependent convection theory. The observational instability strip almost overlaps with the theoretical one, but the observed blue and red envelopes are hotter than the theoretical edges. The distribution of δ Scuti stars in the pulsation strip is not uniform.

关键词： stars: variables: δ Sct - stars: oscillations ?stars: HR diagram

Carbon Balance in an Alpine Steppe in the Qinghai-Tibet Plateau

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Journal of Integrative Plant Biology 2009年第5期51卷 521-526页

作者： zhi-Yong Pei Hua Ouyang cai-ping Zhou Xing-Liang Xu The Administrative Center for China's Agenda 21 Beijing 100038 China Key Laboratory of Ecosystem Network Observation and Modelling Institute of Geographic Sciences and Natural Resources Research the Chinese Academy of Sciences Beijing 100101 China

Carbon fluxes were measured using a static chamber technique in an alpine steppe in the Qinghai-Tibet Plateau from July 2000 to July 2001. It was shown that carbon emissions decreased in autumn and increased in spring of the next year, with higher values in growth seasons than in winters. An exponential correlation （Ecarbon = 0.22（exp（0.09T）＋ In（0.31P ＋ 1））, R^2 = 0.77, P 〈 0.001） was shown between carbon emissions and environmental factors such as temperature （T） and precipitation （P）. Using the daily temperature （T） and total precipitation （R）, annual carbon emission from soil to the atmosphere was estimated to be 79.6 g C/m^2, 46% of which was emitted by microbial respiration. Considering an average net primary production of 92.5 g C/m^2 per year within the 2 year experiment, alpine steppes can take up 55.9 g CO2-C/m^2 per year. This indicates that alpine steppes are a distinct carbon sink, although this carbon reservoir was quite small.

关键词： alpine grassland carbon balance carbon flux net primary productivity Qinghai-Tibet Plateau

RE-SEGNN:recurrent semantic evidence-aware graph neural network for temporal knowledge graph forecasting

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Science China(Information Sciences) 2025年

作者： Wenyu cai Mengfan LI Xuanhua SHI Yuanxin FAN Quntao ZHU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology

Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both entity and relation embedding to make predictions, ignoring the semantic correlations among different entities and relations within the same timestamp. This can lead to random and nonsensical predictions when unseen entities or relations occur. Furthermore, many existing models exhibit limitations in handling highly correlated historical facts with extensive temporal depth. They often either overlook such facts or overly accentuate the relationships between recurring past occurrences and their current counterparts. Due to the dynamic nature of TKG, effectively capturing the evolving semantics between different timestamps can be *** address these shortcomings, we propose the recurrent semantic evidenceaware graph neural network(RE-SEGNN), a novel graph neural network that can learn the semantics of entities and relations simultaneously. For the former challenge, our model can predict a possible answer to missing quadruples based on semantics when facing unseen entities or relations. For the latter problem, based on an obvious established force, both the recency and frequency of semantic history tend to confer a higher reference value for the current. We use the Hawkes process to compute the semantic trend, which allows the semantics of recent facts to gain more attention than those of distant facts. Experimental results show that RE-SEGNN outperforms all SOTA models in entity prediction on 6 widely used datasets, and 5 datasets in relation prediction. Furthermore, the case study shows how our model can deal with unseen entities and relations.

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Porous Organic Cage‑Based Quasi‑Solid‑State Electrolyte with Cavity‑Induced Anion‑Trapping Effect for Long‑Life Lithium Metal Batteries

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Nano-Micro Letters 2025年第2期17卷 376-386页

作者： Wei-Min Qin Zhongliang Li Wen‑Xia Su Jia‑Min Hu Hanqin Zou zhixuan Wu zhiqin Ruan Yue‑Peng cai Kang Li Qifeng Zheng School of Chemistry South China Normal UniversityGuangzhou 510006People’s Republic of China Key Laboratory of Functional Metal‑Organic Compounds of Hunan Province College of Chemistry and Material ScienceHengyang Normal UniversityHengyang 421008People’s Republic of China Guangzhou Key Laboratory of Energy Conversion and Energy Storage Materials Guangzhou 510006People’s Republic of China

Porous organic cages(POCs)with permanent porosity and excellent host–guest property hold great potentials in regulating ion transport behavior,yet their feasibility as solid-state electrolytes has never been testified in a practical ***,we design and fabricate a quasi-solid-state electrolyte(QSSE)based on a POC to enable the stable operation of Li-metal batteries(LMBs).Benefiting from the ordered channels and cavity-induced anion-trapping effect of POC,the resulting POC-based QSSE exhibits a high Li+transference number of 0.67 and a high ionic conductivity of 1.25×10^(−4) S cm^(−1) with a low activation energy of 0.17 *** allow for homogeneous Li deposition and highly reversible Li plating/stripping for over 2000 *** a proof of concept,the LMB assembled with POC-based QSSE demonstrates extremely stable cycling performance with 85%capacity retention after 1000 ***,our work demonstrates the practical applicability of POC as SSEs for LMBs and could be extended to other energy-storage systems,such as Na and K batteries.

关键词： Porous organic cage Cavity-induced anion-trapping Quasi-solid-state electrolyte Homogeneous Li+flux Lithium metal battery