检索结果-南通市图书馆

Applications of in-situ solid-state nuclear magnetic resonance in methanol to olefins reaction

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Science China Chemistry 2025年第1期68卷 134-151页

作者： jing Niu Shutao Xu Yingxu Wei Zhongmin Liu National Engineering Research Center of Lower-Carbon Catalysis Technology Dalian Institute of Chemical PhysicsChinese Academy of Sciences State Key Laboratory of Catalysis Dalian Institute of Chemical Physics Chinese Academy of Sciences University of Chinese Academy of Sciences

The methanol-to-olefins（MTO） reaction offers an alternative pathway for the production of low-carbon olefins from non-oil feedstocks. Fundamental research has been impeded by a lack of comprehensive understanding of its underlying mechanism,despite the significant progress made in industry. In-situ solid-state nuclear magnetic resonance（ss NMR） spectroscopy has emerged as a pivotal tool, offering crucial insights into key species under real-time operando conditions. Furthermore, the host–guest interaction between zeolites or surface species residing on zeolites and the reactant/active intermediates is revealed by the combination of in-situ13C MAS NMR and 2D correlation spectroscopy. Moreover, recent technological advancements in hyperpolarization（HP） methods, including HP129Xe NMR and dynamic nuclear polarization（DNP）, have significantly improved the sensitivity of ss NMR, enabling detailed structural and kinetic analysis as well as the detection of trace species. In this feature article, we summarized recent advancements in（in-situ） ss NMR spectroscopy applied to MTO reaction processes,encompassing mechanistic investigations at various stages and the intricate host–guest interactions. These theoretical insights into the dynamic evolution of MTO reactions lay a solid foundation for the optimization of catalytic processes and the development of efficient catalysts, thereby advancing the techniques towards more sustainable and economical production route for olefins.

关键词： MTO zeolites in-situ ssNMR reactive intermediates reaction mechanism

Merging polymers of intrinsic microporosity and porous carbon-based zinc oxide composites in novel mixed matrix membranes for efficient gas separation

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Green Energy & Environment 2025年第1期10卷 203-213页

作者： Muning Chen Jiemei Zhou jing Ma Weigang Zheng Guanying Dong Xin Li Zhihong Tian Yatao Zhang jing Wang Yong Wang School of Chemical Engineering Zhengzhou UniversityZhengzhou450001HenanChina State Key Laboratory of Materials-Oriented Chemical Engineering and College of Chemical Engineering Nanjing Tech UniversityNanjing211816JiangsuChina Singapore Membrane Technology Centre Nanyang Environment and Water Research InstituteNanyang Technological UniversitySingapore637141Singapore Engineering Research Center for Nanomaterials Henan UniversityKaifeng475004HenanChina State Key Laboratory of Coking Coal Resources Green Exploitation Zhengzhou UniversityZhengzhou 450001China School of Energy and Environment Southeast UniversityNanjing210096JiangsuChina

Mixed matrix membranes(MMMs)have demonstrated significant promise in energy-intensive gas separations by amalgamating the unique properties of fillers with the facile processability of ***,achieving a simultaneous enhancement of permeability and selectivity remains a formidable challenge,due to the difficulty of achieving an optimal match between polymers and *** this study,we incorporate a porous carbon-based zinc oxide composite(C@ZnO)into high-permeability polymers of intrinsic microporosity(PIMs)to fabricate *** dipole–dipole interaction between C@ZnO and PIMs ensures their exceptional compatibility,mitigating the formation of non-selective voids in the resulting ***,C@ZnO with abundant interconnected pores can provide additional low-resistance pathways for gas transport in *** a result,the CO_(2) permeability of the optimized C@ZnO/PIM-1 MMMs is elevated to 13,215 barrer,while the CO_(2)/N_(2) and CO_(2)/CH4 selectivity reached 21.5 and 14.4,respectively,substantially surpassing the 2008 Robeson upper ***,molecular simulation results further corroborate that the augmented membrane gas selectivity is attributed to the superior CO_(2) affinity of C@*** summary,we believe that this work not only expands the application of MMMs for gas separation but also heralds a paradigm shift in the application of porous carbon materials.

关键词： Mixed matrix membranes Polymers of intrinsic microporosity CO_(2)separation Porous carbon materials

Two paths, one goal: mechanism of calcium homeostasis to balance plant growth and immunity

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Science China Life Sciences 2025年第1期68卷 306-308页

作者： Aziz Ul Ikram Yanping jing Huan Chen Jian Chen International Genome Center Jiangsu University Joint Center for Single Cell Biology School of Agriculture and BiologyShanghai Jiao Tong University

Calcium(Ca2+) is an essential nutrient and a cellular signal in plants. The level of free Ca2+in the apoplast is ～10,000-fold higher than that in the cytoplasm at the resting state. This large Ca2+level gradient across the membrane between the interior and exterior can cause Ca2+overload and toxicity. Plants have evolved mechanisms to export Ca2+out of the cytoplasm or store it in intracellular organelles, such as endoplasmic reticulum（ER）, mitochondria, and vacuole.

关键词：

Anomaly Detection of Controllable Electric Vehicles through Node Equation against Aggregation Attack

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2025年第1期82卷 427-442页

作者： jing Guo Ziying Wang Yajuan Guo Haitao Jiang State Grid Jiangsu Electric Power Co. Ltd.Research InstituteState Grid Corporation of ChinaNanjing211100China

The rapid proliferation of electric vehicle(EV)charging infrastructure introduces critical cybersecurity vulnerabilities to power grids *** study presents an innovative anomaly detection framework for EV charging stations,addressing the unique challenges posed by third-party aggregation *** approach integrates node equations-based on the parameter identification with a novel deep learning model,xDeepCIN,to detect abnormal data reporting indicative of aggregation *** employ a graph-theoretic approach to model EV charging networks and utilize Markov Chain Monte Carlo techniques for accurate parameter *** xDeepCIN model,incorporating a Compressed Interaction Network,has the ability to capture complex feature interactions in sparse,high-dimensional charging *** results on both proprietary and public datasets demonstrate significant improvements in anomaly detection performance,with F1-scores increasing by up to 32.3%for specific anomaly types compared to traditional methods,such as wide&deep and DeepFM(Factorization-Machine).Our framework exhibits robust scalability,effectively handling networks ranging from 8 to 85 charging ***,we achieve real-time monitoring capabilities,with parameter identification completing within seconds for networks up to 1000 *** research contributes to enhancing the security and reliability of renewable energy systems against evolving cyber threats,offering a comprehensive solution for safeguarding the rapidly expanding EV charging infrastructure.

关键词： Anomaly detection electric vehicle aggregation attack deep cross-network

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

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

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

同方期刊数据库

Dielectric-ion-conductive ZnNb2O6 layer enabling rapid desolvation and diffusion for dendrite-free Zn metal batteries

在线全文

学校读者我要写书评

暂无评论

Journal of Energy Chemistry 2025年第1期100卷 693-701页

作者： Haifeng Yang Jian Wang Panpan Zhang Xiaomin Cheng Qinghua Guan jing Dong Bixian Chen Lujie Jia jing Zhang Yongzheng Zhang Yunjian Liu Hongzhen Lin School of Materials Science and Engineering Jiangsu University i-Lab & CAS Key Laboratory of Nanophotonic Materials and Devices Suzhou Institute of Nano-Tech and Nano-Bionics Chinese Academy of Sciences Helmholtz Institute Ulm (HIU) Karlsruhe Institute of Technology (KIT) School of Materials Science and Engineering Xi'an University of Technology State Key Laboratory of Chemical Engineering East China University of Science and Technology

Rechargeable aqueous zinc-metal batteries （AZMBs） are promising candidates for large-scale energy storage systems due to their low cost and high ***,their performance and sustainability are significantly hindered by the sluggish desolvation kinetics at the electrode/electrolyte interface and the corresponding hydrogen evolution reaction where active water molecules tightly participate in the Zn（H2O）62+solvation ***,learnt from self-generated solid electrolyte interphase （SEI） in anodes,the dielectric but ion-conductive zinc niobate nanoparticles artificial layer is constructed on metallic Zn surface （ZNB@Zn）,acting as a rapid desolvation *** zincophilic and dielectric-conductive properties of ZNB layer accelerate interfacial desolvation/diffusion and suppress surface corrosion or dendrite formation,achieving uniform Zn plating/stripping behavior,as confirmed by electronic/optical microscopies and interface spectroscopical measurements together with theoretical ***,the as-prepared ZNB@Zn electrode exhibits excellent cycling stability of over 2000 h and robust reversibility （99.54%） even under high current density and depth of discharge ***,the assembled ZNB@Zn-based full cell displays high capacity-retention rate of 80.21%after 3000 cycles at 5 A g-1and outstanding rate performance up to 10 A *** large-areal pouch cell is stabilized for hundreds of cycles,highlighting the bright prospects of the dielectric but ion-conductive layer in further application of AZMBs.

关键词： Zn metal battery Dielectric artificial layer Rapid ion diffusion Zincophilic diffusion Active water inhibition

Prospects on mangrove knowledge services in the smart era: From plant atlas to knowledge graphs

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Science China Earth Sciences 2025年第1期68卷 111-127页

作者： Zhi-Wei HOU Wenlong jing Cheng-Zhi QIN Ji YANG Qing XIA Xiaoling YIN Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and Application Guangzhou Institute of Geography Guangdong Academy of Sciences Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of Sciences College of Resources and Environment University of Chinese Academy of Sciences School of Traffic & Transportation Engineering Changsha University of Science and Technology

Mangroves are crucial to the ecological security of the Earth and human well-being. Their management, conservation, and restoration are of great importance and necessitate the support of spatio-temporal information and multidisciplinary knowledge such as biology and ecology. Traditional knowledge services such as plant atlas provide illustrated textual knowledge of mangroves. However, this kind of service is oriented to information retrieval and is incapable of effectively mining and utilizing fragmented knowledge from multi-source heterogeneous data, facing the problem of “massive data, rare knowledge”. Knowledge graphs are capable of extracting, organizing, and fusing the knowledge contained in massive data into semantic networks that can be understood and computed by computers. They provide a solution for the realization of intelligent knowledge services. Focusing on the urgent need for mangrove knowledge acquisition, formal representation, and intelligent services, this paper proposes a research prospect on mangrove knowledge graphs and knowledge services. We first analyze the similarities and differences between various domain-specific concepts of Tupu. On this basis, we define the mangrove knowledge graph as a large-scale knowledge base that integrates multi-disciplinary knowledge and spatio-temporal information with mangrove ecosystems as the core. Then, we propose a research framework for mangrove knowledge services that can realize the transformation from multi-modal data to intelligent knowledge services, including multiple research levels such as ubiquitous data sensing and aggregation, knowledge organization and graph construction, and intelligent mangrove knowledge services. Subsequently, the methods and workflow for constructing mangrove knowledge graphs are introduced. Finally, we discuss the challenges and possible future directions of mangrove knowledge services in the smart era, including the construction of a mangrove knowledge system that integra

关键词： Mangrove Tupu Knowledge graph Multi-modal Knowledge service

An efficient quantum proactive incremental learning algorithm

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 49-57页

作者： Lingxiao Li jing Li Yanqi Song Sujuan Qin Qiaoyan Wen Fei Gao State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications School of Cyberspace Security Beijing University of Posts and Telecommunications

In scenarios where a large amount of data needs to be learned, incremental learning can make full use of old knowledge, significantly reduce the computational cost of the overall learning process, and maintain high performance. In this paper, taking the MaxCut problem as our example, we introduce the idea of incremental learning into quantum computing, and propose a Quantum Proactive Incremental Learning algorithm(QPIL). Instead of a one-off training of quantum circuit, QPIL contains a multi-phase training on gradually-increased subgraphs of all vertices, proactively reducing large-scale problems to smaller ones to solve in steps, providing an efficient solution for MaxCut. Specifically, some vertices and corresponding edges are randomly selected for training to obtain optimized parameters of the quantum circuit at first. Then, in each incremental phase, the remaining vertices and corresponding edges are gradually added and the parameters obtained from the previous phase are reused in the parameter initialization of the current phase. We perform experiments on 120 different small-scale graphs, and it shows that QPIL performs superior to prevalent quantum and classical baselines in terms of approximation ratio(AR), time cost, anti-forgetting, and solving stability. In particular, QPIL's AR surpasses 20% of mainstream quantum baselines, and the time cost is less than 1/5 of them. The idea of QPIL is expected to inspire efficient and high-quality solutions in large-scale Max Cut and other combinatorial optimization problems.

关键词： variational quantum algorithm incremental learning multi-phase training MaxCut quantum computing

Unraveling brain aging through the lens of oral microbiota

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Neural Regeneration Research 2025年第7期20卷 1930-1943页

作者： Qinchao Hu Si Wang Weiqi Zhang jing Qu Guang-Hui Liu State Key Laboratory of Membrane Biology Institute of ZoologyChinese Academy of SciencesBeijingChina Hospital of Stomatology Guanghua School of StomatologyGuangdong Provincial Key Laboratory of StomatologySun Yat-sen UniversityGuangzhouGuangdong ProvinceChina Advanced Innovation Center for Human Brain Protection and National Clinical Research Center for Geriatric Disorders Xuanwu HospitalCapital Medical UniversityBeijingChina Aging Translational Medicine Center International Center for Aging and CancerBeijing Municipal Geriatric Medical Research CenterXuanwu HospitalCapital Medical UniversityBeijingChina CAS Key Laboratory of Genomic and Precision Medicine Beijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijingChina University of Chinese Academy of Sciences BeijingChina Institute for Stem Cell and Regeneration Chinese Academy of SciencesBeijingChina Aging Biomarker Consortium BeijingChina State Key Laboratory of Stem Cell and Reproductive Biology Institute of ZoologyChinese Academy of SciencesBeijingChina Beijing Institute for Stem Cell and Regenerative Medicine BeijingChina

The oral cavity is a complex physiological community encompassing a wide range of *** of oral microbiota can lead to various oral infectious diseases,such as periodontitis and tooth decay,and even affect systemic health,including brain aging and neurodegenerative *** studies have highlighted how oral microbes might be involved in brain aging and neurodegeneration,indicating potential avenues for intervention *** this review,we summarize clinical evidence demonstrating a link between oral microbes/oral infectious diseases and brain aging/neurodegenerative diseases,and dissect potential mechanisms by which oral microbes contribute to brain aging and *** also highlight advances in therapeutic development grounded in the realm of oral microbes,with the goal of advancing brain health and promoting healthy aging.

关键词： Alzheimer's disease brain aging multiple sclerosis neurodegeneration neurodegenerative diseases oral microbiota Parkinson's disease periodontitis bacteria Porphyromonas gingivalis

维普期刊数据库