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Two-color emissive AIEgens with anti-Kasha property for dual-organelle imaging and phototherapy

Science China Chemistry 2024年第5期67卷 1740-1752页

作者： Pu Chen Guogang Shan Qingli Nie Yuting Yan Pengfei Zhang Zujin Zhao Hai-Tao Feng Ben Zhong Tang AIE Research Center Shaanxi Key Laboratory of PhytochemistryCollege of Chemistry and Chemical EngineeringBaoji University of Arts and SciencesBaoji 721013China Institute of Functional Material Chemistry and National&Local United Engineering lab for Power Battery Faculty of ChemistryNortheast Normal UniversityChangchun 130024China College of Chemistry&Materials Science Northwest UniversityXi’an 710127China Guangdong key laboratory of Nanomedicine CAS-HK Joint Lab of BiomaterialsCAS Key Laboratory of Biomedical Imaging Science and SystemShenzhen Engineering Laboratory of Nanomedicine and NanoformulationsCAS Key Lab for Health InformaticsShenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China State key laboratory of Luminescent Materials and Devices Key Laboratory of Luminescence from Molecular Aggregates of Guangdong ProvinceSouth China University of TechnologyGuangzhou 510640China School of Science and Engineering Shenzhen Institute of Aggregate Science and TechnologyThe Chinese University of Hong KongShenzhen(CUHK-Shenzhen)Shenzhen 518172China

Despite the rapid development of probes for targeting single organelle, the construction of robust dual-organelle targeting probes with multicolor emission was rarely reported. Herein, two dual-emissive aggregation-induced emission luminogens(AIEgens)with donor-π-acceptor structures were designed and synthesized, namely QT-1 and QF-2. Both two AIEgens exhibited excitation wavelength-dependence defying the Kasha's rule, and could stain lipid droplets(LDs) and mitochondria in blue and red fluorescence, respectively. Moreover, thanks to the near-infrared emission and abundant reactive oxygen species(ROS) generation efficiency of QT-1, it was chosen as a photodynamic therapy agent to selectively kill cancer cells from normal cells. Upon light irradiation, an obvious decrease of mitochondrial membrane potential(MMP) and serious change of mitochondrial shape in cells were observed, which corresponded to the efficient inhibition of tumor growth in vivo. This work afforded a promising strategy for the construction of multicolor emission by tuning anti-Kasha behaviors and expanding their application in dualorganelle targeting-based phototheranostics.

关键词： aggregation-induced emission anti-Kasha luminescence fluorescent probe bioimaging photodynamic therapy

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HEN:a novel hybrid explainable neural network based framework for robust network intrusion detection

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Science China(Information Sciences) 2024年第7期67卷 72-90页

作者： Wei WEI Sijin CHEN Cen CHEN Heshi WANG Jing LIU Zhongyao CHENG Xiaofeng ZOU School of Computer Science and Engineering Xi'an University of TechnologyShaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and technology Wuhan University of Science and Technology School of Future technology South China University of Technology shenzhen Research Institute of Hunan University School of Computer Science Hunan University of Technology and Business Institute for Infocomm Research (I2R) Agency for Science Technology and Reseach (A*STAR)

With the rapid development of network technology and the automation process for 5G, cyberattacks have become increasingly complex and threatening. In response to these threats, researchers have developed various network intrusion detection systems(NIDS) to monitor network traffic. However, the incessant emergence of new attack techniques and the lack of system interpretability pose challenges to improving the detection performance of NIDS. To address these issues, this paper proposes a hybrid explainable neural network-based framework that improves both the interpretability of our model and the performance in detecting new attacks through the innovative application of the explainable artificial intelligence(XAI)method. We effectively introduce the Shapley additive explanations(SHAP) method to explain a light gradient boosting machine(Light GBM) model. Additionally, we propose an autoencoder long-term short-term memory(AE-LSTM) network to reconstruct SHAP values previously generated. Furthermore, we define a threshold based on reconstruction errors observed during the training phase. Any network flow that surpasses the specified threshold is classified as an attack flow. This approach enhances the framework's ability to accurately identify attacks. We achieve an accuracy of 92.65%, a recall of 95.26%, a precision of 92.57%,and an F1-score of 93.90% on the dataset NSL-KDD. Experimental results demonstrate that our approach generates detection performance on par with state-of-the-art methods.

关键词： explainable artificial intelligence light gradient boosting machine machine learning network intrusion detection Shapley additive explanation hybrid explainable neural network (HEN)

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Research progress on formation and suppression of crack defects in oxide eutectic ceramics by laser additive manufacturing

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CAILIAO GONGCHENG-JOURNAL OF MATERIALS ENGINEERING 2024年第5期52卷 34-45页

作者： Yao, Jiatong Su, Haijun Jiang, Hao Chen, Qian Shen, Zhonglin Yu, Minghui Zhang, Zhuo Guo, Min Northwestern Polytech Univ State Key Lab Solidificat Proc Xian 710072 Peoples R China Northwestern Polytech Univ Shenzhen Res Inst Shenzhen 518057 Guangdong Peoples R China

Oxide eutectic ceramics have excellent high specific strength , high temperature resistance , corrosion resistance , oxidation and creep resistance , and so on , which are considered to be the promising materials to be used in ultra -high temperature oxidation , corrosion and other extreme environments. It shows great application prospects in the new generation of high thrust -to -weight ratio aero-engine for hightemperature hot -end structural components. Laser additive manufacturing technology has become one of the most promising cutting -edge technologies for the preparation of high-performance complex structural components in recent years. Crack defect is easy to occur in the process of laser rapid solidification of ceramics , which seriously affects the quality and performance of the oxide eutectic ceramics components. So , it has become a key factor restricting the engineering application. Two typical laser additive manufacturing technologies for ceramics including laser engineered net shaping and laser power bed fusion were briefly summarized. The crack morphology characteristics of different shaped components formed by the above two technologies were analyzed and compared. The formation mechanism of cracks in laser additive manufacturing of oxide ceramics was explored from the perspectives of microstructure characteristics ,stress state. Further ,a systematic summary was presented focusing on the improvement of microstructures and the reduction of thermal stress to inhibit crack formation through optimization of process parameters ,compositional design ,and outfield assistance. Finally ,it is pointed out that the future development trends and breakthrough directions of oxide eutectic ceramics by laser additive manufacturing in terms of and factors ,and research.

关键词： oxide eutectic ceramic laser additive manufacturing crack defect formation mechanism crack suppression DIRECTED ENERGY DEPOSITION MECHANICAL-PROPERTIES PROCESS OPTIMIZATION ALUMINA CERAMICS MICROSTRUCTURE COMPOSITE ZIRCONIA COMPONENTS STRENGTH

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Adaptive joint configuration optimization for collaborative inference in edge-cloud systems

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Science China(Information Sciences) 2024年第4期67卷 335-336页

作者： Zheming YANG Wen JI Zhi WANG Institute of computing technology Chinese Academy of Sciences University of Chinese Academy of Sciences Peng Cheng laboratory Tsinghua shenzhen International Graduate School Tsinghua University

With the rapid development of technologies such as deep learning and the Internet of Things(IoT), the deployment and application of various IoT devices are becoming increasingly widespread, resulting in the inference tasks generated increasing dramatically [1]. Many deep neural network(DNN) models are deployed on servers for performing inference tasks.

关键词： Inference engines

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Realize ultralow-energy-consumption photo-synaptic device based on a single(Al,Ga)N nanowire for neuromorphic computing

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Nano Research 2024年第3期17卷 1933-1941页

作者： Xiushuo Gu Min Zhou Yukun Zhao Qianyi Zhang Jianya Zhang Yonglin Huang Shulong Lu College of Electronic and Optical Engineering&College of Flexible Electronics(Future technology) Nanjing University of Posts and TelecommunicationsNanjing 210023China key lab of Nanodevices and applications Suzhou Institute of Nano-Tech and Nano-Bionics(SINANO)Chinese Academy of Sciences(CAS)Suzhou 215123China School of Nano-Tech and Nano-Bionics University of Science and Technology of ChinaHefei 230026China Jiangsu key laboratory of Micro and Nano Heat Fluid Flow technology and Energy application School of Physical Science and TechnologySuzhou University of Science and TechnologySuzhou 215009China

The rapid development of artificial intelligence poses an urgent need for low-energy-consumption and small-sized artificial photonic synapses.Here,it is pretty novel to demonstrate a light-stimulated synaptic device based on a single(Al,Ga)N nanowire successfully.Thanks to the presence of vacancy defects in the single nanowire,the artificial synaptic device can simulate multiple functions of biological synapses under stimulation of both 310 and 365 nm light photons,including paired-pulse facilitation,spike timing dependent plasticity,and memory learning capabilities.The energy consumption of artificial synaptic device can be reduced as little as 5.58×10^(-13) J,which is close to that of the biological synapse in human brain.Furthermore,the synaptic device is demonstrated to have the high stability for both long-time stimulation and long-time storage.Based on the experimental conductance of long-term potentiation and long-term depression,the simulated three-layer neural network can achieve a high recognition rate of 92%after only 10 training epochs.With a brain-like behavior,the single-nanowire-based synaptic devices can promote the development of visual neuromorphic computing technology and artificial intelligence systems requiring ultralow energy consumption.

关键词： single(Al Ga)N nanowire light-stimulated synaptic device low-energy-consumption neural network

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N-doped hollow carbon nanospheres embedded in N-doped graphene forming a three-dimensional interconnected layered porous support loaded with palladium nanoparticles as an efficient electrocatalyst for formic acid oxidation

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新型炭材料(中英文) 2024年

作者： FANG Yue YANG Fu-kai QU Wei-li DENG Chao WANG Zhen-bo key laboratory of Photochemical Biomaterials and Energy Storage Materials shenzhen key laboratory of Special Functional Materials Shenzhen Engineering Laboratory for Advance Technology of Ceramics Guangdong Research Center for Interfacial Engineering of Functional Materials College of Materials Science and Engineering Shenzhen University MIIT key laboratory of Critical Materials technology for New Energy Conversion and Storage School of Chemistry and Chemical Engineering State Key Lab of Urban Water Resource and Environment Harbin Institute of Technology College of Chemistry and Chemical Engineering Harbin Normal University No.1 Normal University South Road

The efficient electrocatalysts with low cost, high activity and good durability plays a crucial role in the application of direct formic acid fuel cells. Herein, Pd nanoparticles supported on N-doped hollow carbon nanospheres (NHCN) embedded in Ndoped graphene (NG) with three-dimensional (3D) layered porous configuration by a simple and economical method were investigated as direct formic acid fuel cell catalysts. Owing to the unique 3D interconnected layered porous configuration doped with nitrogen atoms, Pd/NHCN@NG catalyst with smaller Pd nanoparticle size shows large catalytic active surface area, superior electrocatalyticactivity,highsteady-statecurrentdensity,strongabilitytoresistCOpoisoning,farsurpassingthoseofconventionalPd/C,Pd/NG,andPd/***/GOratio,itisfoundthatwhenthe HCN/GO mass ratio is 1∶1, Pd/NHCN@NG catalyst has the most outstanding performance in catalytic oxidation of formic acid,with an activity 4.21 times that of Pd/C. This work has developed a superior carbon-based support material for electrocatalysts, which brings broad application prospects for the development of fuel cells.

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Cogeneration of Innovative Audio-visual Content: A New Challenge for computing Art

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Machine Intelligence Research 2024年第1期21卷 4-28页

作者： Mengting Liu Ying Zhou Yuwei Wu Feng Gao Advanced Institute of Information technology Peking UniversityHangzhou311215China School of Electronic and Computer Engineering Shenzhen Graduate SchoolPeking UniversityShenzhen 518000China Peng Cheng laboratory Shenzhen 518000China School of Arts Peking UniversityBeijing 100871China

In recent years,computing art has developed rapidly with the in-depth cross study of artificial intelligence generated con-tent(AIGC)and the main features of artworks.Audio-visual content generation has gradually been applied to various practical tasks,including video or game score,assisting artists in creation,art education and other aspects,which demonstrates a broad application pro-spect.In this paper,we introduce innovative achievements in audio-visual content generation from the perspective of visual art genera-tion and auditory art generation based on artificial intelligence(Al).We outline the development tendency of image and music datasets,visual and auditory content modelling,and related automatic generation systems.The objective and subjective evaluation of generated samples plays an important role in the measurement of algorithm performance.We provide a cogeneration mechanism of audio-visual content in multimodal tasks from image to music and display the construction of specific stylized datasets.There are still many new op-portunities and challenges in the field of audio-visual synesthesia generation,and we provide a comprehensive discussion on them.

关键词： Artificial intelligence(AI)art audio-visual artificial intelligence generated content(AIGC) multimodal artistic evalu-ation

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Semantic knowledge graph as a companion for catalyst recommendation

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National Science Open 2024年第2期3卷 46-55页

作者： Zhiying Zhang Shengming Ma Shisheng Zheng Zhiwei Nie Bingxu Wang Kai Lei Shunning Li Feng Pan School of Advanced Materials Peking University Shenzhen Graduate SchoolShenzhen 518055China shenzhen key lab for Information Centric Networking and Blockchain technology(ICNlab) Peking University Shenzhen Graduate SchoolShenzhen 518055China

Our ability to perceive the correlation of different substances in the world is one of the key aspects of human intelligence.The passing of this faculty to artificial intelligence(AI)represents arguably one of the long-standing challenges in the application of AI to scientific problems.To meet this challenge in the burgeoning field of AI for chemistry,we may adopt the paradigm of knowledge graph.Herein,focusing on catalytic chemical reactions,we have developed a semantic knowledge graph framework based on both structured and unstructured data,the latter of which are extracted from the text of 220,000articles on catalysts for organic molecules.The framework captures the latent knowledge of reactant-catalyst-product relationships and can therefore provide accurate recommendation on potential catalysts for targeted reaction,which especially facilitates the research involving large molecules.This study presents a viable pathway towards the implementation of literature-based data management in a catalyst recommendation platform.

关键词： knowledge graph text mining catalysts organic molecules natural language processing

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Interactions of phenol and benzaldehyde in electrocatalytic upgrading process

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Chinese Chemical Letters 2024年第2期35卷 464-468页

作者： Lan Liang Chuanbin Wang Xukai Lu Yunan Sun Beibei Yan Ning Li Guanyi Chen Li'an Hou School of Environmental Science and Engineering Tianjin Key Lab of Biomass/Wastes UtilizationTianjin UniversityTianjin 300350China School of Mechanical Engineering Tianjin University of CommerceTianjin 300134China Georgia Tech shenzhen Institute Tianjin UniversityShenzhen 518071China High Tech Inst Beijing Beijing 100085China

Biomass pyrolysis oil can be improved effectively by electrocatalytic hydrogenation(ECH).However,the unclear interactions among different components lead to low bio-oil upgrading efficiency in the conversion process.Herein,benzaldehyde and phenol,as common compounds in bio-oil,were chosen as model compounds.The interactions between the two components were explored in the ECH process by combining experiments and theoretical calculations.Results showed that phenol could accelerate the conversion of benzaldehyde in the ECH.The selectivity of benzyl alcohol was increased from 60.9%of unadded phenol to 99.1%with 30 mmol/L phenol concentration at 5 h.Benzaldehyde inhibited the ECH of phenol.In the presence of benzaldehyde,the conversion rate of phenol was below 10.0%with no cyclohexanone and cyclohexanol formation at 5 h.The density functional theory(DFT)calculations revealed that the phenol could promote the adsorption of benzaldehyde and facilitate the targeted conversion of benzaldehyde on the active site by lowering the reaction energy barrier.The research on the interaction between phenol and benzaldehyde in the ECH provides a theoretical basis for the application of ECH in practical bio-oil upgrading.

关键词： Interaction Bio-oil Electrocatalytic hydrogenation Phenol Benzaldehyde

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D-type neuropeptide decorated AIEgen/RENP hybrid nanoprobes with light-driven ROS generation ability for NIR-Ⅱ fluorescence imaging-guided through-skull photodynamic therapy of gliomas

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Aggregate 2024年第1期5卷 185-195页

作者： Xuelu He Yuan Luo Yanying Li Yuanbo Pan Ryan T.K.Kwok Lulu He Xiaolin Duan Pengfei Zhang Aiguo Wu Ben Zhong Tang Juan Li Ningbo key laboratory of Biomedical Imaging Probe Materials and technology Zhejiang International Cooperation Base of Biomedical Materials Technology and ApplicationChinese Academy of Sciences(CAS)Key Laboratory of Magnetic Materials and DevicesNingbo Cixi Institute of Biomedical EngineeringZhejiang Engineering Research Center for Biomedical MaterialsNingbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingboP.R.China Guangdong key laboratory of Nanomedicine Shenzhen Engineering Laboratory of Nanomedicine and NanoformulationsCAS-HK Joint Lab for BiomaterialsResearch Laboratory for Biomedical Optics and Molecular ImagingShenzhen Key Laboratory for Molecular ImagingCAS Key Lab for Health InformaticsShenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenP.R.China Department of Chemistry Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and Reconstruction and Institute for Advanced StudyThe Hong Kong University of Science and Technology(HKUST)KowloonP.R.China School of Science and Engineering Shenzhen Institute of Aggregate Science and TechnologyThe Chinese University of Hong KongShenzhenP.R.China

Glioma is one of the most common malignant tumors of the central nervous system,leading high mortality rates in human.Aggregation-induced emission(AIE)photosensitizers-based photodynamic therapy(PDT)has emerged as a promising therapeutic strategy for least-invasive treatment of glioma,which involves local irradiation of the tumor using an external near-infrared(NIR)laser.Unfortunately,most AIE photosensitizers suffered from poorly penetration of the visible light excitation,bad spatiotemporal resolution in deep tissues and low efficient blood-brain barrier(BBB)crossing ability,which greatly limited the clinical practice of AIE photosensitizers for especially deep-seated brain tumor treatment.In this work,we developed a multifunctional NIR-driven theranostic agent through hybrid of AIE photosensitizers TIND with rare-earth doping nanoparticles(RENPs)NaGdF4:Nd/Yb/Tm with up/down dual-mode conversion luminescence.The theranostic agent was further decorated with D-type neuropeptide DNPY for crossing BBB and targeting glioma.Under the 808-nm light irradiation,the down-conversion NIR-II luminescence could indicate the position glioma and the upconversion NIR-I luminescence could trigger the AIE photosensitizers producing reactive oxygen species to inhibit orthotopic glioma tumor growth in situ.These results demonstrate that the integration of Dtype neuropeptide,AIE photosensitizers and RENPs could be promising candidates for in vivo NIR-II fluorescence image-guided through-skull PDT treatments of brain tumors.

关键词： aggregation-induced emission D-type neuropeptide glioma photodynamic therapy rare-earth doping nanoparticles

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