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Advanced electrospinning nanomaterials:From spinning fabrication techniques to electrochemical applications

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Nano Research 2024年第8期17卷 7077-7116页

作者： Jie Zhang Miao yu Shuhui tao College of Materials Science and Engineering Sichuan UniversityChengdu 610065China School of Mechanical Engineering Sichuan UniversityChengdu 610065China National University of Singapore(Chongqing)Research Institute Chongqing 401123China

Fuel cells,rechargeable metal–air batteries,metal-ion batteries,and supercapacitors have been regarded as prospective energy sources technologies to replace the fossil ***,electrochemical sensors also have been successfully developed as promising platforms for detecting pollutants,drug analysis,and healthcare *** is a scalable and suitable technique to design and fabricate diverse nanofibers and their nanocomposites for key components in electrochemical ***,a comprehensive review of electrospinning,from the technological innovations to the novel electrospun composite nanofibers,is *** importantly,the recent advances on the diverse components of electrochemical devices are highlighted,followed with the detailed discussion about the corresponding enhancement mechanism based on the electrospun ***,the currently faced challenges on the fabrication of electrospun nanomaterials are raised and a brief perspective for the future development of electrospun nanomaterials-based electrochemical applications is prospected.

关键词： electrospinning carbon nanofibers metal-organic frameworks electrode materials electrochemical applications

A review on plasma-based CO_(2) utilization:process considerations in the development of sustainable chemical production

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Plasma Science and Technology 2024年第9期26卷 1-16页

作者： Sirui LI Giulia De FELICE Simona EICHKORN tao shao Fausto GALLUCCI Department of Chemical Engineering and Chemistry Eindhoven University of TechnologyEindhoven 5612 AZthe Netherlands Institute of Electrical Engineering Chinese Academy of SciencesBeijing 100190People’s Republic of China

Plasma-based processes,particularly in carbon capture and utilization,hold great potential for addressing environmental challenges and advancing a circular carbon *** significant progress has been made in understanding plasma-induced reactions,plasma-catalyst interactions,and reactor development to enhance energy efficiency and conversion,there remains a notable gap in research concerning overall process *** review emphasizes the critical need for considerations at the process level,including integration and intensification,to facilitate the industrialization of plasma technology for chemical *** centered on the development of plasma-based processes are made with a primary focus on CO_(2) conversion,offering insights to guide future work for the transition of the technology from laboratory scale to industrial *** of current research gaps,especially in upscaling and integrating plasma reactors with other process units,is the key to addressing critical *** review further delves into relevant research in process evaluation and assessment,providing methodological insights and highlighting key factors for comprehensive economic and sustainability ***,recent advancements in novel plasma systems are reviewed,presenting unique advantages and innovative concepts that could reshape the future of process *** review provides essential information for navigating the path forward,ensuring a comprehensive understanding of challenges and opportunities in the development of plasma-based CCU process.

关键词： non-thermal plasma carbon capture and utilization process integration process intensification techno-economic analysis life cycle analysis

A Deep Learning Based Broadcast Approach for Image Semantic Communication over Fading Channels

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China Communications 2024年第7期21卷 78-94页

作者： Ma Kangning Shi yuxuan shao Shuo tao Meixia The Department of Electronic Engineering Shanghai Jiao Tong UniversityShanghai 200240China The School of Cyber and Engineering Shanghai Jiao Tong UniversityShanghai 200240China

We consider an image semantic communication system in a time-varying fading Gaussian MIMO channel,with a finite number of channel states.A deep learning-aided broadcast approach scheme is proposed to benefit the adaptive semantic transmission in terms of different channel *** combine the classic broadcast approach with the image transformer to implement this adaptive joint source and channel coding(JSCC)***,we utilize the neural network(NN)to jointly optimize the hierarchical image compression and superposition code mapping within this *** learned transformers and codebooks allow recovering of the image with an adaptive quality and low error rate at the receiver side,in each channel *** simulation results exhibit our proposed scheme can dynamically adapt the coding to the current channel state and outperform some existing intelligent schemes with the fixed coding block.

关键词： broadcast approach deep learning fading channels semantic communication

Hybrid model for BOF oxygen blowing time prediction based on oxygen balance mechanism and deep neural network

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International Journal of Minerals,Metallurgy and Materials 2024年第1期31卷 106-117页

作者： Xin shao Qing Liu Zicheng Xin Jiangshan Zhang tao Zhou shaoshuai Li State Key Laboratory of Advanced Metallurgy University of Science and Technology BeijingBeijing 100083China Engineering Research Center of MES Technology for Iron&Steel Production Ministry of EducationBeijing 100083China Laiwu Iron and Steel Group Yinshan Section Steel Co. Ltd.Jinan 271104China

The amount of oxygen blown into the converter is one of the key parameters for the control of the converter blowing process,which directly affects the tap-to-tap time of converter. In this study, a hybrid model based on oxygen balance mechanism (OBM) and deep neural network (DNN) was established for predicting oxygen blowing time in converter. A three-step method was utilized in the hybrid model. First, the oxygen consumption volume was predicted by the OBM model and DNN model, respectively. Second, a more accurate oxygen consumption volume was obtained by integrating the OBM model and DNN model. Finally, the converter oxygen blowing time was calculated according to the oxygen consumption volume and the oxygen supply intensity of each heat. The proposed hybrid model was verified using the actual data collected from an integrated steel plant in China, and compared with multiple linear regression model, OBM model, and neural network model including extreme learning machine, back propagation neural network, and DNN. The test results indicate that the hybrid model with a network structure of 3 hidden layer layers, 32-16-8 neurons per hidden layer, and 0.1 learning rate has the best prediction accuracy and stronger generalization ability compared with other models. The predicted hit ratio of oxygen consumption volume within the error±300 m^(3)is 96.67%;determination coefficient (R^(2)) and root mean square error (RMSE) are0.6984 and 150.03 m^(3), respectively. The oxygen blow time prediction hit ratio within the error±0.6 min is 89.50%;R2and RMSE are0.9486 and 0.3592 min, respectively. As a result, the proposed model can effectively predict the oxygen consumption volume and oxygen blowing time in the converter.

关键词： basic oxygen furnace oxygen consumption oxygen blowing time oxygen balance mechanism deep neural network hybrid model

Hybrid pedestrian positioning system using wearable inertial sensors and ultrasonic ranging

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Defence Technology（防务技术） 2024年第3期33卷 327-338页

作者： Lin Qi yu Liu Chuanshun Gao tao Feng yue yu School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China Department of Land Surveying and Geo-Informatics The Hong Kong Polytechnic UniversityHong Kong 999077China

Pedestrian positioning system(PPS)using wearable inertial sensors has wide applications towards various emerging fields such as smart healthcare,emergency rescue,soldier positioning,*** performance of traditional PPS is limited by the cumulative error of inertial sensors,complex motion modes of pedestrians,and the low robustness of the multi-sensor collaboration *** paper presents a hybrid pedestrian positioning system using the combination of wearable inertial sensors and ultrasonic ranging(H-PPS).A robust two nodes integration structure is developed to adaptively combine the motion data acquired from the single waist-mounted and foot-mounted node,and enhanced by a novel ellipsoid constraint *** addition,a deep-learning-based walking speed estimator is proposed by considering all the motion features provided by different nodes,which effectively reduces the cumulative error originating from inertial ***,a comprehensive data and model dual-driven model is presented to effectively combine the motion data provided by different sensor nodes and walking speed estimator,and multi-level constraints are extracted to further improve the performance of the overall *** results indicate that the proposed H-PPS significantly improves the performance of the single PPS and outperforms existing algorithms in accuracy index under complex indoor scenarios.

关键词： Pedestrian positioning system Wearable inertial sensors Ultrasonic ranging Deep-learning Data and model dual-driven

pLoc_Deep-mHum: Predict Subcellular Localization of Human Proteins by Deep Learning

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Natural Science 2020年第7期12卷 526-551页

作者： yu-tao shao Xin-Xin Liu Zhe Lu Kuo-Chen Chou Computer Science Jingdezhen Ceramic Institute Jingdezhen China Gordon Life Science Institute Boston MA 02478 USA

Recently, the life of human beings around the entire world has been endangering by the spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1. To develop effective drugs against Coronavirus, knowledge of protein subcellular localization is indispensable. In 2019, a predictor called “pLoc_bal-mHum” was developed for identifying the subcellular localization of human proteins. Its predicted results are significantly better than its counterparts, particularly for those proteins that may simultaneously occur or move between two or more subcellular location sites. However, more efforts are definitely needed to further improve its power since pLoc_bal-mHum was still not trained by a “deep learning”, a very powerful technique developed recently. The present study was devoted to incorporate the “deep-learning” technique and develop a new predictor called “pLoc_Deep-mHum”. The global absolute true rate achieved by the new predictor is over 81% and its local accuracy is over 90%. Both are overwhelmingly superior to its counterparts. Moreover, a user-friendly web-server for the new predictor has been well established at http://***/pLoc_Deep-mHum/, which will become a very useful tool for fighting pandemic coronavirus and save the mankind of this planet.

关键词： Coronavirus Multi-Label System Human Proteins Deep Learning Five-Steps Rule PseAAC

Primordial black holes and curvature perturbations from false vacuum islands

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Science China(Physics,Mechanics & Astronomy) 2024年第9期67卷 58-72页

作者： Rong-Gen Cai yu-Shi Hao shao-Jiang Wang School of Physical Science and Technology Ningbo UniversityNingbo 315211China CAS Key Laboratory of Theoretical Physics Institute of Theoretical PhysicsChinese Academy of SciencesBeijing 100190China School of Physical Sciences University of Chinese Academy of SciencesBeijing 100049China Asia Pacific Center for Theoretical Physics Pohang 37673Republic of Korea

Recently,much attention has been focused on the false vacuum islands that are flooded by an expanding ocean of true-vacuum bubbles slightly later than most of the other parts of the *** delayed decay regions will accumulate locally larger vacuum energy density by staying in the false vacuum longer than those already transited into the true vacuum.A false vacuum island with thus acquired density contrast of a super-horizon size will evolve locally from radiation dominance to vacuum dominance,creating a local baby Universe that can be regarded effectively as a local closed *** such density contrasts of super-horizon sizes can ever grow large enough to exceed the threshold of gravitational collapse,primordial black holes will form similar to those collapsing curvature perturbations on super-horizon scales induced by small-scale enhancements during *** not,such density contrasts can still induce curvature perturbations potentially observable *** this paper,we revisit and elaborate on the generations of primordial black holes and curvature perturbations from delayed-decayed false vacuum islands during asynchronous first-order phase transitions with fitting formulas convenient for future model-independent studies.

关键词： first-order phase transitions primordial black holes curvature perturbations

pLoc_Deep-mPlant: Predict Subcellular Localization of Plant Proteins by Deep Learning

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Natural Science 2020年第5期12卷 237-247页

作者： yu-tao shao Xin-Xin Liu Zhe Lu Kuo-Chen Chou Computer Science Jingdezhen Ceramic Institute Jingdezhen China Gordon Life Science Institute Boston MA USA

Current coronavirus pandemic has endangered mankind life. The reported cases are increasing exponentially. Information of plant protein subcellular localization can provide useful clues to develop antiviral drugs. To cope with such a catastrophe, a CNN based plant protein subcellular localization predictor called “pLoc_Deep-mPlant” was developed. The predictor is particularly useful in dealing with the multi-sites systems in which some proteins may simultaneously occur in two or more different organelles that are the current focus of pharmaceutical industry. The global absolute true rate achieved by the new predictor is over 95% and its local accuracy is about 90%?-?100%. Both have substantially exceeded the?other existing state-of-the-art predictors. To maximize the convenience for most?experimental scientists, a user-friendly web-server for the new predictor has been established?at?http://***/pLoc_Deep-mPlant/, by which the majority of experimental?scientists can easily obtain their desired data without the need to go through the?mathematical details.

关键词： Pandemic Coronavirus Multi-Label System Plant Proteins Learning at Deeper Level Five-Steps Rule PseAAC

Growth of Ag/g-C_(3)N_(4) nanocomposites on nickel foam to enhance photocatalytic degradation of formaldehyde under visible light

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Journal of Environmental Sciences 2024年第3期137卷 432-442页

作者： Ze Wang Zhi Huang Jiang yu Xiao shao Weidong Peng Jie yu Yinying Jiang Department of Environmental Science and Engineering College of Architecture and EnvironmentSichuan UniversityChengdu 610065China Institute of New Energy and Low Carbon Technology Sichuan UniversityChengdu 610065China School of Agriculture and Environment University of Western AustraliaPerth 6907Western AustraliaAustralia Yibin Institute of Industrial Technology Sichuan UniversityYibin 644000China

Formaldehyde is a pollutant that significantly affects the indoor air ***,conventional remediation approaches can be challenging to deal with low-concentration formaldehyde in an indoor *** this study,Photocatalysts of Ag/graphitic carbon nitride(g-C_(3)N_(4))/Ni with 3D reticulated coral structure were prepared by thermal polymerization and liquid phase photo-deposition,using nickel foam(NF)as the *** demonstrated that when the Ag concentration was 3%,and the relative humidity was 60%,the Ni/Ag/g-C_(3)N_(4)showed the maximum degradation rate of formaldehyde at 90.19%under visible light irradiation,and the formaldehyde concentration after degradation was lower than the Hygienic standard stated by the Chinese *** porous structure of Ni/Ag/g-C_(3)N_(4)and the formation of Schottky junctions promoted the Adsorption efficiency and degradation of formaldehyde,while the nickel foam carrier effectively promoted the desorption of degradation ***,the degradation rate was only reduced by3.4%after 16 recycles,the three-dimensional porous structure extended the lifetime of the *** study provides a new strategy for the degradation of indoor formaldehyde at low concentrations.

关键词： Graphitic carbon nitride(g-C_(3)N_(4)) Nickel foam(NF) Ag Formaldehyde Photocatalysis