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Prediction model on hydrolysis kinetics of phthalate monoester:A density functional theory study

Journal of Environmental Sciences 2024年第1期135卷 51-58页

作者： Tong Xu Jingwen Chen Deming Xia Weihao Tang Jiansheng Cui Chun Liu Shuangjiang Li College of Environmental Science and Engineering Hebei University of Science and TechnologyShijiazhuang 050000China Key Laboratory of Industrial Ecology and Environmental Engineering(Ministry of Education) School of Environmental Science and TechnologyDalian University of TechnologyDalian 116024China National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China Guangdong Key Laboratory of Integrated Agro-environmental Pollution Control and ManagementInstitute of Eco-environmental and Soil SciencesGuangdong Academy of SciencesGuangzhou 510650China

As primary degradation products of phthalate esters,phthalate monoesters(MPEs)have been widely detected in various aquatic environments and drawn growing toxicological *** kinetics that is of importance for assessing environmental persistence of chemicals remain elusive for ***,kinetics of base-catalyzed and neutral hydrolysis for 18 MPEs with different leaving groups was investigated by density functional theory *** indicate that MPEs with leaving groups having p Kaof<10 prefer dissociative transition *** are more persistent than their parents,and their hydrolysis half-lives were calculated to vary from 3.4 min to 79.2 years(p H=7–9).A quantitative structure-activity relationship model was developed for predicting the hydrolysis kinetics *** was found that p Kaof the leaving groups and electronegativity of the MPEs are key factors determining the hydrolysis *** work may lay a theoretical foundation for better understanding the chemical process that governs MPE persistence in aquatic environments.

关键词： Phthalate monoesters Phthalate esters Hydrolysis Density functional theory Quantitative structure-activity relationship

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Efficient photocatalytic NADH regeneration with Rh-loaded Z-scheme mediator-free system

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Chinese Chemical Letters 2024年第2期35卷 496-500页

作者： Yi Zhou Yiling He Ming Gao Ningkai Ding Juying Lei Yanbo Zhou State Environmental Protection Key Laboratory of Environmental Risk Assessment and Control on Chemical Process East China University of Science and TechnologyShanghai 200237China National Engineering Research Center of Industrial Wastewater Detoxication and Resource Recovery East China University of Science and TechnologyShanghai 200237China Shanghai Engineering Research Center for Multi-media Environmental Catalysis and Resource Utilization East China University of Science and TechnologyShanghai 200237China Shanghai Institute of Pollution Control and Ecological Security Shanghai 200092China

Nicotinamide adenine dinucleotide(NADH)regeneration is necessary for the sustainable application of enzymatic *** Rh-based complex[Cp^(*)Rh(bpy)(H)]+has been widely used as an important mediator in NADH regeneration systems,but it is limited by complexity and high ***,a Z-scheme was constructed by loading Rh onto carbon nitride nanosheets/carbon nitride quantum dots(CN-CNQD).The resultant catalyst achieved a high yield of NADH in a mediator-free(M-free)system of 0.283 mmol L^(−1) g^(−1) min^(−1),which is 5.29 times that of pure *** enzyme introduction experiments confirmed that the enzyme active product 1,4-NADH could reach 34.21%selectivity in the M-free *** research revealed that the heterojunction between CNs and CNQDs improved the NADH regeneration activity in the traditional M-involved system,while Rh loading was proved to optimize the yield and selectivity of 1,4-NADH in M-free *** immobilized Rh shows more competitiveness than[Cp^(*)Rh(bpy)(H)]^(+).This study contributes to the construction of an M-free system for further application in greener,lower-cost enzymatic processes.

关键词： Photocatalysis NADH regeneration Mediator-free Carbon nitride Z-scheme

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SOPA-GA-CNN: Synchronous optimisation of parameters and architectures by genetic algorithms with convolutional neural network blocks for securing industrial Internet-of-Things

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IET Cyber-Systems and Robotics 2023年第1期5卷 51-66页

作者： Jia-Cheng Huang Guo-Qiang Zeng Guang-Gang Geng Jian Weng Kang-Di Lu College of Cyber Security and the National Joint Engineering Research Center of Network Security Detection and Protection Technology Jinan UniversityGuangzhouChina National-Local Joint Engineering Research Center of Digitalize Electrical Design Technology Wenzhou UniversityWenzhouChina National Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang UniversityHangzhouChina

In recent years,deep learning has been applied to a variety of scenarios in industrial Internet of Things(IIoT),including enhancing the security of ***,the existing deep learning methods utilised in IIoT security are manually designed by heavily relying on the experience of the *** authors have made the first contribution concerning the joint optimisation of neural architecture search and hyper-parameters optimisation for securing IIoT.A novel automated deep learning method called synchronous optimisation of parameters and architectures by GA with CNN blocks(SOPA-GA-CNN)is proposed to synchronously optimise the hyperparameters and block-based architectures in convolutional neural networks(CNNs)by genetic algorithms(GA)for the intrusion detection issue of *** efficient hybrid encoding strategy and the corresponding GA-based evolutionary operations are designed to characterise and evolve both the hyperparameters,including batch size,learning rate,weight optimiser and weight regularisation,and the architectures,such as the block-based network topology and the parameters of each CNN *** experimental results on five intrusion detection datasets in IIoT,including secure water treatment,water distribution,Gas Pipeline,Botnet in Internet of Things and Power System Attack Dataset,have demonstrated the superiority of the proposed SOPA-GA-CNN to the state-of-the-art manually designed models and neuron-evolutionary methods in terms of accuracy,precision,recall,F1-score,and the number of parameters of the deep learning models.

关键词： cyber-systems deep learning deep neural network Internet of things swarm intelligence

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Peroxidase-like active Cu-ZIF-8 with rich copper(I)-nitrogen sites for excellent antibacterial performances toward drug-resistant bacteria

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Nano Research 2024年第8期17卷 7427-7435页

作者： Xiaoze Wang Hui Wang Jingkun Zhang Ning Han Wenjun Ma Donghai Zhang Mingshui Yao Xianliang Wang Yunfa Chen State Key Laboratory of Mesoscience and Engineering Institute of Process EngineeringChinese Academy of SciencesBeijing 100190China University of the Chinese Academy of Sciences Beijing 100049China Laboratory of Theoretical and Computational Nanoscience CAS Key Laboratory of Nanosystem and Hierarchial FabricationCAS Center for Excellence in NanoscienceNational Center for Nanoscience and TechnologyBeijing 100190China China CDC Key Laboratory of Environment and Population Health National Institute of Environmental HealthChinese Center for Disease Control and PreventionBeijing 100021China

Bacterial pathogens pose a serious threat to human health,and there is an urgent need to develop highly effective antibacterial materials to eliminate the increasingly serious contamination of drug-resistant ***,a Cu-doped ZIF-8 particle with unsaturated copper exhibits high peroxidase-like activity.99.998%antibacterial efficiency against *** can be achieved for 30 min at a low concentration of 50μg·mL^(−1),as well as complete sterilization against ***(up to 8 log).99.999%antibacterial efficiency against Methicillin-resistant Staphylococcus aureus can be achieved,performing orders of magnitude higher than *** mechanism shows that the unsaturated Cu-Nx sites are enzyme-like active centers,which could promote the consumption of bacteria reducing substances by H_(2)O_(2),and the generated*OH further aggravates bacterial oxidative stress and membrane *** importantly,the oxidation activity of adsorbed oxygen species on Cu-ZIF-8 is enhanced by charge transfer and structural changes between the ligand and copper center like natural ***-doped ZIF-8 with peroxidase-like activity shows great potential in antibacterial application and the revealed catalytic mechanism is helpful for understanding the high antibacterial activity of nanoparticles with Cu-Nx sites.

关键词： Cu-doped ZIF-8 peroxidase-like reactive oxygen species(ROS) catalytic mechanism antibacterial activity

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Formation and Deflexion Characteristics of Explosively Formed Projectile with Eccentric Composite Shell Constraint 4

Formation and Deflexion Characteristics of Explosively Forme...

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4th International Conference on Defence technology, ICDT 2024

作者： Tang, Jing Yu, Rui Liao, Yu-Gang Wang, Ya-Jun Du, Hua-Chi Chen, Xiao-Hui Xi'an Modern Control Technology Research Institute National Key Laboratory of Land and Air Based Information Perception and Control Xi'an710065 China Liaoshen Industrial Group Co. Ltd Shenyang110045 China

In this study, the influence of eccentric composite shell constraint to the formation and deflexion characteristics of EFP were studied. The laser high speed photography picture at typical moments of eccentric composite shell EFPs were taken. The influence of outer shell material parameters, height decline ratio (Hot/Hit), eccentric ratio (Ls/Ll) on the formation of EFP were analyzed by establishing geometric models based on Lagrangian-Eulerian (ALE) algorithm. Unaxisymmetric outer shell exhibits eccentricity in the pressure of reflected detonation wave. This made the pressure loading time of the liner at thinner side of outer shell decreased. Therefore, the eccentric change of detonation wave led to the formation of a deflected EFP. Eccentric composite shell constraint has significant influence of formation and deflexion characteristics of EFP. With the strength, density and Ls/Ll of outer shell increase and the Hot/Hit decrease, the velocity L/D and compactness of EFP increase, which is beneficial for forming a compact EFP. However, the displacement in X direction of ballistic and the deflexion angle of EFP increases, which will decline stability to the flight of EFP in air. The shorter the outer shell, the smaller impact on forming and deflexion of EFP, when Hot/Hit of outer shell decreased to 0.4, the EFP characteristic parameter tended to stabilized. The research results can provide guidance for the asymmetric design of EFP. © 2024 institute of Physics Publishing. All rights reserved.

关键词： Detonation

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Graph convolutional network soft sensor for process quality prediction

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Journal of process control 2023年第1期123卷 12-25页

作者： Jia, Mingwei Xu, Danya Yang, Tao Liu, Yi Yao, Yuan Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Chemical Engineering National Tsing Hua University Hsinchu30013 Taiwan

The nonlinear time-varying characteristics of the process industry can be modeled using numerous data-driven soft sensor methods. However, the intrinsic relationships among the variables, especially the localized spatial–temporal correlations that shed light on model behavior, have received little attention. In this study, a soft sensor based on a graph convolutional network is constructed by introducing the concept of graph to process modeling. The focus is on obtaining localized spatial–temporal correlations that aid in comprehending the intricate interactions among the variables included in the soft sensor. The model is trained by considering the regularization terms and it learns distinctive localized spatial–temporal correlations in an end-to-end manner. Furthermore, long-term dependence is established via temporal convolution. Thus, both the localized spatial–temporal correlations and time-series properties are captured. The feasibility of the proposed soft sensor is illustrated using two fermentation processes. The localized spatial–temporal correlations of this case study are visualized, and they demonstrate that the soft sensor is not a black-box model;instead, it is consistent with process knowledge. © 2023 Elsevier Ltd

关键词： Convolution

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Latent-Enhanced Variational Adversarial Active Learning Assisted Soft Sensor

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IEEE Sensors Journal 2023年第14期23卷 15762-15772页

作者： Dai, Yun Yang, Chao Liu, Yi Yao, Yuan Zhejiang University of Technology Institute of Process Equipment and Control Engineering Hangzhou310023 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China National Tsing Hua University Department of Chemical Engineering Hsinchu300044 Taiwan

As the acquisition of variables that measure quality is typically challenging, labeled samples for building a model for soft sensors are often inadequate. Additionally, owing to the installation of redundant sensors, high-dimensional process data with strong correlations are acquired. Therefore, in this study, a sample selection strategy for active learning (AL), referred to as latent-enhanced variational adversarial AL (LVAAL), is developed to enhance quality prediction performance with limited labeled data. The LVAAL method uses a minimax game to explore the latent representation of the original data. Specifically, a latent-enhanced variational autoencoder (LEVAE) is used to deceive the adversarial network into predicting all samples, both labeled and unlabeled, as labeled. However, the adversarial network attempts to indicate the difference between latent representations. Subsequently, Gaussian process regression (GPR) is adopted as a base model for LVAAL. With the proposed LVAAL selection strategy, informative unlabeled samples are incrementally selected, thereby enhancing the prediction performance. The application results for a numerical case and an industrial example demonstrate the advantages of LVAAL compared with existing AL strategies. © 2001-2012 IEEE.

关键词： Gaussian distribution

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Factor analysis thermography for defect detection of panel paintings

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Quantitative InfraRed Thermography Journal 2023年第1期20卷 25-37页

作者： Liu, Kaixin Huang, Kai-Lun Sfarra, Stefano Yang, Jianguo Yi, Liu Yuan, Yao Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou China Department of Chemical Engineering National Tsing Hua University Hsinchu Taiwan Department of Industrial and Information Engineering and Economics University of L’Aquila L’Aquila Italy

Active infrared thermography is an important non-destructive testing method used for revealing defect structures in materials. In many applications, thermographic data processing is necessary to extract defect features from a large number of thermal images. This work proposes to use a factor analysis thermography (FAT) method that automatically extracts defect features from thermograms via exploratory factor analysis, in tandem with a fuzzy c-means (FCM) clustering algorithm to segment the defects and background. By means of factor rotation, factor analysis minimises the complexity of factor loadings and makes the results more interpretable. Consequently, the defect information is extracted while large signal-to-noise ratios are obtained. Employing the FCM image segmentation algorithm on factor loading images reduces the interference of background on human visual detection. Additionally, the parameter selection is emphasised and addressed. Experiments on a panel painting illustrate that the proposed method promotes the accuracy and efficiency of thermographic detection of defects, compared with the popular principal component thermography (PCT) method. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Nondestructive examination

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Deep convolutional autoencoder thermography for artwork defect detection

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Quantitative InfraRed Thermography Journal 2024年第6期21卷 367-383页

作者： Liu, Yi Wang, Fumin Liu, Kaixin Mostacci, Miranda Yao, Yuan Sfarra, Stefano Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou China Shanxi Key Laboratory of Signal Capturing & Processing North University of China Taiyuan China Professional Restorer Celano Italy Department of Chemical Engineering National Tsing Hua University Hsinchu Taiwan Department of Industrial and Information Engineering and Economics University of L’Aquila L’Aquila Italy

Infrared thermography is a cost-effective non-destructive evaluation technique that plays a critical role in extracting information about defects in cultural heritage such as works of art. However, in-depth studies on the internal structure of contemporary artworks using infrared thermography remain lacking, and therefore, a deep convolutional autoencoder thermography (DCAT) data analysis method is proposed for defect detection of contemporary artworks. In the proposed method, original data are enhanced by convolution to reduce the noise and inhomogeneous background in the original thermal images;subsequently, a deep autoencoder is used to extract nonlinear features from the enhanced thermographic data, and the results of the hidden layer are visualised. These visualised images highlight information regarding the internal structure and defects of the artwork. The case study on a handmade replica of Picasso’s ‘La Bouteille de Suze’, reproduced exactly as the original, has shown that DCAT significantly improved the accuracy of defect detection when compared with that of commonly used methods. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Nondestructive examination

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Insight into leaching rare earth from ion-adsorption type rare earth ores with citric acid:Performance,kinetic analysis and differentiation leaching

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Journal of Rare Earths 2025年第3期43卷 591-602,I0007页

作者： Mengfei Zhao Zedong Teng Xingyu Ma Xiaoliang Jiang Hualin Zhang Youming Yang Tinggang Li Faculty of Materials Metallurgy and Chemistry Jiangxi University of Science and TechnologyGanzhou341000China Key Laboratory of Rare Earths Jiangxi Institute of Rare EarthsGanjiang Innovation AcademyChinese Academy of SciencesGanzhou341119China CAS Key Laboratory of Green Process and Engineering Innovation Academy for Green ManufactureNational Engineering Research Center of Green Recycling for Strategic Metal ResourcesBeijing Engineering Research Centre of Process Pollution ControlInstitute of Process EngineeringChinese Academy ofSciencesBeijing100190China State Key Laboratory of Biochemical Engineering Key Laboratory of Biopharmaceutical Preparation and DeliveryChinese Academy of SciencesBeijing100190China University of Chinese Academy of Sciences Beijing100049China

The rare earth elements(REEs)extraction by chemical leaching from ion-adsorption type rare earth ores(IAREO)has led to serious ecological and environmental ***,demand for bioleaching is on the rise with the advantage of being *** one of the organic acids produced by biological metabolism,citric acid was used to leach REEs and explore the performance and *** results demonstrate that citric acid exhibits higher leaching efficiency(96.00%)for REEs at a relatively low concentration of 0.01 mol/L compared with(NH_(4))_(2)SO_(4)(84.29%,0.1 mol/L)and MgSO_(4)(83.99%,0.1 mol/L).Citric acid shows a preference for leaching heavy rare earth elements,with 99%leaching efficiency in IAREO,which shows higher capacity than(NH_(4))_(2)SO_(4)and MgSO_(4)(as inorganic leaching agents).Kinetic analysis indicates that the leaching process of REEs with citric acid is controlled by both the internal diffusion kinetics and chemical reaction kinetics,which is different from inorganic leaching *** Minteq calculations confirm that RE-Citrate is the main constituent of the extract solution in the leaching process of the IAREO,thereby enhancing the leaching efficiency of REEs from the *** suggests that citric acid may be used as a promising organic leaching agent for the environmentalfriendly extraction of REEs from IAREO.

关键词： Ion-adsorption type rare earth ores Rare earths Citric acid Complexation Organic acid Differential leaching

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