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Journal of Energy Chemistry 2025年第1期100卷 26-38页

作者： Xiaodong yang Haochen Shen Xiaoming Xiao Zhichao Li Qi Zhou Wei yang Bin Jiang Yongli Sun Luhong Zhang Zhenhua Yan School of Chemical Engineering and Technology Tianjin University Key Laboratory of Advanced Energy Materials Chemistry (Ministry of Education) Nankai University

This study presents a novel method to fabricate metal-decorated,sulfur-doped layered double hydroxides （M/SLDH） through spontaneous redox and sulfurization *** developed Ag/SLDH and Pt/SLDH catalysts with abundant heterogeneous interfaces and hierarchical nanostructures demonstrated outstanding oxygen evolution reaction （OER） and hydrogen evolution reaction （HER） performance,achieving low overpotentials of 212 and 35 mV at 10 mA cm-2in 1 M KOH,*** both anode and cathode in water splitting,they required only 1.47 V to reach 10 mA cm-2and exhibited high structural robustness,maintaining stability at 1000 mA cm-2for 300 ***-situ Raman analysis revealed that the synergistic effects of metal nanoparticles and S doping significantly promote the transformation into the S-Co1-xFexOOH layer,which serves as the active phase for water ***,ultraviolet photoelectron spectroscopy （UPS） and density functional theory （DFT） analyses indicated that incorporating metal nanoparticles and S doping increase electron density near the Fermi level and reduce reaction energy barriers,thus enhancing intrinsic OER and HER *** study provides a scalable strategy for synthesizing high-performance electrocatalysts for water splitting,with promising potential for broader applications.

关键词： Ultraviolet photoelectron spectroscopy

Joint Estimation of SOH and RUL for Lithium-Ion Batteries Based on Improved Twin Support Vector Machineh

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Energy Engineering 2025年第1期122卷 243-264页

作者： Liyao yang HongyanMa Yingda Zhang Wei He School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China Institute of Distributed Energy Storage Safety Big Data Beijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing100044China

Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management ***,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is *** address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and ***,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance ***,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution *** Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction ***,the proposed models are validated using NASA and CALCE lithium-ion battery *** results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.

关键词： State of health remaining useful life variational modal decomposition random forest twin support vector machine convolutional optimization algorithm

Laser‑Induced Highly Stable Conductive Hydrogels for Robust Bioelectronics

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Nano-Micro Letters 2025年第3期17卷 117-120页

作者： Yibo Li Hao Zhou Huayong yang Kaichen Xu State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical EngineeringZhejiang UniversityHangzhou 310058People’s Republic of China

Despite the promising progress in conductive hydrogels made with pure conducting polymer,great challenges remain in the interface adhesion and robustness in longterm *** address these challenges,*** Hwan Ko and Taek-Soo Kim’s team introduced a laserinduced phase separation and adhesion method for fabricating conductive hydrogels consisting of pure poly(3,4-ethylenedioxythiophene):polystyrene sulfonate on polymer *** laser-induced phase separation and adhesion treated conducting polymers can be selectively transformed into conductive hydrogels that exhibit wet conductivities of 101.4 S cm^(−1)with a spatial resolution down to 5μ***,they maintain impedance and charge-storage capacity even after 1 h of *** micropatterned electrode arrays demonstrate their potential in long-term in vivo signal recordings,highlighting their promising role in the field of bioelectronics.

关键词： Laser processing Conductive hydrogels Stable interface Bio-interfacing electrodes Bioelectronic application

Multi boron-doping effects in hard carbon toward enhanced sodium ion storage

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Journal of Energy Chemistry 2025年第1期100卷 730-738页

作者： Peng Zheng Wang Zhou Ying Mo Biao Zheng Miaomiao Han Qin Zhong Wenwen yang Peng Gao Lezhi yang Jilei Liu College of Materials Science and Engineering Hunan Joint International Laboratory of Advanced Materials and Technology for Clean Energy Hunan Province Key Laboratory for Advanced Carbon Materials and Applied Technology Hunan University Greater Bay Area Institute for Innovation Hunan University Changsha Research Institute of Mining & Metallurgy Co. LTD.

Hard carbon （HC） has been considered as promising anode material for sodium-ion batteries （SIBs）.The optimization of hard carbon’s microstructure and solid electrolyte interface （SEI） property are demonstrated effective in enhancing the Na+storage capability,however,a one-step regulation strategy to achieve simultaneous multi-scale structures optimization is highly ***,we have systematically investigated the effects of boron doping on hard carbon’s microstructure and interface chemistry.A variety of structure characterizations show that appropriate amount of boron doping can increase the size of closed pores via rearrangement of carbon layers with improved graphitization degree,which provides more Na+storage ***-situ Fourier transform infrared spectroscopy/electrochemical impedance spectroscopy （FTIR/EIS） and X-ray photoelectron spectroscopy （XPS） analysis demonstrate the presence of more BC3and less B–C–O structures that result in enhanced ion diffusion kinetics and the formation of inorganic rich and robust SEI,which leads to facilitated charge transfer and excellent rate *** a result,the hard carbon anode with optimized boron doping content exhibits enhanced rate and cycling *** general,this work unravels the critical role of boron doping in optimizing the pore structure,interface chemistry and diffusion kinetics of hard carbon,which enables rational design of sodium-ion battery anode with enhanced Na+storage performance.

关键词： X ray photoelectron spectroscopy

A state of art review on time series forecasting with machine learning for environmental parameters in agricultural greenhouses

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Information Processing in Agriculture 2024年第2期11卷 143-162页

作者： Gedi Liu keyang Zhong Huilin Li Tao Chen yang Wang National Innovation Center for Digital Fishery China Agricultural University100083 BeijingChina Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock Ministry of Agriculture and Rural AffairsChina Agricultural University100083 BeijingChina Beijing Engineering and Technology Research Center for Internet of Things in Agriculture China Agricultural University100083 BeijingChina College of Information and Electrical Engineering China Agricultural University100083 BeijingChina Department of Chemical and Process Engineering University of SurreyGU27XH GuildfordUnited Kingdom

Agricultural greenhouse production has to require a stable and acceptable environment,it is therefore essential for future greenhouse production to obtain full and precisely internal dynamic environment *** modeling based on machine learning methods,e.g.,intelligent time series prediction modeling,is a popular and suitable way to solve the above *** this article,a systematic literature review on applying advanced time series models has been systematically conducted via a detailed analysis and evaluation of 61 pieces selected from 221 *** historical process of time series model application from the use of data and information strategies was first ***,the accuracy and generalization of the model from the selection of model parameters and time steps,providing a new perspective for model development in this field,were compared and ***,the systematic review results demonstrate that,compared with traditional models,deep neural networks could increase data structure mining capabilities and overall information simulation capabilities through innovative and effective structures,thereby it could also broaden the selection range of environmental parameters for agricultural facilities and achieve environmental prediction end-to-end optimization via intelligent time series model based on deep neural networks.

关键词： Horticultural greenhouse environment Time series algorithms Prediction Deep neural networks

Targeting TrkB–PSD-95 coupling to mitigate neurological disorders

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Neural Regeneration Research 2025年第3期20卷 715-724页

作者： Xin yang Yu-Wen Alvin Huang John Marshall Department of Molecular Biology Cell Biology and BiochemistryBrown UniversityProvidenceRIUSA Department of Neurology Warren Alpert Medical School of Brown UniversityProvidenceRIUSA Center for Translational Neuroscience Robert J.and Nancy D.Carney Institute for Brain Science and Brown Institute for Translational ScienceBrown UniversityProvidenceRIUSA

Tropomyosin receptor kinase B(TrkB)signaling plays a pivotal role in dendritic growth and dendritic spine formation to promote learning and *** activity-dependent release of brain-derived neurotrophic factor at synapses binds to pre-or postsynaptic TrkB resulting in the strengthening of synapses,reflected by long-term ***,the association of postsynaptic density protein-95 with TrkB enhances phospholipase Cγ-Ca^(2+)/calmodulin-dependent protein kinaseⅡand phosphatidylinositol 3-kinase-mechanistic target of rapamycin signaling required for long-term *** this review,we discuss TrkB-postsynaptic density protein-95 coupling as a promising strategy to magnify brain-derived neurotrophic factor signaling towards the development of novel therapeutics for specific neurological disorders.A reduction of TrkB signaling has been observed in neurodegenerative disorders,such as Alzheimer's disease and Huntington's disease,and enhancement of postsynaptic density protein-95 association with TrkB signaling could mitigate the observed deficiency of neuronal connectivity in schizophrenia and *** with brain-derived neurotrophic factor is problematic,due to poor pharmacokinetics,low brain penetration,and side effects resulting from activation of the p75 neurotrophin receptor or the truncated TrkB.T1 *** TrkB agonists and antibodies that activate TrkB are being intensively investigated,they cannot distinguish the multiple human TrkB splicing isoforms or cell type-specific *** TrkB–postsynaptic density protein-95 coupling provides an alternative approach to specifically boost TrkB signaling at localized synaptic sites versus global stimulation that risks many adverse side effects.

关键词： Angelman syndrome autism brain-derived neurotrophic factor depression neurodegenerative disorder neurodevelopmental disorder postsynaptic density protein-95 synaptic plasticity TrkB

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Magneto‑Dielectric Synergy and Multiscale Hierarchical Structure Design Enable Flexible Multipurpose Microwave Absorption and Infrared Stealth Compatibility

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Nano-Micro Letters 2025年第2期17卷 401-416页

作者： Chen Li Leilei Liang Baoshan Zhang Yi yang Guangbin Ji School of Electronic Science and Engineering Nanjing UniversityNanjing 210093People’s Republic of China College of Materials Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 210016People’s Republic of China

Developing advanced stealth devices to cope with radar-infrared(IR)fusion detection and diverse application scenarios is increasingly demanded,which faces significant challenges due to conflicting microwave and IR cloaking mechanisms and functional integration ***,we propose a multiscale hierarchical structure design,integrating wrinkled MXene IR shielding layer and flexible Fe_(3)O_(4)@C/PDMS microwave absorption *** top wrinkled MXene layer induces the intensive diffuse reflection effect,shielding IR radiation signals while allowing microwave to pass ***,the permeable microwaves are assimilated into the bottom Fe_(3)O_(4)@C/PDMS layer via strong magneto-electric *** theoretical and experimental optimization,the assembled stealth devices realize a near-perfect stealth capability in both X-band(8–12 GHz)and long-wave infrared(8–14μm)wavelength ***,it delivers a radar cross-section reduction of−20 dB m^(2),a large apparent temperature modulation range(ΔT=70℃),and a low average IR emissivity of ***,the optimal device demonstrates exceptional curved surface conformability,self-cleaning capability(contact angle≈129°),and abrasion resistance(recovery time≈5 s).This design strategy promotes the development of multispectral stealth technology and reinforces its applicability and durability in complex and hostile environments.

关键词： Microwave absorption Radar-infrared compatible stealth Wrinkled MXene Magneto-dielectric synergy Multifunction

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IoT Empowered Early Warning of Transmission Line Galloping Based on Integrated Optical Fiber Sensing and Weather Forecast Time Series Data

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Computers, Materials & Continua 2025年第1期82卷 1171-1192页

作者： Zhe Li Yun Liang Jinyu Wang yang Gao State Grid Henan Electric Power Company Electric Power Scientific Research Institute State Grid Henan Electric Power CompanyZhengzhou450052China

Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power *** early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false *** address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather ***,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused ***,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network ***,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this ***,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.

关键词： Optical fiber sensing multi-source data fusion early warning of galloping time series data IoT adaptive weighted learning irregular time series perception closed-loop attention mechanism

Ammonia pyrolysis oxidation excited by nanosecond pulsed discharge:Global/fluid models hybrid solution

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Journal of Energy Chemistry 2025年第1期100卷 385-396页

作者： Mingming Zhang Hanyu Wu Qi Chen Jiaxin yang Bonan Liu School of Mechanical Electronic and Control Engineering Beijing Jiaotong University China University of Petroleum

The kinetic characteristics of plasma-assisted oxidative pyrolysis of ammonia are studied by using the global/fluid models hybrid solution ***,the stable products of plasma-assisted oxidative pyrolysis of ammonia are *** results show that the consumption of NH3/O2and the production of N2/H2change linearly with the increase of voltage,which indicates the decoupling of nonequilibrium molecular excitation and oxidative pyrolysis of ammonia at low ***,the detailed reaction kinetics mechanism of ammonia oxidative pyrolysis stimulated by a nanosecond pulse voltage at low pressure and room temperature is *** on the reaction path analysis,the simplified mechanism is *** detailed and simplified mechanism simulation results are compared with experimental data to verify the accuracy of the simplified ***,based on the simplified mechanism,the fluid model of ammonia oxidative pyrolysis stimulated by the nanosecond pulse plasma is established to study the pre-sheath/sheath behavior and the resultant consumption and formation of key *** results show that the generation,development,and propagation of the pre-sheath have a great influence on the formation and consumption of *** consumption of NH3by the cathode pre-sheath is greater than that by the anode pre-sheath,but the opposite is true for OH and O（1S）.However,within the sheath,almost all reactions do not ***,by changing the parameters of nanosecond pulse power supply voltage,it is found that the electron number density,electron current density,and applied peak voltages are not the direct reasons for the structural changes of the sheath and ***,the discharge interval has little effect on the sheath structure and gas mixture *** research results of this paper not only help to understand the kinetic promotion of non-equilibrium excitation in the process of oxidative pyrolysis but also help to explore th

关键词： Nanosecond pulsed discharge Ammonia Oxidative pyrolysis Fluid model Global model

Interpretable Machine Learning Method for Compressive Strength Prediction and Analysis of Pure Fly Ash-based Geopolymer Concrete

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Journal of Wuhan University of Technology(Materials Science) 2025年第1期40卷 65-78页

作者： SHI Yuqiong LI Jingyi ZHANG yang LI Li Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education Northwest A&F Uni-versityYangling 712100China College of Water Resources and Architectural Engineering Northwest A&F UniversityYangling 712100China Chongqing Key Laboratory of Public Big Data Security Technology Chongqing 401420China Chongqing College of Mobile Communication Chongqing 401520China Department of Civil and Environmental Engineering The Hong Kong Polytechnic UniversityHung HomKowloonHong KongChina National Rail Transit Electrification and Automation Engineering Technology Research Center(Hong Kong Branch) The Hong Kong Polytechnic UniversityKowloonHung HomHong KongChina

In order to study the characteristics of pure fly ash-based geopolymer concrete(PFGC)conveniently,we used a machine learning method that can quantify the perception of characteristics to predict its compressive *** this study,505 groups of data were collected,and a new database of compressive strength of PFGC was *** order to establish an accurate prediction model of compressive strength,five different types of machine learning networks were used for comparative *** five machine learning models all showed good compressive strength prediction performance on *** them,R2,MSE,RMSE and MAE of decision tree model(DT)are 0.99,1.58,1.25,and 0.25,*** R2,MSE,RMSE and MAE of random forest model(RF)are 0.97,5.17,2.27 and 1.38,*** two models have high prediction accuracy and outstanding generalization *** order to enhance the interpretability of model decision-making,we used importance ranking to obtain the perception of machine learning model to 13 *** 13 variables include chemical composition of fly ash(SiO_(2)/Al_(2)O_(3),Si/Al),the ratio of alkaline liquid to the binder,curing temperature,curing durations inside oven,fly ash dosage,fine aggregate dosage,coarse aggregate dosage,extra water dosage and sodium hydroxide *** temperature,specimen ages and curing durations inside oven have the greatest influence on the prediction results,indicating that curing conditions have more prominent influence on the compressive strength of PFGC than ordinary Portland cement *** importance of curing conditions of PFGC even exceeds that of the concrete mix proportion,due to the low reactivity of pure fly ash.

关键词： machine learning pure fly ash geopolymer compressive strength feature perception