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Ultra‑Transparent and Multifunctional IZVO Mesh Electrodes for Next‑Generation Flexible Optoelectronics

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Nano-Micro Letters 2025年第1期17卷 293-309页

作者： Kiran A.Nirmal Tukaram D.Dongale Atul C.Khot Chenjie Yao Nahyun Kim Tae Geun Kim school of Electrical engineering Korea UniversityAnam‑ro 145Seongbuk‑guSeoulRepublic of Korea Computational electronics and Nanoscience Research Laboratory School of Nanoscience and BiotechnologyShivaji UniversityKolhapur 416004India

Mechanically durable transparent electrodes are essential for achieving long-term stability in flexible optoelectronic ***,they are crucial for applications in the fields of energy,display,healthcare,and soft *** meshes represent a promising alternative to traditional,brittle,metal oxide conductors due to their high electrical conductivity,optical transparency,and enhanced mechanical *** this paper,we present a simple method for fabricating an ultra-transparent conducting metal oxide mesh electrode using selfcracking-assisted *** this method,we produced an electrode with ultra-transparency(97.39%),high conductance(Rs=21.24Ωsq^(−1)),elevated work function(5.16 eV),and good mechanical *** also evaluated the effectiveness of the fabricated electrodes by integrating them into organic photovoltaics,organic light-emitting diodes,and flexible transparent memristor devices for neuromorphic computing,resulting in exceptional device *** addition,the unique porous structure of the vanadium-doped indium zinc oxide mesh electrodes provided excellent flexibility,rendering them a promising option for application in flexible optoelectronics.

关键词： Self-cracking template Vanadium-doped indium zinc oxide mesh Organic solar cells Organic light-emitting diodes Flexible transparent memory

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CaH2-promoted activity of Ni-carbonate interface for CO2 methanation

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Journal of Energy Chemistry 2025年第1期100卷 522-532页

作者： Jin-Peng Wang Guo-Cui Mao Hui-Lin Jiang Bao-Xia Dong Yun-Lei Teng school of Chemistry and Chemical engineering Yangzhou University

Transition metal-carbonate interfaces often act as active sites in heterogeneous catalytic *** interface between transition metal and metal carbonate exhibits a dynamic equilibrium during the CO2hydrogenation reaction,involving surface carbonate hydrogenation and ***,there have been few reports on engineering the activity of the interface between transition metal and alkaline earth metal carbonate for catalytic *** work demonstrated that the incorporation of CaH2in Ni/CaCO3enhances the CO2methanation activity of the *** CO2conversion for Ni/CaH2-CaCO3reached 68.5%at 400°C,which was much higher than that of the Ni/CaCO3（31.6%） and Ni/CaH2-CaO （42.4%） ***,the Ni/CaH2-CaCO3catalysts remained stable during the stability test for 24 h at 400°C and 8 *** research revealed that CaH2played a crucial role in promoting the activity of the Ni-carbonate interface for ***2could modify the electronic structure of Ni and tune the structural properties of CaCO3to generate medium basic sites （OH groups）,which are favorable for the activation of H2and ***-situ Fourier transform infrared spectroscopy （FTIR） analysis combined with density functional theory calculations demonstrated that CO2activation occurs at the hydroxyl group （OH） on the CaH2-modified Ni-carbonate surface,leading to the formation of CO3H****,our study has confirmed that CO2methanation over the Ni/CaH2-CaCO3catalysts proceeds via the formate pathway.

关键词： CO 2 methanation Metal hydride Ni catalysts Carbonate Metal-carbonate interface

Quickly obtaining densely dispersed coherent particles in steel matrix and its related mechanical property

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International Journal of Minerals,Metallurgy and Materials 2025年第1期32卷 111-118页

作者： Xiaoxiao Wang Qingsong Huang school of Chemical engineering Sichuan UniversityChengdu 610065China

Densely distributed coherent nanoparticles(DCN)in steel matrix can enhance the work-hardening ability and ductility of steel *** the routes to this end can be generally classified into the liquid-solid route and the solid-solid ***,the formation of DCN structures in steel requires long processes and complex *** far,obtaining steel with coherent particle enhancement in a short time remains a bottleneck,and some necessary steps remain ***,we show a high-efficiency liquid-phase refining process reinforced by a dynamic magnetic ***-Y-Mn-O particles had an average size of around(3.53±1.21)nm and can be obtained in just around 180 *** small nanoparticles were coherent with the matrix,implying no accumulated dislocations between the particles and the steel *** findings have a potential application for improving material machining capacity,creep resistance,and radiation resistance.

关键词： ferritic steels coherent particles microstructure compression test work hardening

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Remaining Life Prediction Method for Photovoltaic Modules Based on Two-Stage Wiener Process

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Energy engineering 2025年第1期122卷 331-347页

作者： Jie Lin Hongchi Shen Tingting Pei Yan Wu school of Electrical engineering and Information engineering Lanzhou University of TechnologyLanzhou730050China

Photovoltaic (PV) modules, as essential components of solar power generation systems, significantly influence unitpower generation *** service life of these modules directly affects these costs. Over time, the performanceof PV modules gradually declines due to internal degradation and external environmental *** cumulativedegradation impacts the overall reliability of photovoltaic power generation. This study addresses the complexdegradation process of PV modules by developing a two-stage Wiener process model. This approach accountsfor the distinct phases of degradation resulting from module aging and environmental influences. A powerdegradation model based on the two-stage Wiener process is constructed to describe individual differences inmodule degradation processes. To estimate the model parameters, a combination of the Expectation-Maximization(EM) algorithm and the Bayesian method is employed. Furthermore, the Schwarz Information Criterion (SIC) isutilized to identify critical change points in PV module degradation trajectories. To validate the universality andeffectiveness of the proposed method, a comparative analysis is conducted against other established life predictiontechniques for PV modules.

关键词： Photovoltaic modules degradation stochastic processes lifetime prediction

Mechanism Study on the Effect of Retarder on Polyurethane Setting Time Based on Molecular Simulation

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Journal of Wuhan University of Technology(Materials Science) 2025年第1期40卷 224-231页

作者： WU Yuxuan XU Wenyuan YU Tianlai JI Yongcheng school of Civil engineering Northeast Forestry UniversityHarbin 150040China

This study identified castor oil and phosphate ester as effective retarders through setting time,tensile,and flexural tests,and determined their optimal *** mechanism by which phosphate ester affects the setting time of polyurethane was further investigated using molecular dynamics *** transform infrared spectroscopy was also employed to systematically study the physical and chemical interactions between phosphate esters and polyurethane *** results demonstrate that a 1%concentration of phosphate ester provides the most effective retarding effect with minimal impact on the strength of *** phosphate ester is added to the B component of the two-component polyurethane system,its interaction energy with component A decreases,as do the diffusion coefficient and aggregation degree of component B on the surface of component *** reduction in interaction slows the setting ***,the addition of phosphate ester to polyurethane leads to the disappearance or weakening of functional groups,indicating competitive interactions within the phosphate ester components that inhibit the reaction rate.

关键词： polyurethane retarder setting time molecular dynamics diffusion coefficient

RF Optimizer Model for Predicting Compressive Strength of Recycled Concrete

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Journal of Wuhan University of Technology(Materials Science) 2025年第1期40卷 215-223页

作者： LIU Lin WANG Liuyan WANG Hui SUN Huayue school of Civil engineering and Architecture Shenyang UniversityShenyang 110000China

Traditional machine learning(ML)encounters the challenge of parameter adjustment when predicting the compressive strength of reclaimed *** address this issue,we introduce two optimized hybrid models:the Bayesian optimization model(B-RF)and the optimal model(Stacking model).These models are applied to a data set comprising 438 observations with five input variables,with the aim of predicting the compressive strength of reclaimed ***,we evaluate the performance of the optimized models in comparison to traditional machine learning models,such as support vector regression(SVR),decision tree(DT),and random forest(RF).The results reveal that the Stacking model exhibits superior predictive performance,with evaluation indices including R2=0.825,MAE=2.818 and MSE=14.265,surpassing the traditional ***,we also performed a characteristic importance analysis on the input variables,and we concluded that cement had the greatest influence on the compressive strength of reclaimed concrete,followed by ***,the Stacking model can be recommended as a compressive strength prediction tool to partially replace laboratory compressive strength testing,resulting in time and cost savings.

关键词： machine learning recycled concrete compressive strength

Local magnetic moment oscillation around an Anderson impurity on graphene

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 192-201页

作者： Shuai Li Zhen Ma Jin-Hua Gao school of Science Hebei University of Science and Technology school of electronics and Information Zhengzhou University of Light Industry school of Physics and Wuhan National High Magnetic Field Center Huazhong University of Science and Technology

We theoretically investigate the spin resolved Friedel oscillation （FO） and quasiparticle interference （QPI） in graphene induced by an Anderson *** the impurity becomes magnetic,the resulted FO becomes spin dependent,which gives rise to a local magnetic moment oscillation with an envelop decaying as r-2in real space in the doping ***,at half filling,the electron density and local magnetic moment will not oscillate but decay as *** spin resolved FO has both sublattice and spin ***,the local magnetic moment decay at half filling only occurs at one sublattice of graphene,which is quite like the phenomenon observed in the STM experiment (***ález-Herrero et al.,Science 352,437 （2016）).We further give an analytic formula about such spin dependent FO based on the stationary phase ***,we study the interference of quasiparticles around the magnetic impurity by calculating the spin dependent Fourier-transformed local density of states （FT-LDOS）.Our work gives a comprehensive understanding about the local magnetic moment oscillation around an Anderson impurity on graphene.

关键词： Friedel oscillation local magnetic moment graphene Anderson impurity

Multi-model ensemble learning for battery state-of-health estimation:Recent advances and perspectives

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Journal of Energy Chemistry 2025年第1期100卷 739-759页

作者： Chuanping Lin Jun Xu Delong Jiang Jiayang Hou Ying Liang Zhongyue Zou Xuesong Mei State Key Laboratory for Manufacturing Systems engineering Xi'an Jiaotong University Shaanxi Key Laboratory of Intelligent Robots School of Mechanical Engineering Xi'an Jiaotong University Department of Electrical engineering and Automation Luoyang Institute of Science and Technology school of Mechanical engineering Hangzhou Dianzi University

The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational performance. Despite numerous data-driven methods reported in existing research for battery SOH estimation, these methods often exhibit inconsistent performance across different application scenarios. To address this issue and overcome the performance limitations of individual data-driven models,integrating multiple models for SOH estimation has received considerable attention. Ensemble learning(EL) typically leverages the strengths of multiple base models to achieve more robust and accurate outputs. However, the lack of a clear review of current research hinders the further development of ensemble methods in SOH estimation. Therefore, this paper comprehensively reviews multi-model ensemble learning methods for battery SOH estimation. First, existing ensemble methods are systematically categorized into 6 classes based on their combination strategies. Different realizations and underlying connections are meticulously analyzed for each category of EL methods, highlighting distinctions, innovations, and typical applications. Subsequently, these ensemble methods are comprehensively compared in terms of base models, combination strategies, and publication trends. Evaluations across 6 dimensions underscore the outstanding performance of stacking-based ensemble methods. Following this, these ensemble methods are further inspected from the perspectives of weighted ensemble and diversity, aiming to inspire potential approaches for enhancing ensemble performance. Moreover, addressing challenges such as base model selection, measuring model robustness and uncertainty, and interpretability of ensemble models in practical applications is emphasized. Finally, future research prospects are outlined, specifically noting that deep learning ensemble is poised to advance ens

关键词： Contrastive Learning

Endoscopy-assisted lightweight diagnosis system based on transformers for colon polyp detection

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Optoelectronics Letters 2025年第1期21卷 57-64页

作者： FAN Weiming YU Jiahui JU Zhaojie school of Automation and Electrical engineering Shenyang Ligong University Department of Biomedical engineering Zhejiang University school of Computing University of Portsmouth

The integration of endoscopy has significantly propelled the diagnosis and treatment of gastrointestinal diseases, with colonoscopy establishing itself as the primary method for early diagnosis and preventive care in colorectal cancer(CRC). Although deep learning holds promise in mitigating missed polyp rates, modern endoscopy examinations pose additional challenges, such as image blurring and atomizing. This study explores lightweight yet powerful attention mechanisms, introducing the spatial-channel transformer(SCT), an innovative approach that leverages spatial channel relationships for attention weight calculation. The method utilizes rotation operations for inter-dimensional dependencies, followed by residual transformation, encoding inter-channel and spatial information with minimal computational overhead. Extensive experiments on the CVC-Clinic DB polyp detection dataset, addressing endoscopy pitfalls, underscore the superiority of our SCT over other state-of-the-art methods. The proposed model maintains high performance, even in challenging scenarios.

关键词： Endoscopy