In the dynamic landscape of software technologies,the demand for sophisticated applications across diverse industries is ever⁃increasing.However,predicting software defects remains a crucial challenge for ensuring the...In the dynamic landscape of software technologies,the demand for sophisticated applications across diverse industries is ever⁃increasing.However,predicting software defects remains a crucial challenge for ensuring the resilience and dependability of software systems.This study presents a novel software defect prediction technique that significantly enhances performance through a hybrid machine learning approach.The innovative methodology integrates a Genetic Algorithm(GA)for precise feature selection,a Decision Tree(DT)for robust classification,and leverages the capabilities of Particle Swarm Optimization(PSO)and Ant Colony Optimization(ACO)algorithms for precision⁃driven optimization.The utilization of datasets from varied sources enriches the predictive prowess of our model.Of particular significance in our pursuit is the unwavering focus on enhancing the prediction process through a highly refined PSO⁃ACO algorithm,thereby optimizing the efficiency and effectiveness of the GA⁃DT hybrid model.The thorough evaluation of our proposed approach unfolds across seven software projects,unveiling a paradigm shift in performance metrics.Results unequivocally demonstrate that the GA⁃DT with PSO⁃ACO algorithm surpasses its counterparts,showcasing unparalleled accuracy and reliability.Furthermore,our hybrid approach demonstrates outstanding performance in terms of F⁃measure,with an impressive increase rate of 78%.展开更多
Teacher–student relationships play a vital role in improving college students’academic performance and the quality of higher education.However,empirical studies with substantial data-driven insights remain limited.T...Teacher–student relationships play a vital role in improving college students’academic performance and the quality of higher education.However,empirical studies with substantial data-driven insights remain limited.To address this gap,this study collected 3278 questionnaires from seven universities across four provinces in China to analyze the key factors affecting college students’academic performance.A machine learning framework,CQFOA-KELM,was developed by enhancing the Fruit Fly Optimization Algorithm(FOA)with Covariance Matrix Adaptation Evolution Strategy(CMAES)and Quadratic Approximation(QA).CQFOA significantly improved population diversity and was validated on the IEEE CEC2017 benchmark functions.The CQFOA-KELM model achieved an accuracy of 98.15%and a sensitivity of 98.53%in predicting college students’academic performance.Additionally,it effectively identified the key factors influencing academic performance through the feature selection process.展开更多
The development of anode materials with high rate capability and long charge-discharge plateau is the key to improve per-formance of lithium-ion capacitors(LICs).Herein,the porous graphitic carbon(PGC-1300)derived fro...The development of anode materials with high rate capability and long charge-discharge plateau is the key to improve per-formance of lithium-ion capacitors(LICs).Herein,the porous graphitic carbon(PGC-1300)derived from a new triply interpenetrated co-balt metal-organic framework(Co-MOF)was prepared through the facile and robust carbonization at 1300°C and washing by HCl solu-tion.The as-prepared PGC-1300 featured an optimized graphitization degree and porous framework,which not only contributes to high plateau capacity(105.0 mAh·g^(−1)below 0.2 V at 0.05 A·g^(−1)),but also supplies more convenient pathways for ions and increases the rate capability(128.5 mAh·g^(−1)at 3.2 A·g^(−1)).According to the kinetics analyses,it can be found that diffusion regulated surface induced capa-citive process and Li-ions intercalation process are coexisted for lithium-ion storage.Additionally,LIC PGC-1300//AC constructed with pre-lithiated PGC-1300 anode and activated carbon(AC)cathode exhibited an increased energy density of 102.8 Wh·kg^(−1),a power dens-ity of 6017.1 W·kg^(−1),together with the excellent cyclic stability(91.6%retention after 10000 cycles at 1.0 A·g^(−1)).展开更多
Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in th...Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many areas.In this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,China.The two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction results.In addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.展开更多
Rectifying circuit,as a crucial component for converting alternating current into direct current,plays a pivotal role in energy harvesting microsystems.Traditional silicon-based or germanium-based rectifier diodes hin...Rectifying circuit,as a crucial component for converting alternating current into direct current,plays a pivotal role in energy harvesting microsystems.Traditional silicon-based or germanium-based rectifier diodes hinder system integration due to their specific manufacturing processes.Conversely,metal oxide diodes,with their simple fabrication techniques,offer advantages for system integration.The oxygen vacancy defect of oxide semiconductor will greatly affect the electrical performance of the device,so the performance of the diode can be effectively controlled by adjusting the oxygen vacancy concentration.This study centers on optimizing the performance of diodes by modulating the oxygen vacancy concentration within InGaZnO films through control of oxygen flows during the sputtering process.Experimental results demonstrate that the diode exhibits a forward current density of 43.82 A·cm^(−2),with a rectification ratio of 6.94×10^(4),efficiently rectifying input sine signals with 1 kHz frequency and 5 V magnitude.These results demonstrate its potential in energy conversion and management.By adjusting the oxygen vacancy,a methodology is provided for optimizing the performance of rectifying diodes.展开更多
The 3D reconstruction pipeline uses the Bundle Adjustment algorithm to refine the camera and point parameters. The Bundle Adjustment algorithm is a compute-intensive algorithm, and many researchers have improved its p...The 3D reconstruction pipeline uses the Bundle Adjustment algorithm to refine the camera and point parameters. The Bundle Adjustment algorithm is a compute-intensive algorithm, and many researchers have improved its performance by implementing the algorithm on GPUs. In the previous research work, “Improving Accuracy and Computational Burden of Bundle Adjustment Algorithm using GPUs,” the authors demonstrated first the Bundle Adjustment algorithmic performance improvement by reducing the mean square error using an additional radial distorting parameter and explicitly computed analytical derivatives and reducing the computational burden of the Bundle Adjustment algorithm using GPUs. The naïve implementation of the CUDA code, a speedup of 10× for the largest dataset of 13,678 cameras, 4,455,747 points, and 28,975,571 projections was achieved. In this paper, we present the optimization of the Bundle Adjustment algorithm CUDA code on GPUs to achieve higher speedup. We propose a new data memory layout for the parameters in the Bundle Adjustment algorithm, resulting in contiguous memory access. We demonstrate that it improves the memory throughput on the GPUs, thereby improving the overall performance. We also demonstrate an increase in the computational throughput of the algorithm by optimizing the CUDA kernels to utilize the GPU resources effectively. A comparative performance study of explicitly computing an algorithm parameter versus using the Jacobians instead is presented. In the previous work, the Bundle Adjustment algorithm failed to converge for certain datasets due to several block matrices of the cameras in the augmented normal equation, resulting in rank-deficient matrices. In this work, we identify the cameras that cause rank-deficient matrices and preprocess the datasets to ensure the convergence of the BA algorithm. Our optimized CUDA implementation achieves convergence of the Bundle Adjustment algorithm in around 22 seconds for the largest dataset compared to 654 seconds for the sequential implementation, resulting in a speedup of 30×. Our optimized CUDA implementation presented in this paper has achieved a 3× speedup for the largest dataset compared to the previous naïve CUDA implementation.展开更多
The growing demands for energy storage systems,electric vehicles,and portable electronics have significantly pushed forward the need for safe and reliable lithium batteries.It is essential to design functional separat...The growing demands for energy storage systems,electric vehicles,and portable electronics have significantly pushed forward the need for safe and reliable lithium batteries.It is essential to design functional separators with improved mechanical and electrochemical characteristics.This review covers the improved mechanical and electrochemical performances as well as the advancements made in the design of separators utilizing a variety of techniques.In terms of electrolyte wettability and adhesion of the coating materials,we provide an overview of the current status of research on coated separators,in situ modified separators,and grafting modified separators,and elaborate additional performance parameters of interest.The characteristics of inorganics coated separators,organic framework coated separators and inorganic-organic coated separators from different fabrication methods are compared.Future directions regarding new modified materials,manufacturing process,quantitative analysis of adhesion and so on are proposed toward next-generation advanced lithium batteries.展开更多
The available test methods for optimal moisture content of cold recycled mixture(CRM)as well as its bulk specific gravity,and theoretical maximum relative density were analyzed in this work.Some test improvements were...The available test methods for optimal moisture content of cold recycled mixture(CRM)as well as its bulk specific gravity,and theoretical maximum relative density were analyzed in this work.Some test improvements were suggested to improve test control of the CRM road performance based on the discovered flaws.Besides,the properties of reclaimed asphalt pavement(RAP),including the content of old asphalt,penetration index,passing rate of 4.75 mm sieve,and gradation change rate after extraction,were examined.The effects of RAP characteristics on splitting tensile strength,water stability,the high-and low-temperature performance of emulsified asphalt CRM were studied.The results show that the optimum moisture content of CRM should be determined when the compaction work matches the specimen’s molding work.Among the analyzed methods of bulk specific gravity assessment,the dry-surface and CoreLok methods provide more robust and accurate results than the wax-sealing method,while the dry-surface method is the most cost-efficient.The modified theoretical maximum relative density test method is proposed,which can reduce the systematic error of the vacuum test method.The following RAP-CRM trends can be observed.The lower the content of old asphalt and the smaller the change rate of gradation,the smaller the voids and the better the water stability of CRM.The greater the penetration of old asphalt,the higher the fracture work and low-temperature splitting strength.The greater the penetration,the higher the passing rate of 4.75 mm sieve after extraction,and the worse the high-temperature performance of CRM.展开更多
The development of high-performance structural and functional materials is vital in many industrial fields.High-and medium-entropy alloys(H/MEAs)with superior comprehensive properties owing to their specific microstru...The development of high-performance structural and functional materials is vital in many industrial fields.High-and medium-entropy alloys(H/MEAs)with superior comprehensive properties owing to their specific microstructures are promising candidates for structural materials.More importantly,multitudinous efforts have been made to regulate the microstructures and the properties of H/MEAs to further expand their industrial applications.The various heterostructures have enormous potential for the development of H/MEAs with outstanding performance.Herein,multiple heterogeneous structures with single and hierarchical heterogeneities were discussed in detail.Moreover,preparation methods for compositional inhomogeneity,bimodal structures,dualphase structures,lamella/layered structures,harmonic structures(core-shell),multiscale precipitates and heterostructures coupled with specific microstructures in H/MEAs were also systematically reviewed.The deformation mechanisms induced by the different heterostructures were thoroughly discussed to explore the relationship between the heterostructures and the optimized properties of H/MEAs.The contributions of the heterostructures and advanced microstructures to the H/MEAs were comprehensively elucidated to further improve the properties of the alloys.Finally,this review discussed the future challenges of high-performance H/MEAs for industrial applications and provides feasible methods for optimizing heterostructures to enhance the comprehensive properties of H/MEAs.展开更多
This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization balanc...This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization balance.By integrating surrogate models to approximate the objective functions,SMOGWO significantly improves the efficiency and accuracy of the optimization process.The effectiveness of this approach is evaluated using the CEC2009 multi-objective test function suite,where SMOGWO achieves a superiority rate of 76.67%compared to other leading multi-objective algorithms.Furthermore,the practical applicability of SMOGWO is demonstrated through a case study on empty and heavy train allocation,which validates its ability to balance line capacity,minimize transportation costs,and optimize the technical combination of heavy trains.The research highlights SMOGWO's potential as a robust solution for optimization challenges in railway transportation,offering valuable contributions toward enhancing operational efficiency and promoting sustainable development in the sector.展开更多
Rock fragmentation is an important indicator for assessing the quality of blasting operations.However,accurate prediction of rock fragmentation after blasting is challenging due to the complicated blasting parameters ...Rock fragmentation is an important indicator for assessing the quality of blasting operations.However,accurate prediction of rock fragmentation after blasting is challenging due to the complicated blasting parameters and rock properties.For this reason,optimized by the Bayesian optimization algorithm(BOA),four hybrid machine learning models,including random forest,adaptive boosting,gradient boosting,and extremely randomized trees,were developed in this study.A total of 102 data sets with seven input parameters(spacing-to-burden ratio,hole depth-to-burden ratio,burden-to-hole diameter ratio,stemming length-to-burden ratio,powder factor,in situ block size,and elastic modulus)and one output parameter(rock fragment mean size,X_(50))were adopted to train and validate the predictive models.The root mean square error(RMSE),the mean absolute error(MAE),and the coefficient of determination(R^(2))were used as the evaluation metrics.The evaluation results demonstrated that the hybrid models showed superior performance than the standalone models.The hybrid model consisting of gradient boosting and BOA(GBoost-BOA)achieved the best prediction results compared with the other hybrid models,with the highest R^(2)value of 0.96 and the smallest values of RMSE and MAE of 0.03 and 0.02,respectively.Furthermore,sensitivity analysis was carried out to study the effects of input variables on rock fragmentation.In situ block size(XB),elastic modulus(E),and stemming length-to-burden ratio(T/B)were set as the main influencing factors.The proposed hybrid model provided a reliable prediction result and thus could be considered an alternative approach for rock fragment prediction in mining engineering.展开更多
A novel precipitate-free Mg-0.1Sn anode with a homogeneous equal-axis grain structure was developed and rolled successfully at 573 K.Electrochemical test results indicate that the Mg-0.1Sn alloy exhibits enhanced anod...A novel precipitate-free Mg-0.1Sn anode with a homogeneous equal-axis grain structure was developed and rolled successfully at 573 K.Electrochemical test results indicate that the Mg-0.1Sn alloy exhibits enhanced anode dissolution kinetics.A Mg-air battery prepared using this anode exhibits a cell voltage of 1.626 V at 0.5 mA/cm^(2),reasonable anodic efficiency of 58.17%,and good specific energy of 1730.96 mW·h/g at 10 mA/cm^(2).This performance is attributed to the effective reactive anode surface,the suppressed chunk effect,and weak self-corrosion owing to the homogeneous basal texture.展开更多
In recent decades,control performance monitoring(CPM)has experienced remarkable progress in research and industrial applications.While CPM research has been investigated using various benchmarks,the historical data be...In recent decades,control performance monitoring(CPM)has experienced remarkable progress in research and industrial applications.While CPM research has been investigated using various benchmarks,the historical data benchmark(HIS)has garnered the most attention due to its practicality and effectiveness.However,existing CPM reviews usually focus on the theoretical benchmark,and there is a lack of an in-depth review that thoroughly explores HIS-based methods.In this article,a comprehensive overview of HIS-based CPM is provided.First,we provide a novel static-dynamic perspective on data-level manifestations of control performance underlying typical controller capacities including regulation and servo:static and dynamic properties.The static property portrays time-independent variability in system output,and the dynamic property describes temporal behavior driven by closed-loop feedback.Accordingly,existing HIS-based CPM approaches and their intrinsic motivations are classified and analyzed from these two perspectives.Specifically,two mainstream solutions for CPM methods are summarized,including static analysis and dynamic analysis,which match data-driven techniques with actual controlling behavior.Furthermore,this paper also points out various opportunities and challenges faced in CPM for modern industry and provides promising directions in the context of artificial intelligence for inspiring future research.展开更多
Excellent detonation performances and low sensitivity are prerequisites for the deployment of energetic materials.Exploring the underlying factors that affect impact sensitivity and detonation performances as well as ...Excellent detonation performances and low sensitivity are prerequisites for the deployment of energetic materials.Exploring the underlying factors that affect impact sensitivity and detonation performances as well as exploring how to obtain materials with desired properties remains a long-term challenge.Machine learning with its ability to solve complex tasks and perform robust data processing can reveal the relationship between performance and descriptive indicators,potentially accelerating the development process of energetic materials.In this background,impact sensitivity,detonation performances,and 28 physicochemical parameters for 222 energetic materials from density functional theory calculations and published literature were sorted out.Four machine learning algorithms were employed to predict various properties of energetic materials,including impact sensitivity,detonation velocity,detonation pressure,and Gurney energy.Analysis of Pearson coefficients and feature importance showed that the heat of explosion,oxygen balance,decomposition products,and HOMO energy levels have a strong correlation with the impact sensitivity of energetic materials.Oxygen balance,decomposition products,and density have a strong correlation with detonation performances.Utilizing impact sensitivity of 2,3,4-trinitrotoluene and the detonation performances of 2,4,6-trinitrobenzene-1,3,5-triamine as the benchmark,the analysis of feature importance rankings and statistical data revealed the optimal range of key features balancing impact sensitivity and detonation performances:oxygen balance values should be between-40%and-30%,density should range from 1.66 to 1.72 g/cm^(3),HOMO energy levels should be between-6.34 and-6.31 eV,and lipophilicity should be between-1.0 and 0.1,4.49 and 5.59.These findings not only offer important insights into the impact sensitivity and detonation performances of energetic materials,but also provide a theoretical guidance paradigm for the design and development of new energetic materials with optimal detonation performances and reduced sensitivity.展开更多
La-Mg-Ni-based hydrogen storage alloys with superlattice structures are the new generation anode material for nickel metal hydride(Ni-MH)batteries owing to the advantages of high capacity and exceptional activation pr...La-Mg-Ni-based hydrogen storage alloys with superlattice structures are the new generation anode material for nickel metal hydride(Ni-MH)batteries owing to the advantages of high capacity and exceptional activation properties.However,the cycling stability is not currently satisfactory enough which plagues its application.Herein,a strategy of partially substituting La with the Y element is proposed to boost the capacity durability of La-Mg-Ni-based alloys.Furthermore,phase structure regulation is implemented simultaneously to obtain the A5 B19-type alloy with good crystal stability specifically.It is found that Y promotes the phase formation of the Pr5 Co19-type phase after annealing at 985℃.The alloy containing Y contributes to the superior rate capability resulting from the promoted hydrogen diffusion rate.Notably,Y substitution enables strengthening the anti-pulverization ability of the alloy in terms of increasing the volume match between[A_(2)B_(4)]and[AB5]subunits,and effectively enhances the anti-corrosion ability of the alloy due to high electronegativity,realizing improved long-term cycling stability of the alloy from 74.2%to 78.5%after cycling 300 times.The work is expected to shed light on the composition and structure design of the La-Mg-Ni-based hydrogen storage alloy for Ni-MH batteries.展开更多
Background Sow longevity and reproductivity are essential in the modern swine industry.Although many studies have focused on the genetic and genomic factors for selection,little is known about the associations between...Background Sow longevity and reproductivity are essential in the modern swine industry.Although many studies have focused on the genetic and genomic factors for selection,little is known about the associations between the microbiome and sows with longevity in reproduction.Results In this study,we collected and sequenced rectal and vaginal swabs from 48 sows,nine of which completed up to four parities(U4P group),exhibiting reproductive longevity.We first identified predictors of sow longevity in the rectum(e.g.,Akkermansia)and vagina(e.g.,Lactobacillus)of the U4P group using RandomForest in the early breeding stage of the first parity.Interestingly,these bacteria in the U4P group showed decreased predicted KEGG gene abundance involved in the biosynthesis of amino acids.Then,we tracked the longitudinal changes of the micro-biome over four parities in the U4P sows.LEfSe analysis revealed parity-associated bacteria that existed in both the rectum and vagina(e.g.,Streptococcus in Parity 1,Lactobacillus in Parity 2,Veillonella in Parity 4).We also identi-fied patterns of bacterial change between the early breeding stage(d 0)and d 110,such as Streptococcus,which was decreased in all four parties.Furthermore,sows in the U4P group with longevity potential also showed better reproductive performance.Finally,we discovered bacterial predictors(e.g.,Prevotellaceae NK3B31 group)for the total number of piglets born throughout the four parities in both the rectum and vagina.Conclusions This study highlights how the rectal and vaginal microbiome in sows with longevity in reproduc-tion changes within four parities.The identification of parity-associated,pregnancy-related,and reproductive performance-correlated bacteria provides the foundation for targeted microbiome modulation to improve animal production.展开更多
This study investigates the flexural performance of ultra-high performance concrete(UHPC)in reinforced concrete T-beams,focusing on the effects of interfacial treatments.Three concrete T-beam specimens were fabricated...This study investigates the flexural performance of ultra-high performance concrete(UHPC)in reinforced concrete T-beams,focusing on the effects of interfacial treatments.Three concrete T-beam specimens were fabricated and tested:a control beam(RC-T),a UHPC-reinforced beam with a chiseled interface(UN-C-50F),and a UHPC-reinforced beam featuring both a chiseled interface and anchored steel rebars(UN-CS-50F).The test results indicated that both chiseling and the incorporation of anchored rebars effectively created a synergistic combination between the concrete T-beam and the UHPC reinforcement layer,with the UN-CS-50F exhibiting the highest flexural resistance.The cracking load and ultimate load of UN-CS-50F were 221.5%and 40.8%,respectively,higher than those of the RC-T.Finite element(FE)models were developed to provide further insights into the behavior of the UHPCreinforced T-beams,showing a maximumdeviation of just 8%when validated against experimental data.A parametric analysis varied the height,thickness,andmaterial strength of the UHPC reinforcement layer based on the validated FE model,revealing that increasing the UHPC layer thickness from 30 to 50 mm improved the ultimate resistance by 20%while reducing the UHPC reinforcement height from 440 to 300 mm led to a 10%decrease in bending resistance.The interfacial anchoring rebars significantly reduced crack propagation and enhanced stress redistribution,highlighting the importance of strengthening interfacial bonds and optimizing geometric parameters ofUHPCfor improved T-beam performance.These findings offer valuable insights for the design and retrofitting of UHPC-reinforced bridge girders.展开更多
Solid-state batteries are widely recognized as the next-generation energy storage devices with high specific energy,high safety,and high environmental adaptability.However,the research and development of solid-state b...Solid-state batteries are widely recognized as the next-generation energy storage devices with high specific energy,high safety,and high environmental adaptability.However,the research and development of solid-state batteries are resource-intensive and time-consuming due to their complex chemical environment,rendering performance prediction arduous and delaying large-scale industrialization.Artificial intelligence serves as an accelerator for solid-state battery development by enabling efficient material screening and performance prediction.This review will systematically examine how the latest progress in using machine learning(ML)algorithms can be used to mine extensive material databases and accelerate the discovery of high-performance cathode,anode,and electrolyte materials suitable for solid-state batteries.Furthermore,the use of ML technology to accurately estimate and predict key performance indicators in the solid-state battery management system will be discussed,among which are state of charge,state of health,remaining useful life,and battery capacity.Finally,we will summarize the main challenges encountered in the current research,such as data quality issues and poor code portability,and propose possible solutions and development paths.These will provide clear guidance for future research and technological reiteration.展开更多
High-entropy materials(HEMs),an innovative class of materials with complex stoichiometry,have recently garnered consider-able attention in energy storage applications.While their multi-element compositions(five or mor...High-entropy materials(HEMs),an innovative class of materials with complex stoichiometry,have recently garnered consider-able attention in energy storage applications.While their multi-element compositions(five or more principal elements in nearly equiatom-ic proportions)confer unique advantages such as high configurational entropy,lattice distortion,and synergistic cocktail effects,the fun-damental understanding of structure-property relationships in battery systems remains fragmented across existing studies.This review ad-dresses critical research gaps by proposing a multidimensional design paradigm that systematically integrates synergistic mechanisms spanning cathodes,anodes,electrolytes,and electrocatalysts.We provide an in-depth analysis of HEMs’thermodynamic/kinetic stabiliza-tion principles and structure-regulated electrochemical properties,integrating and establishing quantitative correlations between entropy-driven phase stability and charge transport dynamics.By summarizing the performance benchmarking results of lithium/sodium/potassi-um-ion battery components,we reveal how entropy-mediated structural tailoring enhances cycle stability and ionic conductivity.Notably,we pioneer the systematic association of high-entropy effects to electrochemical interfaces,demonstrating their unique potential in stabil-izing solid-electrolyte interphases and suppressing transition metal dissolution.Emerging opportunities in machine learning-driven com-position screening and sustainable manufacturing are discussed alongside critical challenges,including performance variability metrics and cost-benefit analysis for industrial implementation.This work provides both fundamental insights and practical guidelines for advan-cing HEMs toward next-generation battery technologies.展开更多
Surface engineering plays a crucial role in improving the performance of high energy materials,and polydopamine(PDA)is widely used in the field of energetic materials for surface modification and functionalization.In ...Surface engineering plays a crucial role in improving the performance of high energy materials,and polydopamine(PDA)is widely used in the field of energetic materials for surface modification and functionalization.In order to obtain high-quality HMX@PDA-based PBX explosives with high sphericity and a narrow particle size distribution,composite microspheres were prepared using co-axial droplet microfluidic technology.The formation mechanism,thermal behavior,mechanical sensitivity,electrostatic spark sensitivity,compressive strength,and combustion performance of the microspheres were investigated.The results show that PDA can effectively enhance the interfacial interaction between the explosive particles and the binder under the synergistic effect of chemical bonds and the physical"mechanical interlocking"structure.Interface reinforcement causes the thermal decomposition temperature of the sample microspheres to move to a higher temperature,with the sensitivity to impact,friction,and electrostatic sparks(for S-1)increasing by 12.5%,31.3%,and 81.5%respectively,and the compressive strength also increased by 30.7%,effectively enhancing the safety performance of the microspheres.Therefore,this study provides an effective and universal strategy for preparing high-quality functional explosives,and also provides some reference for the safe use of energetic materials in practical applications.展开更多
文摘In the dynamic landscape of software technologies,the demand for sophisticated applications across diverse industries is ever⁃increasing.However,predicting software defects remains a crucial challenge for ensuring the resilience and dependability of software systems.This study presents a novel software defect prediction technique that significantly enhances performance through a hybrid machine learning approach.The innovative methodology integrates a Genetic Algorithm(GA)for precise feature selection,a Decision Tree(DT)for robust classification,and leverages the capabilities of Particle Swarm Optimization(PSO)and Ant Colony Optimization(ACO)algorithms for precision⁃driven optimization.The utilization of datasets from varied sources enriches the predictive prowess of our model.Of particular significance in our pursuit is the unwavering focus on enhancing the prediction process through a highly refined PSO⁃ACO algorithm,thereby optimizing the efficiency and effectiveness of the GA⁃DT hybrid model.The thorough evaluation of our proposed approach unfolds across seven software projects,unveiling a paradigm shift in performance metrics.Results unequivocally demonstrate that the GA⁃DT with PSO⁃ACO algorithm surpasses its counterparts,showcasing unparalleled accuracy and reliability.Furthermore,our hybrid approach demonstrates outstanding performance in terms of F⁃measure,with an impressive increase rate of 78%.
文摘Teacher–student relationships play a vital role in improving college students’academic performance and the quality of higher education.However,empirical studies with substantial data-driven insights remain limited.To address this gap,this study collected 3278 questionnaires from seven universities across four provinces in China to analyze the key factors affecting college students’academic performance.A machine learning framework,CQFOA-KELM,was developed by enhancing the Fruit Fly Optimization Algorithm(FOA)with Covariance Matrix Adaptation Evolution Strategy(CMAES)and Quadratic Approximation(QA).CQFOA significantly improved population diversity and was validated on the IEEE CEC2017 benchmark functions.The CQFOA-KELM model achieved an accuracy of 98.15%and a sensitivity of 98.53%in predicting college students’academic performance.Additionally,it effectively identified the key factors influencing academic performance through the feature selection process.
基金the National Natural Science Foundation of China(No.52004179)the Natural Nat-ural Science Foundation of Guangxi Province,China(No.2020GXNSFAA159015)Shanxi Water and Wood New Carbon Materials Technology Co.,Ltd.,China,and Shanxi Wote Haimer New Materials Technology Co.,Ltd,China.
文摘The development of anode materials with high rate capability and long charge-discharge plateau is the key to improve per-formance of lithium-ion capacitors(LICs).Herein,the porous graphitic carbon(PGC-1300)derived from a new triply interpenetrated co-balt metal-organic framework(Co-MOF)was prepared through the facile and robust carbonization at 1300°C and washing by HCl solu-tion.The as-prepared PGC-1300 featured an optimized graphitization degree and porous framework,which not only contributes to high plateau capacity(105.0 mAh·g^(−1)below 0.2 V at 0.05 A·g^(−1)),but also supplies more convenient pathways for ions and increases the rate capability(128.5 mAh·g^(−1)at 3.2 A·g^(−1)).According to the kinetics analyses,it can be found that diffusion regulated surface induced capa-citive process and Li-ions intercalation process are coexisted for lithium-ion storage.Additionally,LIC PGC-1300//AC constructed with pre-lithiated PGC-1300 anode and activated carbon(AC)cathode exhibited an increased energy density of 102.8 Wh·kg^(−1),a power dens-ity of 6017.1 W·kg^(−1),together with the excellent cyclic stability(91.6%retention after 10000 cycles at 1.0 A·g^(−1)).
基金supported by the National Natural Science Foundation of China [grant numbers 42088101 and 42375048]。
文摘Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many areas.In this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,China.The two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction results.In addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.
文摘Rectifying circuit,as a crucial component for converting alternating current into direct current,plays a pivotal role in energy harvesting microsystems.Traditional silicon-based or germanium-based rectifier diodes hinder system integration due to their specific manufacturing processes.Conversely,metal oxide diodes,with their simple fabrication techniques,offer advantages for system integration.The oxygen vacancy defect of oxide semiconductor will greatly affect the electrical performance of the device,so the performance of the diode can be effectively controlled by adjusting the oxygen vacancy concentration.This study centers on optimizing the performance of diodes by modulating the oxygen vacancy concentration within InGaZnO films through control of oxygen flows during the sputtering process.Experimental results demonstrate that the diode exhibits a forward current density of 43.82 A·cm^(−2),with a rectification ratio of 6.94×10^(4),efficiently rectifying input sine signals with 1 kHz frequency and 5 V magnitude.These results demonstrate its potential in energy conversion and management.By adjusting the oxygen vacancy,a methodology is provided for optimizing the performance of rectifying diodes.
文摘The 3D reconstruction pipeline uses the Bundle Adjustment algorithm to refine the camera and point parameters. The Bundle Adjustment algorithm is a compute-intensive algorithm, and many researchers have improved its performance by implementing the algorithm on GPUs. In the previous research work, “Improving Accuracy and Computational Burden of Bundle Adjustment Algorithm using GPUs,” the authors demonstrated first the Bundle Adjustment algorithmic performance improvement by reducing the mean square error using an additional radial distorting parameter and explicitly computed analytical derivatives and reducing the computational burden of the Bundle Adjustment algorithm using GPUs. The naïve implementation of the CUDA code, a speedup of 10× for the largest dataset of 13,678 cameras, 4,455,747 points, and 28,975,571 projections was achieved. In this paper, we present the optimization of the Bundle Adjustment algorithm CUDA code on GPUs to achieve higher speedup. We propose a new data memory layout for the parameters in the Bundle Adjustment algorithm, resulting in contiguous memory access. We demonstrate that it improves the memory throughput on the GPUs, thereby improving the overall performance. We also demonstrate an increase in the computational throughput of the algorithm by optimizing the CUDA kernels to utilize the GPU resources effectively. A comparative performance study of explicitly computing an algorithm parameter versus using the Jacobians instead is presented. In the previous work, the Bundle Adjustment algorithm failed to converge for certain datasets due to several block matrices of the cameras in the augmented normal equation, resulting in rank-deficient matrices. In this work, we identify the cameras that cause rank-deficient matrices and preprocess the datasets to ensure the convergence of the BA algorithm. Our optimized CUDA implementation achieves convergence of the Bundle Adjustment algorithm in around 22 seconds for the largest dataset compared to 654 seconds for the sequential implementation, resulting in a speedup of 30×. Our optimized CUDA implementation presented in this paper has achieved a 3× speedup for the largest dataset compared to the previous naïve CUDA implementation.
基金the Center of Lithium Battery Membrane Materials jointly established by School of Chemistry and Chemical Engineering of Huazhong University of Science and Technology and Shenzhen Senior Technology Material Co.Ltd.,the National Natural Science Foundation of China(52020105012,52303084)the Young Scientists Fund of Natural Science Foundation of Hubei Province(2023AFB220)for the support of this work.
文摘The growing demands for energy storage systems,electric vehicles,and portable electronics have significantly pushed forward the need for safe and reliable lithium batteries.It is essential to design functional separators with improved mechanical and electrochemical characteristics.This review covers the improved mechanical and electrochemical performances as well as the advancements made in the design of separators utilizing a variety of techniques.In terms of electrolyte wettability and adhesion of the coating materials,we provide an overview of the current status of research on coated separators,in situ modified separators,and grafting modified separators,and elaborate additional performance parameters of interest.The characteristics of inorganics coated separators,organic framework coated separators and inorganic-organic coated separators from different fabrication methods are compared.Future directions regarding new modified materials,manufacturing process,quantitative analysis of adhesion and so on are proposed toward next-generation advanced lithium batteries.
文摘The available test methods for optimal moisture content of cold recycled mixture(CRM)as well as its bulk specific gravity,and theoretical maximum relative density were analyzed in this work.Some test improvements were suggested to improve test control of the CRM road performance based on the discovered flaws.Besides,the properties of reclaimed asphalt pavement(RAP),including the content of old asphalt,penetration index,passing rate of 4.75 mm sieve,and gradation change rate after extraction,were examined.The effects of RAP characteristics on splitting tensile strength,water stability,the high-and low-temperature performance of emulsified asphalt CRM were studied.The results show that the optimum moisture content of CRM should be determined when the compaction work matches the specimen’s molding work.Among the analyzed methods of bulk specific gravity assessment,the dry-surface and CoreLok methods provide more robust and accurate results than the wax-sealing method,while the dry-surface method is the most cost-efficient.The modified theoretical maximum relative density test method is proposed,which can reduce the systematic error of the vacuum test method.The following RAP-CRM trends can be observed.The lower the content of old asphalt and the smaller the change rate of gradation,the smaller the voids and the better the water stability of CRM.The greater the penetration of old asphalt,the higher the fracture work and low-temperature splitting strength.The greater the penetration,the higher the passing rate of 4.75 mm sieve after extraction,and the worse the high-temperature performance of CRM.
基金National Natural Science Foundation of China(52261032,51861021,51661016)Science and Technology Plan of Gansu Province(21YF5GA074)+2 种基金Public Welfare Project of Zhejiang Natural Science Foundation(LGG22E010008)Wenzhou Basic Public Welfare Scientific Research Project(G2023020)Incubation Program of Excellent Doctoral Dissertation-Lanzhou University of Technology。
文摘The development of high-performance structural and functional materials is vital in many industrial fields.High-and medium-entropy alloys(H/MEAs)with superior comprehensive properties owing to their specific microstructures are promising candidates for structural materials.More importantly,multitudinous efforts have been made to regulate the microstructures and the properties of H/MEAs to further expand their industrial applications.The various heterostructures have enormous potential for the development of H/MEAs with outstanding performance.Herein,multiple heterogeneous structures with single and hierarchical heterogeneities were discussed in detail.Moreover,preparation methods for compositional inhomogeneity,bimodal structures,dualphase structures,lamella/layered structures,harmonic structures(core-shell),multiscale precipitates and heterostructures coupled with specific microstructures in H/MEAs were also systematically reviewed.The deformation mechanisms induced by the different heterostructures were thoroughly discussed to explore the relationship between the heterostructures and the optimized properties of H/MEAs.The contributions of the heterostructures and advanced microstructures to the H/MEAs were comprehensively elucidated to further improve the properties of the alloys.Finally,this review discussed the future challenges of high-performance H/MEAs for industrial applications and provides feasible methods for optimizing heterostructures to enhance the comprehensive properties of H/MEAs.
基金supported by the National Natural Science Foundation of China(Project No.5217232152102391)+2 种基金Sichuan Province Science and Technology Innovation Talent Project(2024JDRC0020)China Shenhua Energy Company Limited Technology Project(GJNY-22-7/2300-K1220053)Key science and technology projects in the transportation industry of the Ministry of Transport(2022-ZD7-132).
文摘This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization balance.By integrating surrogate models to approximate the objective functions,SMOGWO significantly improves the efficiency and accuracy of the optimization process.The effectiveness of this approach is evaluated using the CEC2009 multi-objective test function suite,where SMOGWO achieves a superiority rate of 76.67%compared to other leading multi-objective algorithms.Furthermore,the practical applicability of SMOGWO is demonstrated through a case study on empty and heavy train allocation,which validates its ability to balance line capacity,minimize transportation costs,and optimize the technical combination of heavy trains.The research highlights SMOGWO's potential as a robust solution for optimization challenges in railway transportation,offering valuable contributions toward enhancing operational efficiency and promoting sustainable development in the sector.
基金National Natural Science Foundation of China,Grant/Award Number:52374153。
文摘Rock fragmentation is an important indicator for assessing the quality of blasting operations.However,accurate prediction of rock fragmentation after blasting is challenging due to the complicated blasting parameters and rock properties.For this reason,optimized by the Bayesian optimization algorithm(BOA),four hybrid machine learning models,including random forest,adaptive boosting,gradient boosting,and extremely randomized trees,were developed in this study.A total of 102 data sets with seven input parameters(spacing-to-burden ratio,hole depth-to-burden ratio,burden-to-hole diameter ratio,stemming length-to-burden ratio,powder factor,in situ block size,and elastic modulus)and one output parameter(rock fragment mean size,X_(50))were adopted to train and validate the predictive models.The root mean square error(RMSE),the mean absolute error(MAE),and the coefficient of determination(R^(2))were used as the evaluation metrics.The evaluation results demonstrated that the hybrid models showed superior performance than the standalone models.The hybrid model consisting of gradient boosting and BOA(GBoost-BOA)achieved the best prediction results compared with the other hybrid models,with the highest R^(2)value of 0.96 and the smallest values of RMSE and MAE of 0.03 and 0.02,respectively.Furthermore,sensitivity analysis was carried out to study the effects of input variables on rock fragmentation.In situ block size(XB),elastic modulus(E),and stemming length-to-burden ratio(T/B)were set as the main influencing factors.The proposed hybrid model provided a reliable prediction result and thus could be considered an alternative approach for rock fragment prediction in mining engineering.
基金partially supported by the National Natural Science Foundation of China(No.51901153)Shanxi Scholarship Council of China(No.2019032)+1 种基金the Natural Science Foundation of Shanxi,China(No.202103021224049)the Shanxi Zhejiang University New Materials and Chemical Research Institute Scientific Research Project,China(No.2022SX-TD025)。
文摘A novel precipitate-free Mg-0.1Sn anode with a homogeneous equal-axis grain structure was developed and rolled successfully at 573 K.Electrochemical test results indicate that the Mg-0.1Sn alloy exhibits enhanced anode dissolution kinetics.A Mg-air battery prepared using this anode exhibits a cell voltage of 1.626 V at 0.5 mA/cm^(2),reasonable anodic efficiency of 58.17%,and good specific energy of 1730.96 mW·h/g at 10 mA/cm^(2).This performance is attributed to the effective reactive anode surface,the suppressed chunk effect,and weak self-corrosion owing to the homogeneous basal texture.
基金supported in part by the National Natural Science Foundation of China(62125306)Zhejiang Key Research and Development Project(2024C01163)the State Key Laboratory of Industrial Control Technology,China(ICT2024A06)
文摘In recent decades,control performance monitoring(CPM)has experienced remarkable progress in research and industrial applications.While CPM research has been investigated using various benchmarks,the historical data benchmark(HIS)has garnered the most attention due to its practicality and effectiveness.However,existing CPM reviews usually focus on the theoretical benchmark,and there is a lack of an in-depth review that thoroughly explores HIS-based methods.In this article,a comprehensive overview of HIS-based CPM is provided.First,we provide a novel static-dynamic perspective on data-level manifestations of control performance underlying typical controller capacities including regulation and servo:static and dynamic properties.The static property portrays time-independent variability in system output,and the dynamic property describes temporal behavior driven by closed-loop feedback.Accordingly,existing HIS-based CPM approaches and their intrinsic motivations are classified and analyzed from these two perspectives.Specifically,two mainstream solutions for CPM methods are summarized,including static analysis and dynamic analysis,which match data-driven techniques with actual controlling behavior.Furthermore,this paper also points out various opportunities and challenges faced in CPM for modern industry and provides promising directions in the context of artificial intelligence for inspiring future research.
基金supported by the Fundamental Research Funds for the Central Universities(Grant No.2682024GF019)。
文摘Excellent detonation performances and low sensitivity are prerequisites for the deployment of energetic materials.Exploring the underlying factors that affect impact sensitivity and detonation performances as well as exploring how to obtain materials with desired properties remains a long-term challenge.Machine learning with its ability to solve complex tasks and perform robust data processing can reveal the relationship between performance and descriptive indicators,potentially accelerating the development process of energetic materials.In this background,impact sensitivity,detonation performances,and 28 physicochemical parameters for 222 energetic materials from density functional theory calculations and published literature were sorted out.Four machine learning algorithms were employed to predict various properties of energetic materials,including impact sensitivity,detonation velocity,detonation pressure,and Gurney energy.Analysis of Pearson coefficients and feature importance showed that the heat of explosion,oxygen balance,decomposition products,and HOMO energy levels have a strong correlation with the impact sensitivity of energetic materials.Oxygen balance,decomposition products,and density have a strong correlation with detonation performances.Utilizing impact sensitivity of 2,3,4-trinitrotoluene and the detonation performances of 2,4,6-trinitrobenzene-1,3,5-triamine as the benchmark,the analysis of feature importance rankings and statistical data revealed the optimal range of key features balancing impact sensitivity and detonation performances:oxygen balance values should be between-40%and-30%,density should range from 1.66 to 1.72 g/cm^(3),HOMO energy levels should be between-6.34 and-6.31 eV,and lipophilicity should be between-1.0 and 0.1,4.49 and 5.59.These findings not only offer important insights into the impact sensitivity and detonation performances of energetic materials,but also provide a theoretical guidance paradigm for the design and development of new energetic materials with optimal detonation performances and reduced sensitivity.
基金the financial support by the National Nat-ural Science Foundation of China(Nos.52201282,52071281,52371239)the China Postdoctoral Science Foundation(No.2023M742945)+4 种基金Hebei Provincial Postdoctoral Science Foundation(No.B2023003023)the Science Research Project of Hebei Education Department(No.BJK2022033)the Natural Science Foundation of Hebei Province(No.C2022203003)the Inner Mongolia Science and Technology Major Project(No.2020ZD0012)the Baotou Science and Technology Planning Project(No.XM2022BT09).
文摘La-Mg-Ni-based hydrogen storage alloys with superlattice structures are the new generation anode material for nickel metal hydride(Ni-MH)batteries owing to the advantages of high capacity and exceptional activation properties.However,the cycling stability is not currently satisfactory enough which plagues its application.Herein,a strategy of partially substituting La with the Y element is proposed to boost the capacity durability of La-Mg-Ni-based alloys.Furthermore,phase structure regulation is implemented simultaneously to obtain the A5 B19-type alloy with good crystal stability specifically.It is found that Y promotes the phase formation of the Pr5 Co19-type phase after annealing at 985℃.The alloy containing Y contributes to the superior rate capability resulting from the promoted hydrogen diffusion rate.Notably,Y substitution enables strengthening the anti-pulverization ability of the alloy in terms of increasing the volume match between[A_(2)B_(4)]and[AB5]subunits,and effectively enhances the anti-corrosion ability of the alloy due to high electronegativity,realizing improved long-term cycling stability of the alloy from 74.2%to 78.5%after cycling 300 times.The work is expected to shed light on the composition and structure design of the La-Mg-Ni-based hydrogen storage alloy for Ni-MH batteries.
基金funded by the National Key Research and Development Program of China (2023YFE0124400)the Specific university discipline construction project (2023B10564001)+1 种基金grants administered by the Arkansas Biosciences Institute and the USDAa core grant (P20GM121293, proteogenomics core)。
文摘Background Sow longevity and reproductivity are essential in the modern swine industry.Although many studies have focused on the genetic and genomic factors for selection,little is known about the associations between the microbiome and sows with longevity in reproduction.Results In this study,we collected and sequenced rectal and vaginal swabs from 48 sows,nine of which completed up to four parities(U4P group),exhibiting reproductive longevity.We first identified predictors of sow longevity in the rectum(e.g.,Akkermansia)and vagina(e.g.,Lactobacillus)of the U4P group using RandomForest in the early breeding stage of the first parity.Interestingly,these bacteria in the U4P group showed decreased predicted KEGG gene abundance involved in the biosynthesis of amino acids.Then,we tracked the longitudinal changes of the micro-biome over four parities in the U4P sows.LEfSe analysis revealed parity-associated bacteria that existed in both the rectum and vagina(e.g.,Streptococcus in Parity 1,Lactobacillus in Parity 2,Veillonella in Parity 4).We also identi-fied patterns of bacterial change between the early breeding stage(d 0)and d 110,such as Streptococcus,which was decreased in all four parties.Furthermore,sows in the U4P group with longevity potential also showed better reproductive performance.Finally,we discovered bacterial predictors(e.g.,Prevotellaceae NK3B31 group)for the total number of piglets born throughout the four parities in both the rectum and vagina.Conclusions This study highlights how the rectal and vaginal microbiome in sows with longevity in reproduc-tion changes within four parities.The identification of parity-associated,pregnancy-related,and reproductive performance-correlated bacteria provides the foundation for targeted microbiome modulation to improve animal production.
基金The National Natural Science Foundation of China(Grant#52278161)the Science and Technology Project of Guangzhou(Grant#2024A04J9888)the Guangdong Basic and Applied Basic Research Foundation(Grant#2023A1515010535).
文摘This study investigates the flexural performance of ultra-high performance concrete(UHPC)in reinforced concrete T-beams,focusing on the effects of interfacial treatments.Three concrete T-beam specimens were fabricated and tested:a control beam(RC-T),a UHPC-reinforced beam with a chiseled interface(UN-C-50F),and a UHPC-reinforced beam featuring both a chiseled interface and anchored steel rebars(UN-CS-50F).The test results indicated that both chiseling and the incorporation of anchored rebars effectively created a synergistic combination between the concrete T-beam and the UHPC reinforcement layer,with the UN-CS-50F exhibiting the highest flexural resistance.The cracking load and ultimate load of UN-CS-50F were 221.5%and 40.8%,respectively,higher than those of the RC-T.Finite element(FE)models were developed to provide further insights into the behavior of the UHPCreinforced T-beams,showing a maximumdeviation of just 8%when validated against experimental data.A parametric analysis varied the height,thickness,andmaterial strength of the UHPC reinforcement layer based on the validated FE model,revealing that increasing the UHPC layer thickness from 30 to 50 mm improved the ultimate resistance by 20%while reducing the UHPC reinforcement height from 440 to 300 mm led to a 10%decrease in bending resistance.The interfacial anchoring rebars significantly reduced crack propagation and enhanced stress redistribution,highlighting the importance of strengthening interfacial bonds and optimizing geometric parameters ofUHPCfor improved T-beam performance.These findings offer valuable insights for the design and retrofitting of UHPC-reinforced bridge girders.
基金the National Key Research Program of China under granted No.92164201National Natural Science Foundation of China for Distinguished Young Scholars No.62325403+2 种基金Natural Science Foundation of Jiangsu Province(BK20230498)Jiangsu Funding Program for Excellent Postdoctoral Talent(2024ZB427)the National Natural Science Foundation of China(62304147).
文摘Solid-state batteries are widely recognized as the next-generation energy storage devices with high specific energy,high safety,and high environmental adaptability.However,the research and development of solid-state batteries are resource-intensive and time-consuming due to their complex chemical environment,rendering performance prediction arduous and delaying large-scale industrialization.Artificial intelligence serves as an accelerator for solid-state battery development by enabling efficient material screening and performance prediction.This review will systematically examine how the latest progress in using machine learning(ML)algorithms can be used to mine extensive material databases and accelerate the discovery of high-performance cathode,anode,and electrolyte materials suitable for solid-state batteries.Furthermore,the use of ML technology to accurately estimate and predict key performance indicators in the solid-state battery management system will be discussed,among which are state of charge,state of health,remaining useful life,and battery capacity.Finally,we will summarize the main challenges encountered in the current research,such as data quality issues and poor code portability,and propose possible solutions and development paths.These will provide clear guidance for future research and technological reiteration.
基金supported by National Natural Science Foundation of China(No.5227130161).
文摘High-entropy materials(HEMs),an innovative class of materials with complex stoichiometry,have recently garnered consider-able attention in energy storage applications.While their multi-element compositions(five or more principal elements in nearly equiatom-ic proportions)confer unique advantages such as high configurational entropy,lattice distortion,and synergistic cocktail effects,the fun-damental understanding of structure-property relationships in battery systems remains fragmented across existing studies.This review ad-dresses critical research gaps by proposing a multidimensional design paradigm that systematically integrates synergistic mechanisms spanning cathodes,anodes,electrolytes,and electrocatalysts.We provide an in-depth analysis of HEMs’thermodynamic/kinetic stabiliza-tion principles and structure-regulated electrochemical properties,integrating and establishing quantitative correlations between entropy-driven phase stability and charge transport dynamics.By summarizing the performance benchmarking results of lithium/sodium/potassi-um-ion battery components,we reveal how entropy-mediated structural tailoring enhances cycle stability and ionic conductivity.Notably,we pioneer the systematic association of high-entropy effects to electrochemical interfaces,demonstrating their unique potential in stabil-izing solid-electrolyte interphases and suppressing transition metal dissolution.Emerging opportunities in machine learning-driven com-position screening and sustainable manufacturing are discussed alongside critical challenges,including performance variability metrics and cost-benefit analysis for industrial implementation.This work provides both fundamental insights and practical guidelines for advan-cing HEMs toward next-generation battery technologies.
基金supported by the National Natural Science Foundation of China(Grant No.22005275).
文摘Surface engineering plays a crucial role in improving the performance of high energy materials,and polydopamine(PDA)is widely used in the field of energetic materials for surface modification and functionalization.In order to obtain high-quality HMX@PDA-based PBX explosives with high sphericity and a narrow particle size distribution,composite microspheres were prepared using co-axial droplet microfluidic technology.The formation mechanism,thermal behavior,mechanical sensitivity,electrostatic spark sensitivity,compressive strength,and combustion performance of the microspheres were investigated.The results show that PDA can effectively enhance the interfacial interaction between the explosive particles and the binder under the synergistic effect of chemical bonds and the physical"mechanical interlocking"structure.Interface reinforcement causes the thermal decomposition temperature of the sample microspheres to move to a higher temperature,with the sensitivity to impact,friction,and electrostatic sparks(for S-1)increasing by 12.5%,31.3%,and 81.5%respectively,and the compressive strength also increased by 30.7%,effectively enhancing the safety performance of the microspheres.Therefore,this study provides an effective and universal strategy for preparing high-quality functional explosives,and also provides some reference for the safe use of energetic materials in practical applications.
