SMC再生SBS沥青的流变性能和微观特性研究

【目的】探究新型温拌再生剂甲基苯乙烯共聚物(styreneic methyl copolymer,SMC)对老化苯乙烯系热塑性弹性体(styrene butadiene styrene,SBS)改性沥青的再生效果和再生机理。【方法】首先,在室内制备不同老化程度的沥青。然后,采用SMC再生剂对不同老化程度的SBS改性沥青进行再生。接着,对再生后的SBS改性沥青进行动态剪切流变和低温弯曲蠕变试验,以评价其流变性能。最后,开展红外光谱试验以揭示其作用机理,进行电镜扫描试验以验证SMC再生剂的再生效果。【结果】SMC再生剂会降低老化SBS改性沥青的车辙因子,降低老化SBS改性沥青的恢复率,同时会使老化SBS改性沥青的低温性能得到极大提升。SMC再生剂未与SBS改性沥青发生化学反应,两者仅为物理共混。同时,SMC再生剂能够弥补沥青因老化产生的裂缝。【结论】SMC再生剂对老化SBS改性沥青具有较好的修复效果,能够为沥青的再生提供一种新的途径。

基于多策略改进的SMC-GTO电机速度跟踪控制

针对传统控制算法在农业机器人驱动电机速度控制方面存在的响应时间长、跟踪效果差以及参数整定难度大,导致驱动电机转速难以在短时间内收敛至预期值的问题,提出一种基于多策略改进的SMC-GTO电机速度跟踪控制算法。首先,为了缩短驱动电机速度响应时间,提高速度跟踪效果,设计了一种新型趋近率的改进滑模控制器(I-SMC);其次,为了快速整定滑模控制器参数,引入了多策略改进的大猩猩部队优化算法(MIGTO)。仿真结果表明:I-SMC能够将电机运行过程中的动态误差累计与过冲控制在0 rad/s内,并且可以在0.4 s内响应至预定速度。MIGTO算法在开发和探索阶段均展现出卓越的性能,尤其在整定基于新型趋近率的改进滑模控制器参数工作中表现突出。该研究通过引入I-SMC和MIGTO算法,成功改进了驱动电机速度控制方式,有效缩短了响应时间,提升了速度跟踪效果。

智能汽车轨迹跟踪MPC-RBF-SMC协同控制策略研究

针对自动驾驶车辆行驶过程中模型失配以及外部环境干扰导致车辆轨迹跟踪环节精确性不高的问题,提出了一种结合车辆运动学模型预测控制(MPC)、径向基(RBF)神经网络和滑模控制(SMC)的轨迹跟踪控制策略。通过建立车辆运动学MPC模型计算当前状态车辆期望横摆角速度,并将其与实际横摆角速度的偏差输入RBF-SMC控制器,利用RBF快速逼近非线性模型的特点,结合滑模控制输出前轮转角,实现车辆的横向轨迹跟踪控制。仿真结果表明,与传统的控制器相比,该方法轨迹跟踪精度显著提高,并在不同行驶工况下表现出较好的鲁棒性。

Recent advances in transition metal phosphide materials:Synthesis and applications in supercapacitors

Supercapacitors(SCs)are considered promising energy storge systems because of their outstanding power density,fast charge and discharge rate and long-term cycling stability.The exploitation of cheap and efficient electrode materials is the key to improve the performance of supercapacitors.As the battery-type materials,transition metal phosphides(TMPs)possess high theoretical specific capacity,good electrical conductivity and superior structural stability,which have been extensively studied to be electrode materials for supercapacitors.In this review,we summarize the up-to-date progress on TMPs materials from diversified synthetic methods,diverse nanostructures and several prominent TMPs and their composites in application of supercapacitors.In the end,we also propose the remaining challenges toward the rational discovery and synthesis of high-performance TMP electrodes materials for energy storage.

人工智能背景下Materials Project数据库在计算材料学课程教学中的应用

该文探讨了在人工智能背景下,Materials Project数据库在计算材料学课程教学中的应用和影响。Materials Project数据库是一个集成了AI和大数据技术的开放获取的材料库,能为学生提供海量的材料晶体结构和物性数据,使教学内容更为丰富,让学生能通过亲自操作获取和分析数据,深入理解微观结构与物性之间的关系。这一新兴的教学模式不仅提升了学生的科研能力和创新思维能力,还有助于培养具备计算材料专业知识和多学科交叉的复合型人才。总体来说,人工智能时代下,大数据的引入为计算材料学课程带来新的活力,并对未来教育改革和实践产生了积极影响。

Material point method simulation of hydro-mechanical behaviour in twophase porous geomaterials: A state-of-the-art review

The material point method(MPM)has been gaining increasing popularity as an appropriate approach to the solution of coupled hydro-mechanical problems involving large deformation.In this paper,we survey the current state-of-the-art in the MPM simulation of hydro-mechanical behaviour in two-phase porous geomaterials.The review covers the recent advances and developments in the MPM and their extensions to capture the coupled hydro-mechanical problems involving large deformations.The focus of this review is aiming at providing a clear picture of what has or has not been developed or implemented for simulating two-phase coupled large deformation problems,which will provide some direct reference for both practitioners and researchers.

Valorization of Camellia oleifera oil processing byproducts to value-added chemicals and biobased materials: A critical review

The C.oleifera oil processing industry generates large amounts of solid wastes,including C.oleifera shell(COS)and C.oleifera cake(COC).Distinct from generally acknowledged lignocellulosic biomass(corn stover,bamboo,birch,etc.),Camellia wastes contain diverse bioactive substances in addition to the abundant lignocellulosic components,and thus,the biorefinery utilization of C.oleifera processing byproducts involves complicated processing technologies.This reviewfirst summarizes various technologies for extracting and converting the main components in C.oleifera oil processing byproducts into value-added chemicals and biobased materials,as well as their potential applications.Microwave,ultrasound,and Soxhlet extractions are compared for the extraction of functional bioactive components(tannin,flavonoid,saponin,etc.),while solvothermal conversion and pyrolysis are discussed for the conversion of lignocellulosic components into value-added chemicals.The application areas of these chemicals according to their properties are introduced in detail,including utilizing antioxidant and anti-in-flammatory properties of the bioactive substances for the specific application,as well as drop-in chemicals for the substitution of unrenewable fossil fuel-derived products.In addition to chemical production,biochar fabricated from COS and its applications in thefields of adsorption,supercapacitor,soil remediation and wood composites are comprehensively reviewed and discussed.Finally,based on the compositions and structural characteristics of C.oleifera byproducts,the development of full-component valorization strategies and the expansion of the appli-cationfields are proposed.

Multi-layer perceptron-based data-driven multiscale modelling of granular materials with a novel Frobenius norm-based internal variable

One objective of developing machine learning(ML)-based material models is to integrate them with well-established numerical methods to solve boundary value problems(BVPs).In the family of ML models,recurrent neural networks(RNNs)have been extensively applied to capture history-dependent constitutive responses of granular materials,but these multiple-step-based neural networks are neither sufficiently efficient nor aligned with the standard finite element method(FEM).Single-step-based neural networks like the multi-layer perceptron(MLP)are an alternative to bypass the above issues but have to introduce some internal variables to encode complex loading histories.In this work,one novel Frobenius norm-based internal variable,together with the Fourier layer and residual architectureenhanced MLP model,is crafted to replicate the history-dependent constitutive features of representative volume element(RVE)for granular materials.The obtained ML models are then seamlessly embedded into the FEM to solve the BVP of a biaxial compression case and a rigid strip footing case.The obtained solutions are comparable to results from the FEM-DEM multiscale modelling but achieve significantly improved efficiency.The results demonstrate the applicability of the proposed internal variable in enabling MLP to capture highly nonlinear constitutive responses of granular materials.

基于SMCS模型的高强螺栓及节点火灾全过程断裂性能模拟

高强螺栓广泛应用于钢结构节点连接,火灾高温会影响其基本材性和断裂行为,从而影响螺栓节点抗火性能甚至整体结构抗倒塌性能。基于10.9级高强螺栓火灾全过程(升温段、降温段、火灾后)单轴拉伸试验结果,结合有限元模拟,对不同温度历程和应力三轴度对应的螺栓SMCS断裂模型进行校准,并与螺栓材性试验和T-stub节点试验结果对比验证;对T-stub节点火灾全过程断裂行为进行参数分析,研究损伤准则和温度历程对节点失效模式和变形特征的影响。结果表明:校准的SMCS模型能够有效、准确地预测螺栓和节点在火灾全过程和高应力三轴度(0.3~1.2)下的受拉断裂行为,适用预测误差在12%以内;拉伸温度和峰值温度是影响高强螺栓抗断能力的主要因素,螺栓抗断能力随温度升高而提高;不同温度历程下T-stub节点可能发生翼缘板屈服断裂、翼缘板和螺栓同时屈服断裂、螺栓屈服断裂三种失效模式,且节点的变形能力(延性系数)与失效模式有关,确定钢板母材和螺栓的断裂模型是准确预测节点失效模式的关键。

Emerging two-dimensional Mo-based materials for rechargeable metal-ion batteries:Advances and perspectives

With the rapid development of rechargeable metal-ion batteries(MIBs)with safety,stability and high energy density,significant efforts have been devoted to exploring high-performance electrode materials.In recent years,two-dimensional(2D)molybdenum-based(Mo-based)materials have drawn considerable attention due to their exceptional characteristics,including low cost,unique crystal structure,high theoretical capacity and controllable chemical compositions.However,like other transition metal compounds,Mo-based materials are facing thorny challenges to overcome,such as slow electron/ion transfer kinetics and substantial volume changes during the charge and discharge processes.In this review,we summarize the recent progress in developing emerging 2D Mo-based electrode materials for MIBs,encompassing oxides,sulfides,selenides,carbides.After introducing the crystal structure and common synthesis methods,this review sheds light on the charge storage mechanism of several 2D Mo-based materials by various advanced characterization techniques.The latest achievements in utilizing 2D Mo-based materials as electrode materials for various MIBs(including lithium-ion batteries(LIBs),sodium-ion batteries(SIBs)and zinc-ion batteries(ZIBs))are discussed in detail.Afterwards,the modulation strategies for enhancing the electrochemical performance of 2D Mo-based materials are highlighted,focusing on heteroatom doping,vacancies creation,composite coupling engineering and nanostructure design.Finally,we present the existing challenges and future research directions for 2D Mo-based materials to realize high-performance energy storage systems.

Field-assisted machining of difficult-to-machine materials

Difficult-to-machine materials (DMMs) are extensively applied in critical fields such as aviation,semiconductor,biomedicine,and other key fields due to their excellent material properties.However,traditional machining technologies often struggle to achieve ultra-precision with DMMs resulting from poor surface quality and low processing efficiency.In recent years,field-assisted machining (FAM) technology has emerged as a new generation of machining technology based on innovative principles such as laser heating,tool vibration,magnetic magnetization,and plasma modification,providing a new solution for improving the machinability of DMMs.This technology not only addresses these limitations of traditional machining methods,but also has become a hot topic of research in the domain of ultra-precision machining of DMMs.Many new methods and principles have been introduced and investigated one after another,yet few studies have presented a comprehensive analysis and summarization.To fill this gap and understand the development trend of FAM,this study provides an important overview of FAM,covering different assisted machining methods,application effects,mechanism analysis,and equipment design.The current deficiencies and future challenges of FAM are summarized to lay the foundation for the further development of multi-field hybrid assisted and intelligent FAM technologies.

Application of deep learning for informatics aided design of electrode materials in metal-ion batteries

To develop emerging electrode materials and improve the performances of batteries,the machine learning techniques can provide insights to discover,design and develop battery new materials in high-throughput way.In this paper,two deep learning models are developed and trained with two feature groups extracted from the Materials Project datasets to predict the battery electrochemical performances including average voltage,specific capacity and specific energy.The deep learning models are trained with the multilayer perceptron as the core.The Bayesian optimization and Monte Carlo methods are applied to improve the prediction accuracy of models.Based on 10 types of ion batteries,the correlation coefficients are maintained above 0.9 compared to DFT calculation results and the mean absolute error of the prediction results for voltages of two models can reach 0.41 V and 0.20 V,respectively.The electrochemical performance prediction times for the two trained models on thousands of batteries are only 72.9 ms and 75.7 ms.Besides,the two deep learning models are applied to approach the screening of emerging electrode materials for sodium-ion and potassium-ion batteries.This work can contribute to a high-throughput computational method to accelerate the rational and fast materials discovery and design.

The DNA damage repair complex MoMMS21-MoSMC5 is required for infection-related development and pathogenicity of Magnaporthe oryzae

The conserved DNA damage repair complex,MMS21-SMC5/6(Methyl methane sulfonate 21-Structural maintenance of chromosomes 5/6),has been extensively studied in yeast,animals,and plants.However,its role in phytopathogenic fungi,particularly in the highly destructive rice blast fungus Magnaporthe oryzae,remains unknown.In this study,we functionally characterized the homologues of this complex,MoMMS21 and MoSMC5,in M.oryzae.We first demonstrated the importance of DNA damage repair in M.oryzae by showing that the DNA damage inducer phleomycin inhibited vegetative growth,infection-related development and pathogenicity in this fungus.Additionally,we discovered that MoMMS21 and MoSMC5 interacted in the nuclei,suggesting that they also function as a complex in M.oryzae.Gene deletion experiments revealed that both MoMMS21 and MoSMC5 are required for infection-related development and pathogenicity in M.oryzae,while only MoMMS21 deletion affected growth and sensitivity to phleomycin,indicating its specific involvement in DNA damage repair.Overall,our results provide insights into the roles of MoMMS21 and MoSMC5 in M.oryzae,highlighting their functions beyond DNA damage repair.

A thermodynamics-based three-scale constitutive model for partially saturated granular materials

A three-scale constitutive model for unsaturated granular materials based on thermodynamic theory is presented.The three-scale yield locus,derived from the explicit yield criterion for solid matrix,is developed from a series of discrete interparticle contact planes.The three-scale yield locus is sensitive to porosity changes;therefore,it is reinterpreted as a corresponding constitutive model without phenomenological parameters.Furthermore,a water retention curve is proposed based on special pore morphology and experimental observations.The features of the partially saturated granular materials are well captured by the model.Under wetting and isotropic compression,volumetric compaction occurs,and the degree of saturation increases.Moreover,the higher the matric suction,the greater the strength,and the smaller the volumetric compaction.Compared with the phenomenological Barcelona basic model,the proposed three-scale constitutive model has fewer parameters;virtually all parameters have clear physical meanings.

Interatomic Interaction Models for Magnetic Materials:Recent Advances

Atomistic modeling is a widely employed theoretical method of computational materials science.It has found particular utility in the study of magnetic materials.Initially,magnetic empirical interatomic potentials or spinpolarized density functional theory(DFT)served as the primary models for describing interatomic interactions in atomistic simulations of magnetic systems.Furthermore,in recent years,a new class of interatomic potentials known as magnetic machine-learning interatomic potentials(magnetic MLIPs)has emerged.These MLIPs combine the computational efficiency,in terms of CPU time,of empirical potentials with the accuracy of DFT calculations.In this review,our focus lies on providing a comprehensive summary of the interatomic interaction models developed specifically for investigating magnetic materials.We also delve into the various problem classes to which these models can be applied.Finally,we offer insights into the future prospects of interatomic interaction model development for the exploration of magnetic materials.

Research progress of permanent ferrite magnet materials

Permanent ferrite magnet materials are extensively employed due to their exceptional magnetic properties and cost-effectiveness.The fast development in electromobile and household appliance industries contributes to a new progress in permanent ferrite materials.This paper reviews the deveolpement and progress of permanent ferrite magnet industry in recent years.The emergence of new raw material,the advancement of perparation methods and manufacturing techniques,and the potential applications of permanent ferrite materials are introduced and discussed.Specifically,nanocrystallization plays a crucial role in achieving high performance at a low cost and reducing reliance on rare earth resources,and therefore it could be a promising development trendency.

Progress on two-dimensional ferrovalley materials

The electron's charge and spin degrees of freedom are at the core of modern electronic devices. With the in-depth investigation of two-dimensional materials, another degree of freedom, valley, has also attracted tremendous research interest. The intrinsic spontaneous valley polarization in two-dimensional magnetic systems, ferrovalley material, provides convenience for detecting and modulating the valley. In this review, we first introduce the development of valleytronics.Then, the valley polarization forms by the p-, d-, and f-orbit that are discussed. Following, we discuss the investigation progress of modulating the valley polarization of two-dimensional ferrovalley materials by multiple physical fields, such as electric, stacking mode, strain, and interface. Finally, we look forward to the future developments of valleytronics.

MXene-based hybrid materials for electrochemical and photoelectrochemical H_(2) generation

The conversion of solar energy to produce clean hydrogen fuel through water splitting is an emerging strategy for efficiently storing solar energy in the form of solar fuel.This aligns with the increasing global demand for the development of an ideal energy alternative to fossil fuels that does not emit greenhouse gases.Electrochemical(EC) and photoelectrochemical(PEC) water splitting technologies have garnered significant attention worldwide for advanced hydrogen solar fuel production in recent decades.To achieve sustainable green H_(2) production,it is essential to create efficient catalyst materials that are low-cost and can replace expensive noble metal-based catalysts.These characteristics make them an ideal catalyst material for the process.Two-dimensional MXenes with M_(n+1)X_(n) structure have been identified as a promising option for EC and PEC water splitting due to their superior hydrophilicity,metal-like conductivity,large surface area,and adjustable surface chemistry.Here,we present a summary of recent advancements in the synthesis and performance enhancement methods for MXene hybrid materials in hydrogen production through EC and PEC water splitting.Furthermore,we examine the challenges and insights associated with the rational design of MXene-based hybrid materials to facilitate efficient water splitting for sustainable solar fuel production.

The Roadmap of 2D Materials and Devices Toward Chips

Due to the constraints imposed by physical effects and performance degra certain limitations in sustaining the advancement of Moore’s law.Two-dimensional(2D)materials have emerged as highly promising candidates for the post-Moore era,offering significant potential in domains such as integrated circuits and next-generation computing.Here,in this review,the progress of 2D semiconductors in process engineering and various electronic applications are summarized.A careful introduction of material synthesis,transistor engineering focused on device configuration,dielectric engineering,contact engineering,and material integration are given first.Then 2D transistors for certain electronic applications including digital and analog circuits,heterogeneous integration chips,and sensing circuits are discussed.Moreover,several promising applications(artificial intelligence chips and quantum chips)based on specific mechanism devices are introduced.Finally,the challenges for 2D materials encountered in achieving circuit-level or system-level applications are analyzed,and potential development pathways or roadmaps are further speculated and outlooked.

Gypsum-based Silica Gel Humidity-controlling Composite Materials:Preparation,Characterization,and Performance

Gypsum was used as substrate,and silica gel was mixed into substrate at a certain mass ratio to prepare humidity-controlling composites;moreover,the moisture absorption and desorption properties of gypsum-based composites were compared with adding different silica gel particle size and proportion.The morphological characteristics,the isothermal equilibrium moisture content curve,moisture absorption and desorption rate,moisture absorption and desorption stability,and humidity-conditioning performance were tested and analyzed.The experimental results show that,compared with pure-gypsum,the surface structure of the gypsum-based composites is relatively loose,the quantity,density and aperture of the pores in the structure increase.The absorption and desorption capacity increase along with the increase of silica gel particle size and silica gel proportion.When 3 mm silica gel particle size is added with a mass ratio of 40%,the maximum equilibrium moisture content of humidity-controlling composites is 0.161 g/g at 98% relative humidity(RH),3.22 times that of pure-gypsum.The moisture absorption and desorption rates are increased,the equilibrium moisture absorption and desorption rates are 2.68 times and 1.61 times that of pure-gypsum at 58.5% RH,respectively.The gypsum-based composites have a good stability,which has better timely response to dynamic humidity changes and can effectively regulate indoor humidity under natural conditions.