Porous framework materials for energy&environment relevant applications:A systematic review

Carbon peaking and carbon neutralization trigger a technical revolution in energy&environment related fields.Development of new technologies for green energy production and storage,industrial energy saving and efficiency reinforcement,carbon capture,and pollutant gas treatment is in highly imperious demand.The emerging porous framework materials such as metal–organic frameworks(MOFs),covalent organic frameworks(COFs)and hydrogen-bonded organic frameworks(HOFs),owing to the permanent porosity,tremendous specific surface area,designable structure and customizable functionality,have shown great potential in major energy-consuming industrial processes,including sustainable energy gas catalytic conversion,energy-efficient industrial gas separation and storage.Herein,this manuscript presents a systematic review of porous framework materials for global and comprehensive energy&environment related applications,from a macroscopic and application perspective.

Enhanced entropy generation and heat transfer characteristics of magnetic nano-encapsulated phase change materials in latent heat thermal energy storage systems

The objective of the current study is to investigate the importance of entropy generation and thermal radiation on the patterns of velocity,isentropic lines,and temperature contours within a thermal energy storage device filled with magnetic nanoencapsulated phase change materials(NEPCMs).The versatile finite element method(FEM)is implemented to numerically solve the governing equations.The effects of various parameters,including the viscosity parameter,ranging from 1 to 3,the thermal conductivity parameter,ranging from 1 to 3,the Rayleigh parameter,ranging from 102 to 3×10^(2),the radiation number,ranging from 0.1 to 0.5,the fusion temperature,ranging from 1.0 to 1.2,the volume fraction of NEPCMs,ranging from 2%to 6%,the Stefan number,ranging from 1 to 5,the magnetic number,ranging from 0.1 to 0.5,and the irreversibility parameter,ranging from 0.1 to 0.5,are examined in detail on the temperature contours,isentropic lines,heat capacity ratio,and velocity fields.Furthermore,the heat transfer rates at both the cold and hot walls are analyzed,and the findings are presented graphically.The results indicate that the time taken by the NEPCMs to transition from solid to liquid is prolonged inside the chamber region as the fusion temperatureθf increases.Additionally,the contours of the heat capacity ratio Cr decrease with the increase in the Stefan number Ste.

Sound Transmission Loss Analysis of a Double Plate-Acoustic Cavity Coupling System with In-Plane Functionally Graded Materials

In this paper, the isogeometric analysis (IGA) is employed to develop an acoustic radiation model for a double plate-acoustic cavity coupling system, with a focus on analyzing the sound transmission loss (STL). The functionally graded (FG) plate exhibits a different material properties in-plane, and the power-law rule is adopted as the governing principle for material mixing. To validate the harmonic response and demonstrate the accuracy and convergence of the isogeometric modeling, ANASYS is utilized to compare with numerical examples. A plane wave serves as the acoustic excitation, and the Rayleigh integral is applied to discretize the radiated plate. The STL results are compared with the literature, confirming the reliability of the coupling system. Finally, the investigation is conducted to study impact of cavity depth and power-law parameter on the STL.

Radiofrequency sensing systems based on emerging two-dimensional materials and devices

As one of the most promising platforms for wireless communication,radiofrequency(RF)electronics have been widely advocated for the development of sensing systems.In particular,monolayer and few-layer two-dimensional(2D)materials exhibiting extraordinary electrical properties not only can be integrated to improve the performance of RF circuits,but also to display exceptional sensing capabilities.This review provides an in-depth perspective of current trends and challenges in the application of 2D materials for RF biochemical sensing,including:(i)theoretical bases to achieve different sensing schemes;(ii)unique properties of 2D materials for reasoning their applications in RF sensing;(iii)developments in 2D RF sensors to facilitate the practice of biochemical sensors with ever-demanding sensitivities,as well as their potential uses in meeting the requirements and challenges of biochemical sensors in the Internet-of-Things era.

Materials,preparation,performances and mechanism of polyurethane modified asphalt and its mixture:A systematic review

With the rapid development of asphalt pavement technology,it has attracted considerable attention to improving the durability of asphalt pavement.An effective action is to use modified asphalt with high performance and durability.Polyurethane(PU)has been used in asphalt pavement engineering to enhance the durability and service life of asphalt pavement because of its excellent high-temperature performance,toughness,wear resistance,aging resistance and oil resistance.However,PU modified asphalt technology is still in the exploratory stage.The preparation,modification mechanism and working performances of PU modified asphalt need to be further clarified.Therefore,this paper summarized the research progress of PU modified asphalt and its mixture.The composition of PU modified asphalt was introduced.The addition methods of PU materials and preparation process parameters of the PU modified asphalt were determined.The modification mechanism of PU on asphalt was discussed.The effects of polyurethane on asphalt were analyzed and the road performances of its mixture were evaluated.Finally,the development tendency towards PU modified asphalt and its mixture were forecasted.

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.

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.

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.

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.

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。

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.

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.

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 machine learning in perovskite materials and devices:A review

Metal-halide hybrid perovskite materials are excellent candidates for solar cells and photoelectric devices.In recent years,machine learning(ML)techniques have developed rapidly in many fields and provided ideas for material discovery and design.ML can be applied to discover new materials quickly and effectively,with significant savings in resources and time compared with traditional experiments and density functional theory(DFT)calculations.In this review,we present the application of ML in per-ovskites and briefly review the recent works in the field of ML-assisted perovskite design.Firstly,the advantages of perovskites in solar cells and the merits of ML applied to perovskites are discussed.Secondly,the workflow of ML in perovskite design and some basic ML algorithms are introduced.Thirdly,the applications of ML in predicting various properties of perovskite materials and devices are reviewed.Finally,we propose some prospects for the future development of this field.The rapid devel-opment of ML technology will largely promote the process of materials science,and ML will become an increasingly popular method for predicting the target properties of materials and devices.

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.

MXenes: Versatile 2D materials with tailored surface chemistry and diverse applications

MXenes,the most recent addition to the 2D material family,have attracted significant attention owing to their distinctive characteristics,including high surface area,conductivity,surface characteristics,mechanical strength,etc.This review begins by presenting MXenes,providing insights into their structural characteristics,synthesis methods,and surface functional groups.The review covers a thorough analysis of MXene surface properties,including surface chemistry and termination group impacts.The properties of MXenes are influenced by their synthesis,which can be fluorine-based or fluorinedependent.Fluorine-based synthesis techniques involve etching with fluorine-based reagents,mainly including HF or LiF/HCl,while fluorine-free methods include electrochemical etching,chemical vapor deposition(CVD),alkaline etching,Lewis acid-based etching,etc.These techniques result in the emergence of functional groups such as-F,-O,-OH,-Cl,etc.on the MXenes surface,depending on the synthesis method used.Properties of MXenes,such as electrical conductivity,electronic properties,catalytic activity,magnetic properties,mechanical strength,and chemical and thermal stability,are examined,and the role of functional groups in determining these properties is explored.The review delves into the diverse applications of MXenes,encompassing supercapacitors,battery materials,hydrogen storage,fuel cells,electromagnetic interference(EMI) shielding,pollutant removal,water purification,flexible electronics,sensors,additive manufacturing,catalysis,biomedical and healthcare fields,etc.Finally,this article outlines the challenges and opportunities in the current and future development of MXenes research,addressing various aspects such as synthesis scalability,etching challenges,and multifunctionality,and exploring novel applications.The review concludes with future prospects and conclusions envisioning the impact of MXenes on future technologies and innovation.

Fast prototype and rapid construction of three-dimensional and multi-scaled pitcher for controlled drainage by systematic biomimicry

Biomimetic materials that use natural wisdom to solve practical problems are developing rapidly.The trend for systematic biomimicry is towards in-situ characterization of naturalcreatures with high spatial resolutions.Furthermore,rapid reconstruction of digital twin models with the same complex features as the prototype is indispensable.However,it faces bottlenecks and limits in fast characterization and fabrication,precise parameter optimization,geometricdeviations control,and quality prediction.To solve these challenges,here,we demonstrate astate-of-the-art method taking advantage of micro-computed tomography and three-dimensional printing for the fast characterization of the pitcher plant Nepenthes x ventrata and fabrication of its biomimetic model to obtain a superior drainage controller with multiscale structures withprecise surface morphology optimization and geometric deviation control.Thefilm-rupture-based drainage dynamic and mechanisms are characterized by x-ray and high-speed videography,which determines the crucial structures for unique directionaldrainage.Then the optimized artificial pitchers are further developed into sustained drainage devices with novel applications,such as detection,reaction,and smoke control.

Recent Progress in Improving Rate Performance of Cellulose-Derived Carbon Materials for Sodium-Ion Batteries

Cellulose-derived carbon is regarded as one of the most promising candidates for high-performance anode materials in sodium-ion batteries;however,its poor rate performance at higher current density remains a challenge to achieve high power density sodium-ion batteries.The present review comprehensively elucidates the structural characteristics of cellulose-based materials and cellulose-derived carbon materials,explores the limitations in enhancing rate performance arising from ion diffusion and electronic transfer at the level of cellulose-derived carbon materials,and proposes corresponding strategies to improve rate performance targeted at various precursors of cellulose-based materials.This review also presents an update on recent progress in cellulose-based materials and cellulose-derived carbon materials,with particular focuses on their molecular,crystalline,and aggregation structures.Furthermore,the relationship between storage sodium and rate performance the carbon materials is elucidated through theoretical calculations and characterization analyses.Finally,future perspectives regarding challenges and opportunities in the research field of cellulose-derived carbon anodes are briefly highlighted.

Unlocking the potential of ultra-thin two-dimensional antimony materials:Selective growth and carbon coating for efficient potassium-ion storage

Antimony-based anodes have attracted wide attention in potassium-ion batteries due to their high theoretical specific capacities(∼660 mA h g^(-1))and suitable voltage platforms.However,severe capacity fading caused by huge volume change and limited ion transportation hinders their practical applications.Recently,strategies for controlling the morphologies of Sb-based materials to improve the electrochemical performances have been proposed.Among these,the two-dimensional Sb(2D-Sb)materials present excellent properties due to shorted ion immigration paths and enhanced ion diffusion.Nevertheless,the synthetic methods are usually tedious,and even the mechanism of these strategies remains elusive,especially how to obtain large-scale 2D-Sb materials.Herein,a novel strategy to synthesize 2D-Sb material using a straightforward solvothermal method without the requirement of a complex nanostructure design is provided.This method leverages the selective adsorption of aldehyde groups in furfural to induce crystal growth,while concurrently reducing and coating a nitrogen-doped carbon layer.Compared to the reported methods,it is simpler,more efficient,and conducive to the production of composite nanosheets with uniform thickness(3–4 nm).The 2D-Sb@NC nanosheet anode delivers an extremely high capacity of 504.5 mA h g^(-1) at current densities of 100 mA g^(-1) and remains stable for more than 200 cycles.Through characterizations and molecular dynamic simulations,how potassium storage kinetics between 2D Sb-based materials and bulk Sb-based materials are explored,and detailed explanations are provided.These findings offer novel insights into the development of durable 2D alloy-based anodes for next-generation potassium-ion batteries.

Recent Advances in In-Memory Computing:Exploring Memristor and Memtransistor Arrays with 2D Materials

The conventional computing architecture faces substantial chal-lenges,including high latency and energy consumption between memory and processing units.In response,in-memory computing has emerged as a promising alternative architecture,enabling computing operations within memory arrays to overcome these limitations.Memristive devices have gained significant attention as key components for in-memory computing due to their high-density arrays,rapid response times,and ability to emulate biological synapses.Among these devices,two-dimensional(2D)material-based memristor and memtransistor arrays have emerged as particularly promising candidates for next-generation in-memory computing,thanks to their exceptional performance driven by the unique properties of 2D materials,such as layered structures,mechanical flexibility,and the capability to form heterojunctions.This review delves into the state-of-the-art research on 2D material-based memristive arrays,encompassing critical aspects such as material selection,device perfor-mance metrics,array structures,and potential applications.Furthermore,it provides a comprehensive overview of the current challenges and limitations associated with these arrays,along with potential solutions.The primary objective of this review is to serve as a significant milestone in realizing next-generation in-memory computing utilizing 2D materials and bridge the gap from single-device characterization to array-level and system-level implementations of neuromorphic computing,leveraging the potential of 2D material-based memristive devices.