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.

A Novel Hybrid Optimization Algorithm for Materialized View Selection from Data Warehouse Environments

Responding to complex analytical queries in the data warehouse(DW)is one of the most challenging tasks that require prompt attention.The problem of materialized view(MV)selection relies on selecting the most optimal views that can respond to more queries simultaneously.This work introduces a combined approach in which the constraint handling process is combined with metaheuristics to select the most optimal subset of DW views from DWs.The proposed work initially refines the solution to enable a feasible selection of views using the ensemble constraint handling technique(ECHT).The constraints such as self-adaptive penalty,epsilon(ε)-parameter and stochastic ranking(SR)are considered for constraint handling.These two constraints helped the proposed model select the finest views that minimize the objective function.Further,a novel and effective combination of Ebola and coot optimization algorithms named hybrid Ebola with coot optimization(CHECO)is introduced to choose the optimal MVs.Ebola and Coot have recently introduced metaheuristics that identify the global optimal set of views from the given population.By combining these two algorithms,the proposed framework resulted in a highly optimized set of views with minimized costs.Several cost functions are described to enable the algorithm to choose the finest solution from the problem space.Finally,extensive evaluations are conducted to prove the performance of the proposed approach compared to existing algorithms.The proposed framework resulted in a view maintenance cost of 6,329,354,613,784,query processing cost of 3,522,857,483,566 and execution time of 226 s when analyzed using the TPC-H benchmark dataset.

Materials Selection of Thermoplastic Matrices of Natural Fibre Composites for Cyclist Helmet Using an Integration of DMAIC Approach in Six Sigma Method Together with Grey Relational Analysis Approach

Natural fibre reinforced polymer composite(NFRPC)materials are gaining popularity in the modern world due to their eco-friendliness,lightweight nature,life-cycle superiority,biodegradability,low cost,and noble mechanical properties.Due to the wide variety of materials available that have comparable attributes and satisfy the requirements of the product design specification,material selection has become a crucial component of design for engineers.This paper discusses the study’s findings in choosing the suitable thermoplastic matrices of Natural Fibre Composites for Cyclist Helmet utilising the DMAIC,and GRA approaches.The results are based on integrating two decision methods implemented utilising two distinct decision-making approaches:qualitative and quantitative.This study suggested thermoplastic polyethylene as a particularly ideal matrix in composite cyclist helmets during the selection process for the best thermoplastic matrices material using the 6σtechnique,with the decision based on the highest performance,the lightest weight,and the most environmentally friendly criteria.The DMAIC and GRA approach significantly influenced the material selection process by offering different tools for each phase.In the future study,selection technique may have been more exhaustive if more information from other factors had been added.

Enhanced microwave absorption property of silver decorated biomass ordered porous carbon composite materials with frequency selective surface incorporation

Porous carbon(PC)is a promising electromagnetic(EM)wave absorbing material thanks to its light weight,large specific surface area as well as good dissipating capacity.To further improve its microwave absorbing performance,silver coated porous carbon(Ag@PC)is synthesized by one-step hydro-thermal synthesis process making use of fir as a biomass formwork.Phase compositions,morphological structure,and microwave absorption capability of the Ag@PC has been explored.Research results show that the metallic Ag was successfully reduced and the particles are evenly distributed inward the pores of the carbon formwork,which accelerates graphitization process of the amorphous carbon.The Ag@PC composite without adding polyvinyl pyrrolidone(PVP)exhibits higher dielectric constant and better EM wave dissipating capability.This is because the larger particles of Ag give rise to higher electric conductivity.After combing with frequency selective surface(FSS),the EM wave absorbing performance is further improved and the frequency region below-10 d B is located in8.20-11.75 GHz,and the minimal reflection loss value is-22.5 dB.This work indicates that incorporating metallic Ag particles and FSS provides a valid way to strengthen EM wave absorbing capacity of PC material.

Physical and numerical investigations of target stratum selection for ground hydraulic fracturing of multiple hard roofs

Ground hydraulic fracturing plays a crucial role in controlling the far-field hard roof,making it imperative to identify the most suitable target stratum for effective control.Physical experiments are conducted based on engineering properties to simulate the gradual collapse of the roof during longwall top coal caving(LTCC).A numerical model is established using the material point method(MPM)and the strain-softening damage constitutive model according to the structure of the physical model.Numerical simulations are conducted to analyze the LTCC process under different hard roofs for ground hydraulic fracturing.The results show that ground hydraulic fracturing releases the energy and stress of the target stratum,resulting in a substantial lag in the fracturing of the overburden before collapse occurs in the hydraulic fracturing stratum.Ground hydraulic fracturing of a low hard roof reduces the lag effect of hydraulic fractures,dissipates the energy consumed by the fracture of the hard roof,and reduces the abutment stress.Therefore,it is advisable to prioritize the selection of the lower hard roof as the target stratum.

Optimizing design of lattice materials based on finite element simulation

The optimized design of simple cross-truss and column lattice structures was carried out by the SolidWorks simulation module.The effective density of the structure was calculated according to the weight reduction requirements proposed by the project.Then,the vari-ation curve between the maximum bearing stress of the unit structure and the structural variables was obtained by simulation.Meanwhile,the mathematical equation between the maximum bearing stress and the structural variables could be obtained through MATLAB fitting.The results indicated that with the decrease in the number of cells,the compressive strength of the prepared column lattice increased(400 to 4 cells,compressive strength 29 MPa to 160 MPa).However,the yield strength increased with the number of cells.The compression strength of the simple cross-truss lattice samples indicated an increase trend with the decrease of the pillar size(an increase of the number of units),reaching 91 MPa(pillar diameter 0.52 mm,number of units 25).While the yield strength increased with the increasing of the number of units.In addition,the additive manufacturing processes of simple cubic lattice and simple cross-pillar lattice were investigated using selective laser melting.The compression performance obtained from the experiment is compared with the simulation results,which are in good agreement.The results of this paper can provide an important reference for optimizing design of lattice materials.

Experimental Study of Microalgae Cultivation under Selective Illumination by Ag/CoSO_(4)for Bioelectrode Materials Preparation

Microalgae biomass is an ideal precursor to prepare renewable carbon materials,which has broad application.The bioaccumulation efficiency(lipids,proteins,carbohydrates)and biomass productivity of microalgae are influenced by spectroscopy during the culture process.In this study,a bilayer plate-type photobioreactor was designed to cultivate Chlorella protothecoides with spectral selectivity by nanofluids.Compared to culture without spectral selectivity,the spectral selectivity of Ag/CoSO_(4)nanofluids increased microalgae biomass by 5.76%,and the spectral selectivity of CoSO_(4)solution increased by 17.14%.In addition,the spectral selectivity of Ag/CoSO_(4)nanofluids was more conducive to the accumulation of nutrients(29.46%lipids,50.66%proteins,and 17.86%carbohydrates)in microalgae.Further cultured chlorella was utilized to prepare bioelectrode materials,it was found that algal based biochar had a good pore structure(micro specific surface area:1627.5314 m^(2)/g,average pore size:0.21294 nm).As the current density was 1 A/g,the specific capacitance reached 230 F/g,appearing good electrochemical performance.

Decision making tools for optimal material selection:A review

The present work reviews different decision making tools(material comparing and choosing tools)used for selecting the best material considering different parameters.In this review work,the authors have tried to address the following important enquiries:1)the engineering applications addressed by the different material choosing and ranking methods;2)the predominantly used decision making tools addressing the optimal material selection for the engineering applications;3)merits and demerits of decision making tools used;4)the dominantly used criteria or objectives considered while selecting a suitable alternative material;5)overview of DEA on material selection field.The authors have surveyed literatures from different regions of the globe and considered literatures since 1988.The present review not only stresses the importance of material selection in the early design stage of the product development but also aids the design and material engineers to apply different decision making tools systematically.

ARTIFICIAL NEURAL NETWORKS BASED GEARS MATERIAL SELECTION HYBRID INTELLIGENT SYSTEM

An artificial neural networks(ANNs) based gear material selection hybrid intelligent system is established by analyzing the individual advantages and weakness of expert system (ES) and ANNs and the applications in material select of them. The system mainly consists of tow parts: ES and ANNs. By being trained with much data samples, the back propagation (BP) ANN gets the knowledge of gear materials selection, and is able to inference according to user input. The system realizes the complementing of ANNs and ES. Using this system, engineers without materials selection experience can conveniently deal with gear materials selection.

A Multi-Criteria Decision Support System for the Selection of Low-Cost Green Building Materials and Components

The necessity of having an effective computer-aided decision support system in the housing construction industry is rapidly growing alongside the demand for green buildings and green building products. Identifying and defining financially viable low-cost green building materials and components, just like selecting them, is a crucial exercise in subjectivity. With so many variables to consider, the task of evaluating such products can be complex and discouraging. Moreover, the existing mode for selecting and managing, often very large information associated with their impacts constrains decision-makers to perform a trade-off analysis that does not necessarily guarantee the most environmentally preferable material. This paper introduces the development of a multi-criteria decision support system (DSS) aimed at improving the understanding of the principles of best practices associated with the impacts of low-cost green building materials and components. The DSS presented in this paper is to provide designers with useful and explicit information that will aid informed decision-making in their choice of materials for low-cost green residential housing projects. The prototype MSDSS is developed using macro-in-excel, which is a fairly recent database management technique used for integrating data from multiple, often very large databases and other information sources. This model consists of a database to store different types of low-cost green materials with their corresponding attributes and performance characteristics. The DSS design is illustrated with particular emphasis on the development of the material selection data schema, and application of the Analytical Hierarchy Process (AHP) concept to a material selection problem. Details of the MSDSS model are also discussed including workflow of the data evaluation process. The prototype model has been developed with inputs elicited from domain experts and extensive literature review, and refined with feedback obtained from selected expert builder and developer companies. This paper further demonstrates the application of the prototype MSDSS for selecting the most appropriate low-cost green building material from among a list of several available options, and finally concludes the study with the associated potential benefits of the model to research and practice.

A Comparative Study on Several Anti-Corrosion Materials for Power FGD System

In the study organic-inorganic hybrid composite, epoxy modified silicone coating and vinyl ester flake mastic were comparatively used as several anti-corrosion materials that provided protection for flue gas desulfu-rization (FGD). The relationship between curing conversion rate of hybrid polymer and temperature was investigated by differential scanning calorimeter (DSC). The adhesion strength, coefficient of thermal expansion and flame retardant properties of three anti-corrosion materials were measured and analyzed. A corrosion test in 8% H2SO4 and 5% HCl at temperature cycle of 40°C~ 160°C was applied to study corrosion resistance of several anti-corrosion materials. Gravimetric measurement and morphological observation of three materials before and after corrosion test were comparatively analyzed in the paper. The small weight change and good morphological structure of hybrid composite during corrosion test demonstrate that hybrid composite has better anti-corrosion properties than epoxy modified silicone coating and vinyl ester flake mastic.

Site selection of emergency material warehouse under fuzzy environment

The objective of this work was to determine the location of emergency material warehouses. For the site selection problem of emergency material warehouses, the triangular fuzzy numbers are respectively demand of the demand node, the distance between the warehouse and demand node and the cost of the warehouse, a bi-objective programming model was established with minimum total cost of the system and minimum distance between the selected emergency material warehouses and the demand node. Using the theories of fuzzy numbers, the fuzzy programming model was transformed into a determinate bi-objective mixed integer programming model and a heuristic algorithm for this model was designed. Then, the algorithm was proven to be feasible and effective through a numerical example. Analysis results show that the location of emergency material warehouse depends heavily on the values of degree a and weight wl. Accurate information of a certain emergency activity should be collected before making the decision.

Laser-induced breakdown spectroscopy for the classification of wood materials using machine learning methods combined with feature selection

In this paper,we explore whether a feature selection method can improve model performance by using some classical machine learning models,artificial neural network,k-nearest neighbor,partial least squares-discrimination analysis,random forest,and support vector machine(SVM),combined with the feature selection methods,distance correlation coefficient(DCC),important weight of linear discriminant analysis(IW-LDA),and Relief-F algorithms,to discriminate eight species of wood(African rosewood,Brazilian bubinga,elm,larch,Myanmar padauk,Pterocarpus erinaceus,poplar,and sycamore)based on the laser-induced breakdown spectroscopy(LIBS)technique.The spectral data are normalized by the maximum of line intensity and principal component analysis is applied to the exploratory data analysis.The feature spectral lines are selected out based on the important weight assessed by DCC,IW-LDA,and Relief-F.All models are built by using the different number of feature lines(sorted by their important weight)as input.The relationship between the number of feature lines and the correct classification rate(CCR)of the model is analyzed.The CCRs of all models are improved by using a suitable feature selection.The highest CCR achieves(98.55...0.39)%when the SVM model is established from 86 feature lines selected by the IW-LDA method.The result demonstrates that a suitable feature selection method can improve model recognition ability and reduce modeling time in the application of wood materials classification using LIBS.

Optimal Selection of Raw Material Forest Species for Edible Fungi in Guizhou Province

A comprehensive evaluation system,which focused on optimal selection of raw material forest species for edible fungi,was established by combination of Analytic Hierarchy Process(AHP)and Experts Grading Method(EGM).The evaluation system had 4 indices of grade I and 12 indices of grade II.Among the 12 indices of grade II,the weighted values of production quality of edible fungi P2(0.2874),usable time P7(0.1873),annual average increment P8(0.1873),edible fungi production suitability P1(0.0958)were larger than the values of others.Based on the comprehensive evaluation system,this study analyzed and screened 47 broadleaf species of 40 genera of 25 families.There were 16 broadleaf species having the comprehensive evaluation scores of equal to or greater than2.4000,which were available as major tree species for edible fungi development of Guizhou Province,especially species such as Liriodendron chinense,Quercus acutissima,Alnus cremastogyne,Betula luminfera,Elaeocarpus duclouxii,Elaeocarpus sylvestris,Choerospondias axillaris.The 10 broadleaf tree species with comprehensive evaluation score of 2.1000≤Y 2.4000 were recommended as candidates for edible fungi production,while the 21 broadleaf species with the comprehensive evaluation score of less than 2.1000 were not recommended.

Critical materials selection for 700℃clean and high-efficiency coal-fired boiler

One of the pathways for achieving the goal of increasing the efficiency of coal-fired power plants and reducing the emission of pollutants and CO_2 is by improving the steam temperature and pressure.But the increase of steam parameters depend on the development of new high-temperature metal materials.This paper presented the constructive suggestions on critical materials selection for China's new generation of 700℃clean and high-efficiency coal-fired boiler by summarizing the research and development situation of critical materials for coal-fired boilers operating at 700℃/35 MPa of the Europe,Japan and USA.

The Selection and Application of Anticorrosive Materials for Flue Gas Desulfurization (FGD) System in Cement Plant

This paper combined with the actual case of flue gas desulfurization (FGD) system in cement Plant, analyzes the corrosion environment in each area of the FGD system, and selects appropriate anticorrosive materials for different corrosion environment, so as to provide operating experience and reference for the safe, stable and efficient operation of the FGD system.

Concurrent Material Selection of Natural Fibre Filament for Fused Deposition Modeling Using Integration of Analytic Hierarchy Process/Analytic Network Process

The employment of natural fibres in fused deposition modeling has raised much attention from researchers in finding a suitable formulation for the natural fibre composite filaments.Moreover,selection of suitable natural fibres for fused deposition modeling should be performed before the development of the composites.It could not be performed without identifying selection criteria that comprehend both materials and fused deposition modeling process requirements.Therefore,in this study,integration of the Analytic Hierarchy Process(AHP)/Analytic Network Process(ANP)has been introduced in selecting the natural fibres based in different clusters of selection concurrently.The selection process has been performed based on the interdependency among the selection criteria.Pairwise comparison matrices are constructed based on AHP’s hierarchical model and super matrices are constructed based on the ANP’s network model.As a result,flax fibre has ranked at the top of the selection by scored 19.5%from the overall evaluation.Flax fibre has excellent material properties and been found in various natural fibre composite applications.Further investigation is needed to study the compatibility of this fibre to be reinforced with a thermoplastic polymer matrix to develop a resultant natural fibre composite filament for fused deposition modeling.

Material Selection of a Natural Fibre Reinforced Polymer Composites using an Analytical Approach

Material selection has become a critical part of design for engineers,due to availability of diverse choice of materials that have similar properties and meet the product design specification.Implementation of statistical analysis alone makes it difficult to identify the ideal composition of the final composite.An integrated approach between statistical model and micromechanical model is desired.In this paper,resultant natural fibre and polymer matrix from previous study is used to estimate the mechanical properties such as density,Young’s modulus and tensile strength.Four levels of fibre loading are used to compare the optimum natural fibre reinforced polymer composite(NFRPC).The result from this analytical approach revealed that kenaf/polystyrene(PS)with 40%fibre loading is the ideal composite in automotive component application.It was found that the ideal composite score is 1.156 g/cm^(3),24.2 GPa and 413.4 MPa for density,Young’s modulus and tensile strength,respectively.A suggestion to increase the properties on Young’s modulus are also presented.This work proves that the statistical model is well incorporated with the analytical approach to choose the correct composite to use in automotive application.

Sine Trigonometry Operational Laws for Complex Neutrosophic Sets and Their Aggregation Operators in Material Selection

In this paper,sine trigonometry operational laws(ST-OLs)have been extended to neutrosophic sets(NSs)and the operations and functionality of these laws are studied.Then,extending these ST-OLs to complex neutrosophic sets(CNSs)forms the core of thiswork.Some of themathematical properties are proved based on ST-OLs.Fundamental operations and the distance measures between complex neutrosophic numbers(CNNs)based on the ST-OLs are discussed with numerical illustrations.Further the arithmetic and geometric aggregation operators are established and their properties are verified with numerical data.The general properties of the developed sine trigonometry weighted averaging/geometric aggregation operators for CNNs(ST-WAAO-CNN&ST-WGAO-CNN)are proved.A decision making technique based on these operators has been developed with the help of unsupervised criteria weighting approach called Entropy-ST-OLs-CNDM(complex neutrosophic decision making)method.A case study for material selection has been chosen to demonstrate the ST-OLs of CNDM method.To check the validity of the proposed method,entropy based complex neutrosophic CODAS approach with ST-OLs has been executed numerically and a comparative analysis with the discussion of their outcomes has been conducted.The proposed approach proves to be salient and effective for decision making with complex information.

Materialized Views Selection Problem in Decision Supporting Systems: Issues and Challenges

The data warehouse is the most widely used database structure in many decision support systems around the world. This is the reason why a lot of research has been conducted in the literature over the last two decades on their design, refreshment and optimization. The manipulation of hypercubes (cubes) of data is a frequently used operation in the design of multidimensional data warehouses, due to their better adaptation to OLAP (On-Line Analytical Processing). However, the updating of these hypercubes is a very complicated process due mainly to the mass and complexity of the data presented. The purpose of this paper is to present the state of the art of works based on multidimensional modeling using the hypercube as a unit of presentation of data stores. It starts with the base of this process which is the choice of the views (cubes) forming our data warehouse base. The objective of this work is to describe the state of the art of research works dealing with the selection of materialized views in decision support systems.