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.

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.

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.

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. 287 4）,usable time P7（ 0. 187 3）,annual average increment P8（ 0. 187 3）,edible fungi production suitability P1（ 0. 095 8） 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. 400 0,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. 100 0 ≤ Y 2. 400 0 were recommended as candidates for edible fungi production,while the 21 broadleaf species with the comprehensive evaluation score of less than 2. 100 0 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.

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 α and weight w1 .Accurate information of a certain emergency activity should be collected before making the decision.

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.

Materials Selection Method Combined with Different MADM Methods

Materials selection is a multiple attribute decision making(MADM)problem.A lot of MADM methods are applicable to materials selection,and it may produce considerable differences between the results of materials selection.But it is unknown which MADM method is better.So it is desirable to decide reasonable final result of materials selection in consideration of the individual results from different MADM methods.In this paper,materials selection method combined with different MADM methods is proposed.The method is based on final ranks of alternative materials,where the final ranks are determined from the ranks of the alternative materials using different MADM methods.This method is applied to select optimal magnesium alloy material for automobile wheels.This method may be widely used to select optimal material in engineering practice.

Adsorption of rubidium ion from aqueous solution by surface ion imprinted materials

A new type of rubidium ion-imprinted polymer has been synthesized by the surface-imprinting technique using methacrylic acid as the functional monomer,the rubidium ion as the template,methanol as the solvent,and silica as a carrier.Ethylene glycol dimethacrylate and 2,2-azobisisobutyronitrile were used as acrosslinker and an initiator,respectively.In addition,based on the macrocyclic effect of crown ethers,the 18-crown-6 ligand was introduced as a ligand to fix the template ions better.Scanning electron microscopy,zeta-potential analysis,Fourier transform infrared spectroscopy,thermogravimetric analysis,and X-ray photoelectron spectroscopy were performed to characterize the ion-imprinted polymer.The effects of the preparation and adsorption conditions on the adsorption performance of the rubidium ion-imprinted polymer were investigated.The results indicated that the rubidium ion-imprinted polymer has high selectivity and faster kinetics than other adsorbents,with an equilibrium adsorption capacity of 200.19 mg·g^(-1)at 298 K within 25 min.The sorption isotherm was well described by the Freundlich isotherm model,while the adsorption kinetics fitted the pseudo-second-order kinetic model.Consecutive adsorption-desorption experiments showed that the ion-imprinted polymer had good chemical stability and reusability.

Selective extraction and separation of Fe,Mn oxides and organic materials in river surficial sediments

In order to investigate the adsorption mechanism of trace metals to surficial sediments （SSs）, a selective extraction procedure was improved in the present work. The selective extraction procedure has been proved to selectively remove and separate Fe, Mn oxides and organic materials （OMs） in the non-residual fraction from the SSs collected in Songhua River, China. After screening different kinds of conventional extractants of Fe and Mn oxides and OMs used for separation of heavy metals in the soils and sediments, NH2OH .HCl （0.1 mol/L） ＋ HNO3 （0.1 mol/L）, （NH4）2C2O4 （0.2 mol/L） ＋ H2C2O4 （pH 3.0）, and 30% of H2O2 were respectively applied to selectively extract Mn oxides, Fe/Mn oxides and OMs. After the extraction treatments, the target components were removed with extraction efficiencies between 86.09%--3.36% for the hydroxylamine hydrochloride treatment, 80.63%- 101.09% for the oxalate solution extraction, and 94.76%-102.83% for the hydrogen peroxide digestion, respectively. The results indicate that this selective extraction technology was effective for the extraction and separation ofFe, Mn oxides and OMs in the SSs, and important for further mechanism study of trace metal adsorption onto SSs.