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.

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.

Highly Efficient Broadband Achromatic Microlens Design Based on Low-Dispersion Materials

Metalenses with achromatic performance offer a new opportunity for high-quality imaging with an ultracompact configuration;however,they suffer from complex fabrication processes and low focusing efficiency.In this study,we propose an efficient design method for achromatic microlenses on a wavelength scale using materials with low dispersion,an adequately designed convex surface,and a thickness profile distribution.By taking into account the absolute chromatic aberration,relative focal length shift(FLS),and numerical aperture(NA),microlens with a certain focal length can be realized through our realized map of geometric features.Accordingly,the designed achromatic microlenses with low-dispersion fused silica were fabricated using a focused ion beam,and precise surface profiles were obtained.The fabricated microlenses exhibited a high average focusing efficiency of 65%at visible wavelengths of 410-680 nm and excellent achromatic capability via white light imaging.Moreover,the design exhibited the advantages of being polarization-insensitive and near-diffraction-limited.These results demonstrate the effectiveness of our proposed achromatic microlens design approach,which expands the prospects of miniaturized optics such as virtual and augmented reality,ultracompact microscopes,and biological endoscopy.

Application of Energy-Saving Materials in Architectural Design

The conventional process of building construction is associated with issues such as the waste of construction materials and environmental pollution.Sustainable development highlights the importance of energy conservation and eco-friendly practices.It is essential to use energy-efficient and green materials in building designs to ensure the healthy growth of construction companies.This article discusses the advantages and principles of incorporating energy-saving materials in architectural design.It examines the strategies and critical control points for using energy-saving materials in architectural design,offering guidance for the sustainable development of the construction industry.

Recent advances in cobalt phosphide-based materials for electrocatalytic water splitting:From catalytic mechanism and synthesis method to optimization design

Electrochemical water splitting has long been considered an effective energy conversion technology for trans-ferring intermittent renewable electricity into hydrogen fuel,and the exploration of cost-effective and high-performance electrocatalysts is crucial in making electrolyzed water technology commercially viable.Cobalt phosphide(Co-P)has emerged as a catalyst of high potential owing to its high catalytic activity and durability in water splitting.This paper systematically reviews the latest advances in the development of Co-P-based materials for use in water splitting.The essential effects of P in enhancing the catalytic performance of the hydrogen evolution reaction and oxygen evolution reaction are first outlined.Then,versatile synthesis techniques for Co-P electrocatalysts are summarized,followed by advanced strategies to enhance the electrocatalytic performance of Co-P materials,including heteroatom doping,composite construction,integration with well-conductive sub-strates,and structure control from the viewpoint of experiment.Along with these optimization strategies,the understanding of the inherent mechanism of enhanced catalytic performance is also discussed.Finally,some existing challenges in the development of highly active and stable Co-P-based materials are clarified,and pro-spective directions for prompting the wide commercialization of water electrolysis technology are proposed.

Magnesium-based biomaterials for coordinated tissue repair:A comprehensive overview of design strategies,advantages,and challenges

Magnesium-based biomaterials(MBMs)are one of the most promising materials for tissue engineering due to their unique mechanical properties and excellent functional properties.This review describes the development,advantages,and challenges of MBMs for biomedical applications,especially for tissue repair and regeneration.The history of the use of MBMs from the beginning of the 20th century is traced,and the transformative advances in contemporary applications of MBMs in areas such as orthopedics and cardiovascular surgery are emphasized.The review also provides insight into the signaling pathways affected by MBMs,such as the PI3K/Akt and RANKL/RANK/OPG pathways,which are critical for osteogenesis and angiogenesis.The review advocates that future research should focus on optimizing alloy compositions,surface modification and exploring innovative technologies such as 3D printing to improve the efficacy of MBMs in complex tissue repair.The potential of MBMs to tissue engineering and regenerative medicine is significant,urging further exploration and interdisciplinary collaboration to maximize their therapeutic effects.

Reliability-based life-cycle cost seismic design optimization of coastal bridge piers with nonuniform corrosion using different materials

Reinforcement corrosion is the main cause of performance deterioration of reinforced concrete(RC)structures.Limited research has been performed to investigate the life-cycle cost(LCC)of coastal bridge piers with nonuniform corrosion using different materials.In this study,a reliability-based design optimization(RBDO)procedure is improved for the design of coastal bridge piers using six groups of commonly used materials,i.e.,normal performance concrete(NPC)with black steel(BS)rebar,high strength steel(HSS)rebar,epoxy coated(EC)rebar,and stainless steel(SS)rebar(named NPC-BS,NPC-HSS,NPC-EC,and NPC-SS,respectively),NPC with BS with silane soakage on the pier surface(named NPC-Silane),and high-performance concrete(HPC)with BS rebar(named HPC-BS).First,the RBDO procedure is improved for the design optimization of coastal bridge piers,and a bridge is selected to illustrate the procedure.Then,reliability analysis of the pier designed with each group of materials is carried out to obtain the time-dependent reliability in terms of the ultimate and serviceability performances.Next,the repair time of the pier is predicted based on the time-dependent reliability indices.Finally,the time-dependent LCCs for the pier are obtained for the selection of the optimal design.

Novel heart valve leaflet designs with stiff polymeric materials and biomimetic kinematics

Despite continuous efforts to improve the robustness of cardiac valve implants,neither bioprosthetic nor mechanical valves fulfill both hemodynamic and durability requirements.This study discussed novel flexible leaflet designs,focusing on polymeric materials with proven hemocompatibility,such as polyether ether ketone,of much higher stiffness than native tissue,aiming at optimal valve implants.A biomimetic valve with a single-curvature belly-curve(B-C)was used as a reference for new design variants with a double-curvature B-C with varying radii.Soft(13.2 MPa)and stiff(2.4 GPa)leaflet materials and different thicknesses were studied using lean simulations and in vitro experiments under physiologic hemodynamic conditions.The performance was assessed using opening pressure(OP)and orifice area(OA).The latter was determined by a newly developed automatized image processing tool.Experimental trends are in agreement with simulations and demonstrated that a buckling-inspired double-curvature leaflet design significantly enhances the trileaflet valve opening behavior,which is particularly advantageous for stiffer leaflet materials.Compared to the reference,the best-performing variant showed an OP improvement of 47%and 44%based on simulations and experiments,respectively.In contrast,the achieved mean pressure differential was directly comparable to state-of-the-art bioprosthetic valves.The OA was slightly reduced for new variants but still in the satisfying range.

Teaching Design of Course Building Decoration Materials Based on Generative Artificial Intelligence

With the digital transformation of global education and China's emphasis on education digital,generative AI technology has been widely used in the field of higher education.In this paper,the development of generative AI technology and its potential in personalized learning,interactive content creation and adaptive assessment in education were introduced firstly.Then,the application case of generative AI tools in teaching content creation,scenario-based teaching content development,visual teaching content development,complex concept deconstruction and analogy,student-led application practice and other aspects in the teaching of Building Decoration Materials was discussed.Through the teaching experiment and effect evaluation,the positive influence of generative AI technology on the improvement of students'learning effect and teaching efficiency was verified.Finally,some thoughts and inspirations on the combination of educational theory and generative AI technology,the integration of teaching design and generative AI technology,and the practice cases and effect evaluation were put forward,and the importance of teacher role transformation and personalized learning path design was emphasized to provide theoretical and practical support for the innovative development of higher education.

Big Data Creates New Opportunities for Materials Research: A Review on Methods and Applications of Machine Learning for Materials Design

Materials development has historically been driven by human needs and desires, and this is likely to con- tinue in the foreseeable future. The global population is expected to reach ten billion by 2050, which will promote increasingly large demands for clean and high-ef ciency energy, personalized consumer prod- ucts, secure food supplies, and professional healthcare. New functional materials that are made and tai- lored for targeted properties or behaviors will be the key to tackling this challenge. Traditionally, advanced materials are found empirically or through experimental trial-and-error approaches. As big data generated by modern experimental and computational techniques is becoming more readily avail- able, data-driven or machine learning (ML) methods have opened new paradigms for the discovery and rational design of materials. In this review article, we provide a brief introduction on various ML methods and related software or tools. Main ideas and basic procedures for employing ML approaches in materials research are highlighted. We then summarize recent important applications of ML for the large-scale screening and optimal design of polymer and porous materials, catalytic materials, and energetic mate- rials. Finally, concluding remarks and an outlook are provided.

Rational design of photofunctional dyes BODIPYs/aza-BODIPYs and applications for photocatalysis, photoelectric conversion and thermochromic materials

4,4-Difluoro-4-bora-3a,4a-diaza-sindacene (BODIPY) is a sort of photofunctional dye which possesses advantages including strong light-capturing property, high photon-resistance, etc. Meso-N substituted aza-BODIPY is a crucial derivative of BODIPY scaffold that has the favorable optical properties and a significant spectral redshift. The photophysical properties can be tuned by molecular design, and the attenuation path of the excited state energy release of absorbed light energy can be well controlled via structural modifications, enabling tailored application. It has been extensively employed in life medicine fields including fluorescence imaging diagnosis, photodynamic therapy photosensitizer and photothermal therapy reagent and so forth. Extensive research and review have been performed in these areas. However, BODIPYs/aza-BODIPYs have a significant role in energy, catalysis, optoelectronics, photo-responsive materials and other fields. Nevertheless, there are relatively few studies and reviews in these fields on the modification and application based on BODIPY/aza-BODIPY scaffold. Herein, in this review we summarized the application of BODIPY/aza-BODIPY in the aforementioned fields, with the molecular regulation of dye as the foundation and the utilization in the above fields as the objective, in the intention of providing inspiration for the exploration of innovative BODIPY/aza-BODIPY research in the field of light resource conversion and functional materials.

Architectural Design and Additive Manufacturing of Mechanical Metamaterials:A Review

Mechanical metamaterials can be defined as a class of architected materials that exhibit unprecedented mechanical properties derived from designed artificial architectures rather than their constituent materials.While macroscale and simple layouts can be realized by conventional top-down manufacturing approaches,many of the sophisticated designs at various length scales remain elusive,due to the lack of adequate manufacturing methods.Recent progress in additive manufacturing(AM)has led to the realization of a myriad of novel metamaterial concepts.AM methods capable of fabricating microscale architectures with high resolution,arbitrary complexity,and high feature fidelity have enabled the rapid development of architected meta materials and drastically reduced the design-computation and experimental-validation cycle.This paper first provides a detailed review of various topologies based on the desired mechanical properties,including stiff,strong,and auxetic(negative Poisson’s ratio)metamaterials,followed by a discussion of the AM technologies capable of fabricating these metamaterials.Finally,we discuss current challenges and recommend future directions for AM and mechanical metamaterials.

Discovery and design of lithium battery materials via high-throughput modeling

This paper reviews the rapid progress in the field of high-throughput modeling based on the Materials Genome Initiative, and its application in the discovery and design of lithium battery materials. It offers examples of screening, optimization and design of electrodes, electrolytes, coatings, additives, etc. and the possibility of introducing the machine learning method into material design. The application of the material genome method in the development of lithium battery materials provides the possibility to speed up the upgrading of new candidates in the discovery of lots of functional materials.

ELT Materials Design of a Speaking Unit based on Needs Analysis

In this article,the authors design a speaking unit based on needs analysis following Hutchinson and Waters'(1987) model.First,the rationale in designing this unit is introduced,which involves the teaching approach adopted and relevant theories in organizing the materials.Then,the teaching plan of this speaking unit is provided and some activities are designed to create an authentic and optimal situation for students to practice their speaking skill.

Determination of the best materials for development and designing product using a multi-criteria decision-making

Purpose–Material selection,driven by wide and often conflicting objectives,is an important,sometimes difficult problem in material engineering.In this context,multi-criteria decision-making(MCDM)methodologies are effective.An approach of MCDM is needed to cater to criteria of material assortment simultaneously.More firms are now concerned about increasing their productivity using mathematical tools.To occupy a gap in the previous literature this research recommends an integrated MCDM and mathematical Bi-objective model for the selection of material.In addition,by using the Technique for Order Preference by Similarity to Ideal Solution(TOPSIS),the inherent ambiguities of decision-makers in paired evaluations are considered in this research.It goes on to construct a mathematical bi-objective model for determining the best item to purchase.Design/methodology/approach–The entropy perspective is implemented in this paper to evaluate the weight parameters,while the TOPSIS technique is used to determine the best and worst intermediate pipe materials for automotive exhaust system.The intermediate pipes are used to join the components of the exhaust systems.The materials usually used to manufacture intermediate pipe are SUS 436LM,SUS 430,SUS 304,SUS 436L,SUH 409 L,SUS 441 L and SUS 439L.These seven materials are evaluated based on tensile strength(TS),hardness(H),elongation(E),yield strength(YS)and cost(C).A hybrid methodology combining entropy-based criteria weighting,with the TOPSIS for alternative ranking,is pursued to identify the optimal design material for an engineered application in this paper.This study aims to help while filling the information gap in selecting the most suitable material for use in the exhaust intermediate pipes.After that,the authors searched for and considered eight materials and evaluated them on the following five criteria:(1)TS,(2)YS,(3)H,(4)E and(5)C.The first two criteria have been chosen because they can have a lot of influence on the behavior of the exhaust intermediate pipes,on their performance and on the cost.In this structure,the weights of the criteria are calculated objectively through the entropy method in order to have an unbiased assessment.This essentially measures the quantity of information each criterion contribution,indicating the relative importance of these criteria better.Subsequently,the materials were ranked using the TOPSIS method in terms of their relative performance by measuring each material from an ideal solution to determine the best alternative.The results show that SUS 309,SUS 432L and SUS 436 LM are the first three materials that the exhaust intermediate pipe optimal design should consider.Findings–The material matrix of the decision presented in Table 3 was normalized through Equation 5,as shown in Table 5,and the matrix was multiplied with weighting criteriaß_j.The obtained weighted normalized matrix V_ij is presented in Table 6.However,the ideal,worst and best value was ascertained by employing Equation 7.This study is based on the selection of material for the development of intermediate pipe using MCDM,and it involves four basic stages,i.e.method of translation criteria,screening process,method of ranking and search for methods.The selection was done through the TOPSIS method,and the criteria weight was obtained by the entropy method.The result showed that the top three materials are SUS 309,SUS 432L and SUS 436 LM,respectively.For the future work,it is suggested to select more alternatives and criteria.The comparison can also be done by using different MCDM techniques like and Choice Expressing Reality(ELECTRE),Decision-Making Trial and Evaluation Laboratory(DEMATEL)and Preference Ranking Organization Method for Enrichment Evaluation(PROMETHEE).Originality/value–The results provide important conclusions for material selection in this targeted application,verifying the employment of mutual entropy-TOPSIS methodology for a series of difficult engineering decisions in material engineering concepts that combine superior capacity with better performance as well as cost-efficiency in various engineering design.

Advances in the structure design of substrate materials for zinc anode of aqueous zinc ion batteries

Aqueous zinc ion batteries(AZIBs) demonstrate tremendous competitiveness and application prospects because of their abundant resources,low cost, high safety, and environmental friendliness. Although the advanced electrochemical energy storage systems based on zinc ion batteries have been greatly developed, many severe problems associated with Zn anode impede its practical application, such as the dendrite formation,hydrogen evolution, corrosion and passivation phenomenon. To address these drawbacks, electrolytes, separators, zinc alloys, interfacial modification and structural design of Zn anode have been employed at present by scientists. Among them, the structural design for zinc anode is relatively mature, which is generally believed to enhance the electroactive surface area of zinc anode, reduce local current density, and promote the uniform distribution of zinc ions on the surface of anode. In order to explore new research directions, it is crucial to systematically summarize the structural design of anode materials. Herein, this review focuses on the challenges in Zn anode, modification strategies and the three-dimensional(3D) structure design of substrate materials for Zn anode including carbon substrate materials, metal substrate materials and other substrate materials. Finally, future directions and perspectives about the Zn anode are presented for developing high-performance AZIBs.

High-throughput computational screening and design of nanoporous materials for methane storage and carbon dioxide capture

The globally increasing concentrations of greenhouse gases in atmosphere after combustion of coal-or petroleum-based fuels give rise to tremendous interest in searching for porous materials to efficiently capture carbon dioxide(CO_2) and store methane(CH4), where the latter is a kind of clean energy source with abundant reserves and lower CO_2 emission. Hundreds of thousands of porous materials can be enrolled on the candidate list, but how to quickly identify the really promising ones, or even evolve materials(namely, rational design high-performing candidates) based on the large database of present porous materials? In this context, high-throughput computational techniques, which have emerged in the past few years as powerful tools, make the targets of fast evaluation of adsorbents and evolving materials for CO_2 capture and CH_4 storage feasible. This review provides an overview of the recent computational efforts on such related topics and discusses the further development in this field.

Optimization design and residual thermal stress analysis of PDC functionally graded materials

The distribution of thermal stresses in functionally graded polycrystalline diamond compact (PDC) and in single coating of PDC are analyzed respectively by thermo-mechanical finite element analysis (FEA). It is shown that they each have a remarkable stress concentration at the edge of the interfaces. The diamond coatings usually suffer premature failure because of spallation, distortion or defects such as cracks near the interface due to these excessive residual stresses. Results showed that the axial tensile stress in FGM coating is reduced from 840 MPa to 229 MPa compared with single coating, and that the shear stress is reduced from 671 MPa to 471 MPa. Therefore, the single coating is more prone to spallation and cracking than the FGM coating. The effects of the volume compositional distribution factor (n) and the number of the graded layers (L) on the thermal stresses in FGM coating are also discussed respectively. Modelling results showed that the optimum value of the compositional distribution factor is 1.2, and that the best number of the graded layers is 6.

Porous structures of natural materials and bionic design

This investigation and morphology analysis of porous structure of some kinds of natural materials such as chicken eggshell, partridge eggshell, pig bone, and seeds of mung bean, soja, ginkgo, lotus seed, as well as the epidermis of apples, with SEM (Scanning Electronic Microscope) showed that natural structures’ pores can be classified into uniform pores, gradient pores and multi pores from the viewpoint of the distribution variation of pore density, size and geometry. Furthermore, an optimal design of porous bearings was for the first time developed based on the gradient configuration of natural materials. The bionic design of porous structures is predicted to be widely developed and applied in the fields of materials and mechanical engineering in the future.

Machine learning of materials design and state prediction for lithium ion batteries

With the widespread use of lithium ion batteries in portable electronics and electric vehicles,further improvements in the performance of lithium ion battery materials and accurate prediction of battery state are of increasing interest to battery researchers.Machine learning,one of the core technologies of artificial intelligence,is rapidly changing many fields with its ability to learn from historical data and solve complex tasks,and it has emerged as a new technique for solving current research problems in the field of lithium ion batteries.This review begins with the introduction of the conceptual framework of machine learning and the general process of its application,then reviews some of the progress made by machine learning in both improving battery materials design and accurate prediction of battery state,and finally points out the current application problems of machine learning and future research directions.It is believed that the use of machine learning will further promote the large-scale application and improvement of lithium-ion batteries.