Material Design Method Research Articles

Bending‐Free Lattices as Underlying Optimal Isotropic Microstructures of the Least‐Compliant 2D Elastic Bodies

This study puts forward a method of reconstruction of the suboptimal isotropic truss microstructures for the known distribution of Young's modulus and Poisson's ratio predicted by the Isotropic Material Design (IMD) method of minimizing the compliance (maximizing the stiffness) of a 2D elastic body. The varying underlying microstructures corresponding to the optimal designs are recovered by matching the values of the optimal elastic moduli with the values of the effective moduli of the representative volume elements (RVE) computed by the asymptotic homogenization method. The used hexagonal shape of the RVE with its specific internal symmetry provides the assumed isotropy of the periodic body. Hexagonal periodic cells are made up of 3 or 12 pin‐jointed families of bars with different stiffness characteristics. Combining these families into two or three groups with identical bar stiffnesses in a group allows for the exact reproduction of Poisson's ratio for each point of the body. The ability to model an auxetic behavior within the subdomains where the optimal Poisson's ratio assumes negative values is also shown. The effective value of the Young's modulus is scaled independently of the value of the effective Poisson's ratio by selecting the cross section of the bars in the group.

Preparation of dual-functional composite magnetic nanomaterials modified with different metals/aptamers and their performance in exosome enrichment

外泌体作为一种细胞外囊泡,其内容物可以反映亲代细胞的重要信息,而自身也具有独特的结构,能够执行特征的生物学功能。基于外泌体的表面化学和生物学特征,制备了不同类型的金属/适配体(Apt)双功能复合磁性纳米材料,并将其应用于外泌体的富集纯化。将适配体和外泌体表面目标膜蛋白的特异性结合性能与以钛、锆为代表的金属氧化物和外泌体磷脂双层膜的特异性亲和作用结合,可极大地提高分离材料对外泌体的分离选择性和富集容量。分别以Fe3O4@Zr-MOFs、Fe3O4@Zr-Ti-MOFs和Fe3O4@TiO2等金属有机框架(MOFs)/金属氧化物磁性纳米材料为基底,制备对应的双功能MOFs/金属氧化物-适配体复合磁性纳米材料Fe3O4@Zr-MOFs-Apt、Fe3O4@Zr-Ti-MOFs-Apt和Fe3O4@TiO2-Apt,并进一步对不同材料的外泌体富集性能加以评价。以超速离心法提取的模型外泌体以及尿液为样品,对修饰相同质量适配体和不同含量金属氧化物的双功能材料的富集性能加以对比。将3种双功能磁性纳米材料应用于尿液外泌体的富集,得到的外泌体裂解后经质谱鉴定,分别得到233、343和832个外泌体蛋白。这一结果也表明双功能磁性纳米材料可以充分结合核酸适配体亲和的高选择性和金属氧化物的高富集容量优势,对于复杂生物样品中外泌体的快速、高效分离纯化具有潜在的应用价值,而针对材料制备和分离纯化方法的设计也为新型外泌体富集材料的设计提供了一条可行的新思路。

Graphene wrapped Y2O3 coated LiNi0.5Mn1.5O4 quasi-spheres as novel cathode materials for lithium-ion batteries

LiNi0.5Mn1.5O4 with a high-voltage spinel structure is a potential cathode material for high-energy lithium-ion batteries (LIBs). Y2O3 coated quasi-spheres of LiNi0.5Mn1.5O4 covered in graphene (LNMO-YO-G) have been synthesized by a microwave-assisted chemical co-precipitation technique. The coating of quasi-spheres with Y2O3 and subsequent wrapping in graphene nanosheets does not modify the bulk structure and inhibits the production of undesirable phases. Thermal analysis indicates that the developed materials demonstrate good thermal stability. The material exhibits an initial capacity of 133 mAh g−1 at the C/10 rate with a capacity retention of 98% after 100 cycles. Remarkably, a discharge capacity of 115 mAh g−1 is achieved in LNMO-YO-G at a 10C rate, reflecting its extraordinary improvement in the rate capability. Furthermore, after 20 cycles at higher temperature (55 °C), the cathode samples exhibit an excellent capacity of 132 mAh g−1. Y2O3 coating reduces the leaching of ions from the electrode, but such coatings reduce the electrical conductivity. Conversely, graphene increases the electrical conductivity, wraps the active particles along an electrically conductive path, and prevents agglomeration. Parasitic reactions are inhibited without compromising electrical conductivity due to the synergistic material design and fast microwave synthesis method. The proposed material synthesis strategy can be effectively extended to other classes of electrode materials to improve their cyclic performance.

Surface modification of cellulose nanocrystals towards new materials development

AbstractCellulose nanocrystals (CNCs) are a kind of sustainable nanoparticle from biomass, which are widely used as reinforcing filler and assembly building block for high‐performance composites and function materials including biomaterial, optics, and so forth. Here, their unique advantages in material applications were reviewed based on their rod‐like morphology, crystalline structure, dimension‐related effects, and multi‐level order structure. Then, we focused on the molecular engineering of CNCs, including the structure and physicochemical properties of their surface, along with surface modification methods and steric effects. We further discussed the performance‐improvement and functionalization methods based on multi‐component complex systems, together with the effects of surface molecular engineering on the performance and functions. Meanwhile, methods of optimizing orientation in uniaxial arrays were discussed along with those of enhancing photoluminescence efficiency via surface chemical modification and substance coordination. In the end, we prospected the design, development, and construction methods of new CNCs materials.

Accelerating Optimizing the Design of Carbon‐based Electrocatalyst via Machine Learning

AbstractIn this era of artificial intelligence, we urgently want to optimize the current material design methods to come up with a more efficient and more accurate closed‐loop system. The approach requires at least three parts including high‐throughput screening, automated synthesis platform, and machine learning algorithms. Fortunately, the techniques mentioned above have been substantial developed. We have introduced the common algorithms of machine learning. Then, several machine learning‐based design of carbon‐based electrocatalysts are discussed. We tried to illustrate the research norms involving machine learning. Besides, other paper structures and details have been also discussed.

Nonlinear Multiscale Modelling and Design using Gaussian Processes

A method for nonlinear material modeling and design using statistical learning is proposed to assist in the mechanical analysis of structural materials. Conventional computational homogenization schemes are proven to underperform in analyzing the complex nonlinear behavior of such microstructures with finite deformations. Also, the higher computational cost of the existing homogenization schemes inspires the inception of a data-driven multiscale computational homogenization scheme. In this paper, a statistical nonlinear homogenization scheme is discussed to mitigate these issues using the Gaussian Process Regression technique. A data-driven model is trained for different strain states of microscale unit cells. In the macroscale, nonlinear response of the macroscopic structure is analyzed, for which the stresses and material responses are predicted by the trained surrogate model.

Accelerated discovery of high-performance Cu-Ni-Co-Si alloys through machine learning

Cu-Ni-Co-Si alloys have been regarded as a candidate for the next-generation integrated circuits. Nevertheless, using the trial and error method to design high-performance copper alloys requires a lot of effort and time. Thus, the material design method based on machine learning is used to accelerate the exploitation of alloys. In this study, a composition-process-property database of Cu-Ni-Co-Si alloys was established, and a new strategy that could simultaneously realize the prediction of properties and the optimization of compositions and process parameters was proposed. Four groups were chosen from 38,880 candidates by the multi-performance screening method; goodagreements existed between the prediction and the test. The Cu-2.3Ni-0.7Co-0.7Si alloy had the best performance among the designed alloys, and this alloy was studied in depth. The influence of the dissolution of Co in Ni2Si was analyzed from a novel perspective. Interestingly, the trace amount of Co replacing Ni to form (Ni, Co)2Si increased the phase dissolution temperature dramatically and shortened the coarsening rate. Affected by Co, the over-aging process was slowed down, which broadened the use range of alloys greatly. Therefore, the developed Cu-2.3Ni-0.7Co-0.7Si alloy can prove to be promising materials that meet different working conditions, and its performance was better than C70350 alloy.

Heterogeneous iron oxide nanoparticles anchored on carbon nanotubes for high-performance lithium-ion storage and fenton-like oxidation

In this work, heterogeneous hematite (Fe2O3) and magnetite (Fe3O4) nanoparticles are jointly engineered on the external surface of multi-walled carbon nanotubes (CNTs) to construct a composite material (Fe2O3@Fe3O4/CNT). A simple one-step redox reaction is triggered in a hydrothermal reaction system containing functionalized CNT (FCNT) aqueous suspension and iron foils. Both Fe2O3 and Fe3O4 nanoparticles with controlled size are generated and well dispersed in the interconnected CNT framework. Controlled samples of Fe2O3@Fe3O4 and Fe3O4/CNT have also been prepared and used to investigate the synthetic mechanism and evaluate the lithium-ion storage performances. As an anodic active material for lithium-ion batteries, the Fe2O3@Fe3O4/CNT composite delivered a high reversible capacity of about 924 mAh·g−1 for 200 continual charge/discharge cycles under a high current rate of 1000 mA·g−1. As a catalyst in a Fenton-like reaction for degrading methyl orange (MO) contaminant in waterbody, the Fe2O3@Fe3O4/CNT composite exhibited an attractive decomposition efficiency (99.5% decomposition within 60 min) and good stability. The beneficial factors contributing to the inspiring performances are discussed. The effective and scalable material design and synthesis method can be regarded to have good potential in other fields.

Physics-guided multi-objective mixture optimization for functional cementitious composites containing microencapsulated phase changing materials

A physics-guided multi-objective optimization procedure is developed for the mixture design of functional cementitious materials containing microencapsulated phase change materials (MEPCM). The mixture design procedure combines physics-based models with multi-objective optimization and decision-making methods to meet user’s demands on material’s mechanical and thermal properties, as well as the requirements for sustainability, functionalities, and cost. Physics-based models were utilized to draw the linkage between design variables and objective functions, including a hydration model to capture the hydration kinetics of slag-blended cement and a multiscale sub-stepping homogenization model to obtain the properties of cementitious composite. The multi-objective feasible enhanced particle swarm optimization (MOFEPSO) algorithm and the technique for preference by similarity to an ideal solution (TOPSIS) algorithm are used for mixture optimization and decision-making. The material design method is demonstrated through the design of functional cementitious composite materials containing two MEPCMs – i.e., a polymer encapsulated paraffin wax (PolyPCM) and a recently developed fly-ash cenosphere encapsulated PCM (CenoPCM). The design decision-making charts show the trade-offs among mechanical, thermal, and economic performances of cementitious composites containing MEPCMs. The mixture optimization and decision-making method can be used to assist the design of a variety types of functional cementitious composite and concrete.

Advanced structural material design based on simulation and data-driven method

Advanced structural materials have received extensive attention in the field of materials and structural design in recent years. These materials generally achieve excellent performances via structural design at multiple length-scales. In the early material design, some researchers created reasonable mathematical and mechanical models from the natural topologies; some researchers established bionic mechanical models based on the structural and functional characteristics of biological systems. Nevertheless, it isn't easy to obtain the optimal designs only based on ingenious design. To traverse the design space to search for the optimal design by trial and error is also not practical. For these reasons, the topology optimization method has been successfully applied to the design of advanced structural materials such as phononic crystals, cellular materials, etc. However, the existing topology optimization methods still have challenges in achieving accurate reverse designs. Data-driven methods can establish complex relationships of multi-dimensional variables, and they can reveal mechanical mechanisms and laws that are difficult to be discovered by traditional methods. Hence, this paper systematically reviews the development of advanced structural material design methods. Various mainstream design methods are compared and illustrated. The status of intelligent design of advanced structural materials based on data-driven methods is introduced. The prospect of this research area is discussed.

Self-Healing Soft Sensors: From Material Design to Implementation.

The demand for interfacing electronics in everyday life is rapidly accelerating, with an ever-growing number of applications in wearable electronics and electronic skins for robotics, prosthetics, and other purposes. Soft sensors that efficiently detect environmental or biological/physiological stimuli have been extensively studied due to their essential role in creating the necessary interfaces for these applications. Unfortunately, due to their natural softness, these sensors are highly sensitive to structural and mechanical damage. The integration of natural properties, such as self-healing, into these systems should improve their reliability, stability, and long-term performance. Recent studies on self-healing soft sensors for varying chemical and physical parameters are herein reviewed. In addition, contemporary studies on material design, device structure, and fabrication methods for sensing platforms are also discussed. Finally, the main challenges and future perspectives in this field are introduced, while focusing on the most promising examples and directions already reported.

Optimization of Molecular Characteristics via Machine Learning Based on Continuous Representation of Molecules

We demonstrate an automatic materials design method using continuous representation of molecule and its atomic arrangement via a neural network algorithm. This method is applied to optimizing and predicting the HOMO-LUMO gap within the molecules composed of carbon, oxygen, nitrogen, fluorine, and hydrogen. Adopting the Quantum Machine 9 (QM9) dataset as a training dataset for the molecules, we first established a continuous representation of molecules in a latent space, then predicted molecules that have target values of the HOMO-LUMO gap. In the gap maximization calculation, the CF4 with the largest gap value in the QM9 dataset was automatically found despite there is no a priori data for the gap. In the case of a target gap value of 0.10 hartree, we found a new molecule whose gap value is closer to 0.10 hartree than any other molecules in the QM9 dataset.

Component optimization of porous permeable brick in “sponge city” based on rainfall area division

The present study aims to prepare resin-based permeable bricks with micron-sized pores using fine aggregate with a particle diameter of 0.08mm-0.6mm and bisphenol-A epoxy resin, a polymer binder. The rainfall area of China was divided into 5 levels by using ArcGIS and Geoda geographic analysis software; The performance indexes of key components of resin-based permeable brick were selected by correlation with relevant standards, and the performance of materials is adjusted and designed according to the corresponding performance indexes, so as to meet the design requirements of “Sponge City”. The main method of material design is to adjust the key parameters of raw materials, such as aggregates, binder materials and admixtures, by using Image Pro Plus software and SEM and other test methods, so as to optimize the selection of “graded” aggregates and admixtures for different performance requirements scenarios to achieve the material selection goal. The highest permeable rate of resin-based permeable brick is 6.22× 10–2cm/s s, and the maximum compressive strength is more than 60MPa.

On the Elemental Impact Factor, a Method to Determine an Alloy's Compositional Influences upon Phase Stability and Metallurgical Material Properties.

Design-driven materials engineering is gaining wider acceptance with the advancement and refinement of commercially available thermodynamic software as well as enhanced computing power. Computationally designed materials are a significant improvement over the more common and resource-intensive experimental approach to materials design by way of trial and error. While not entirely eliminating experimental methods for alloy design, thermodynamic and kinetic models provide accurate predictions of phases within a given alloy, which enables material properties to be calculated. Accordingly, the present paper introduces a new technique that offers a systematic method of material design by way of utilizing commercial computational software, which has been termed the elemental impact factor. In turn, the present manuscript considers Al 6061 as a proof-of-concept metallic alloy system for elemental impact factor substantiation. Effects of chemical composition on resultant equilibrium and metastable material phases as well as properties can be efficiently assessed with the elemental impact factor framework for metallurgical materials design. Desired phases or properties may be produced by adding elements with a positive elemental impact factor, while deleterious phases or undesired properties may be reduced by adding elements with a negative elemental impact factor. Therefore, the elemental impact factor methodology was presented and then demonstrated herein with examples that showcase the technique’s potential applications and utility for integrated structure-processing-property-performance analysis.

Long fibre-reinforced thermoplastic theoretical model for energy absorption analysis based on impact performance

This article deals with initial material selection and design method of long fibre-reinforced thermoplastic (LFT) based on impact resistance requirements. In this study, effects of material properties and impact condition, including total fibre thickness, fibre aspect ratio and impactor diameter, on the minimum penetration energy of LFT plates are analysed based on experiments and validated numerical models. Mathematical expression is established between them. Applicability of the original model for LFT plate with different fibre aspect ratios subjected to different impactors is discussed. The minimum penetration energy is used as the bridge between energy absorption characteristics and material parameters, impact condition. Finally, an updated theoretical model for energy absorption analysis is proposed. We conclude that the updated semi-empirical model is a valuable tool for a first stage material selection and design.

Controlling Performance of Organic–Inorganic Hybrid Perovskite Triboelectric Nanogenerators via Chemical Composition Modulation and Electric Field‐Induced Ion Migration

AbstractIn this paper, new strategies are proposed to design high‐performance organic–inorganic hybrid perovskite (PVK)‐based triboelectric nanogenerators (TENGs) via both chemical composition modulation and electric field‐induced ion migration in the films. Both composition variation and ion migration under electric field are found to change the type of conductivity of the perovskite films, then modify their surface potentials and electron affinities. These are utilized to fabricate PVK‐based TENGs in pairs with poly‐tetrafluoroethylene (PTFE) or nylon films, respectively. Results show that PVK films are able to work as either a positive or a negative tribo‐material depending on the tribo‐material pair used; the optimal performances are obtained for PTFE/PVK TENGs using a PVK film with a MAI/PbI2 ratio of 2 and forward polarization, and for nylon/PVK TENGs using a PVK film with a MAI/PbI2 ratio of 0.4 and reverse polarization, respectively. The maximum output voltage and peak power density of PTFE/PVK TENGs are about 979 V and 24 W m−2, 2.5 and 6.5 times higher than those of TENGs with nonoptimal composition ratio or that are poorly polarized. This work provides a new material design method for high‐performance TENGs and a novel polarization strategy for TENG performance enhancement.

Investigation on the designing method of asphalt emulsion cold recycled mixture based on one-time compaction

The asphalt emulsion cold recycled mixture (CRME) technology is one of the main utilization method when RAP is used in the asphalt pavement construction. However, there is not a unified material design method for this technology. In this paper, according to the Marshall test method, the test samples were prepared by two molding methods: one-time compaction and two-time compaction. The effects of compaction work and moisture content on the performance of CRME were studied. The test results showed that after the one-time compaction of CRME, as the compaction work increased, the air void of the mixture gradually decreased, and indirect tensile strength (ITS) gradually increased. When the first compaction times exceeded 125 times, the density of CRME could meet the requirement, only limited changes could be observed when the compaction work was continuously increased. However, the increase in compaction work may cause lead to the occurrence of internal cracks and reduce the strength of the mixture. The moisture content of CRME was basically the same at 60 °C for 120 h and 70 °C for 48 h, indicating that the moisture content parameters of CRME met the principle of time-temperature equivalence. CRME was cured for 8 h at 60 °C, which had met the requirements of the technical specification with a moisture content of less than 2%. When the samples were cured for more than 24 h, there was no remarkable difference in ITS and air void of the mixtures formed by one-time compaction and two-time compaction. Therefore, there was no need to perform a second compaction for the mixture. On this basis, a material design method for CRME with one-time compaction was proposed.

Machine learning-assisted discovery of strong and conductive Cu alloys: Data mining from discarded experiments and physical features

Copper alloys with high strength and electrical conductivity are ideal candidates for a wide range of civilian and engineering applications. Traditional methods of alloy design, such as “trial and error” experiments, are costly and time-consuming, limiting the discovery of Cu alloys. Machine learning (ML)-based technology facilitates a better understanding on inter-relationships within massive experimental datasets, and shortens the development cycle of new alloy systems. Herein, we propose a method of material design based on ML to discover high-performance Cu alloys. The ML models are trained from “discarded” experimental data that show undesirable hardness and/or electrical conductivity. We constructed effective Gaussian process regression-based models successfully from limited training data by engaging additional features. The predicted Cu alloys were experimentally synthesized and characterized, exhibiting superior hardness or electrical conductivity with respect to original training data. The increase of hardness is due to precipitation of second-phase Co-Ti and Fe-Ti compounds during aging, while the rise of electrical conductivity is caused by a purification effect of the precipitates on Cu matrix. The impact of physical features on ML models was further evaluated by a genetic algorithm. Our findings suggest that ML holds great potential for developing high-performance Cu alloys.

Prussian blue analog Co3[Co(CN)6]2 as a cathode material for lithium–sulfur batteries

In this work, the authors proposed a unique strategy to prepare a high performance Lithium–Sulfur (Li–S) battery cathode by an N-rich nano-graphene hollow sphere from the Prussian blue analog Co3[Co(CN)6]2. The one-pot solution method with low cost materials was designed to synthesize the cathode by annealing with different temperatures and consistent HCl etching steps for the precursor. The Li–S batteries made by this precursor showed remarkable charge–discharge stability (570.4 mA h g−1 after 500 cycles at a 1 C current density) and outstanding rate performance (1145.5, 717.9, and 672.5 mA hg−1 at 0.1, 1.0, and 2.0 Ag−1 current densities) at 800 °C. The results indicated that the stable three-dimensional multi-layer hollow sphere structure relieved the volume expansion of sulfur, which had a significant impact on the polysulfide adsorption, inhibiting the “shuttle effect.” Also, the rich doping of nitrogen in this structure produced many defects and active sites that improved the interfacial adsorption of polysulfides. While Co3[Co(CN)6]2 shows its potential as a cathode material, the material design method used in this paper provides a unique approach to realizing a high performance sulfur cathode for Li–S batteries.

Recent Progress in MXene‐Based Materials: Potential High‐Performance Electrocatalysts

AbstractThe family of transition metal carbides, nitrides, and carbonitrides (collectively called MXenes) has been a thriving field since the first invention of Ti3C2Tx (MXene) in 2011. MXene is a new type of nanometer 2D sheet material, which exhibits great application potentials in various fields due to its multiple advantages such as high specific surface area, good electrical conductivity, and high mechanical strength. Electrocatalysis is regarded as the core of future clean energy conversion technologies, and MXene‐based materials provide inspiration for the design and preparation of electrocatalysts with high activity, high selectivity, and long loading life time. The applications of MXene‐based materials in electrocatalysis, including hydrogen evolution reaction, nitrogen reduction reaction, oxygen evolution reaction, oxygen reduction reaction, carbon dioxide reduction reaction, and methanol oxidation reaction are summarized in this review. As a crucial session regarding experiments, the current safer and more environmentally friendly preparation methods of MXene are also discussed. Focusing on the materials design and enhancement methods, the key challenges and opportunities for MXene‐based materials as a next‐generation platform in both fundamental research and practical electrocatalysis applications are presented. This account serves to promote future efforts toward the development of MXenes and related materials in the electrocatalysis applications.