Topological homogenization of metamaterial variability

With the proliferation of additive manufacturing and 3D printing technologies, a broader palette of material properties can be elicited from cellular solids, also known as metamaterials, architected foams, programmable materials, or lattice structures. Metamaterials are designed and optimized under the assumption of perfect geometry and a homogeneous underlying base material. Yet in practice real lattices contain thousands or even millions of complex features, each with imperfections in shape and material constituency. While the role of these defects on the mean properties of metamaterials has been well studied, little attention has been paid to the stochastic properties of metamaterials, a crucial next step for high reliability aerospace or biomedical applications. In this work we show that it is precisely the large quantity of features that serves to homogenize the heterogeneities of the individual features, thereby reducing the variability of the collective structure and achieving effective properties that can be even more consistent than the monolithic base material. In this first statistical study of additive lattice variability, a total of 239 strut-based lattices were mechanically tested for two pedagogical lattice topologies (body centered cubic and face centered cubic) at three different relative densities. The variability in yield strength and modulus was observed to exponentially decrease with feature count (to the power −0.5), a scaling trend that we show can be predicted using an analytic model or a finite element beam model. The latter provides an efficient pathway to extend the current concepts to arbitrary/complex geometries and loading scenarios. These results not only illustrate the homogenizing benefit of lattices, but also provide governing design principles that can be used to mitigate manufacturing inconsistencies via topological design.     

Manufacturing catalyst-coated membranes by ultrasonic spray deposition for PEMFC: Identification of key parameters and their impact on PEMFC performance

This study deals with the manufacturing of catalyst-coated membranes (CCMs) for newcomers in the field of coating. Although there are many studies on electrode ink composition for improving the performance of proton-exchange membrane fuel cells (PEMFCs), there are few papers dealing with electrode coating itself. Usually, it is a know-how that often remains secret and constitutes the added value of scientific teams or the business of industrialists. In this paper, we identify and clarify the role of key parameters to improve coating quality and also to correlate coating quality with fuel cell performance via polarization curves and electrochemical active surface area measurements. We found that the coating configurations can affect the performance of lab-made CCMs in PEMFCs. After the repeatability of the performance obtained by our coating method has been proved, we show that: (i) edge effects, due to mask shadowing - cannot be neglected when the active surface area is low, (ii) a heterogeneous thickness electrode produces performance lower than a homogeneous thickness electrode, and (iii) the origin and storage of platinum on carbon powders are a very important source of variability in the obtained results.     

Application of artificial neural network method to predict the breakage properties of PGE bearing chromite ore

In the present investigation, systematic grinding experiments were conducted in a laboratory ball mill to determine the breakage properties of low-grade PGE bearing chromite ore. The population balance modeling technique was used to study the breakage parameters such as primary breakage distribution (B_{i, j}) and the specific rates of breakage (S_i). The breakage and selection function values were determined for six feed sizes. The results stated that the breakage follows the first-order grinding kinetics for all the feed sizes. It was observed that the coarser feed sizes exhibit higher selection function values than the finer feed size. Further, an artificial neural network was used to predict breakage characteristics of low-grade PGE bearing chromite ore. The predicted results obtained from the neural network modeling were close to the experimental results with a correlation of determination R² = 0.99 for both product size and selection function.     

Influence of silicon carbide on structural,optical and magnetic properties of Wollastonite/Fe2O3 nanocomposites

The influence of adding 10, 20 and 30% molar ratio of silicon carbide (SiC) separately to a composite of wollastonite (W) with a fixed content of 10%Fe₂O₃ prepared by wet precipitation method was studied. The crystal structure of the annealed composite powders was inspected by X-ray diffraction (XRD); revealing multi-phase structure. The highest estimated crystallite size investigated by Scherrer equation of W, SiC, WFe:SiC₁₀, WFe:SiC₂₀ and WFe:SiC₃₀ were 53.89, 54.6, 56.3, 48.5 and 54.6 nm respectively; demonstrating the formation of nanocomposites. Particles shape, size and crystallinity of the samples were studied using high resolution transmission electron microscope (HR-TEM). The band gap E_g values of the nanocomposites increased with SiC content having an intermediate value that lies between that of γ-Fe₂O₃ (maghemite) and SiC. Ferromagnetic and paramagnetic contributions were observed in the magnetic hysteresis loops for the composites. This study highlighted that the coercive field (H_ci) of the composites improved with increasing the SiC content. The innovative wollastonite/Fe₂O₃/SiC with amended magnetic properties elicited attention due to their promising application in bone filler and industrial purposes.     

Machine learning of phases and mechanical properties in complex concentrated alloys

The mechanical properties of complex concentrated alloys (CCAs) depend on their formed phases and corresponding microstructures.The data-driven prediction of the phase formation and associated mechanical properties is essential to discovering novel CCAs.The present work collects 557 samples of various chemical compositions,comprising 61 amorphous,167 single-phase crystalline,and 329 multi-phases crystalline CCAs.Three classification models are developed with high accuracies to category and understand the formed phases of CCAs.Also,two regression models are constructed to predict the hard-ness and ultimate tensile strength of CCAs,and the correlation coefficient of the random forest regression model is greater than 0.9 for both of two targeted properties.Furthermore,the Shapley additive expla-nation (SHAP) values are calculated,and accordingly four most important features are identified.A significant finding in the SHAP values is that there exists a critical value in each of the top four fea-tures,which provides an easy and fast assessment in the design of improved mechanical properties of CCAs.The present work demonstrates the great potential of machine learning in the design of advanced CCAs.     

Microstructure-abrasive wear correlation of in situ ZA27/TiC composites

Abrasive wear is a complex surface degradation process driven by various factors such as microstructure, the mechanical properties of the target material, the abrasive, loading conditions, and the surrounding environment. In this study, in situ TiC reinforced Zinc Aluminum alloy composites were prepared through a liquid metallurgy route and the synergistic effect of applied load, sliding speed, abrasive grit size and TiC content on the high-stress abrasive wear response were investigated. The test materials' wear response was established by characterising wear surfaces, sub-surfaces, debris particles, and an abrasive medium. The study suggests that the wear resistance of the specimens decreases with an increase in the applied load, and the composite reinforced with 10 wt % of TiC shows superior wear behaviour among all the test materials. The study also points out that the ZA-27 alloy reinforced with in situ TiC can be a suitable replacement of the conventionally used materials for automotive applications.     

Studies on spinel cobaltites,MCo2O4 (M = Mn,Zn, Fe,Ni and Co) and their functional properties

Optimization of electrodes for charge storage with appropriate processing conditions places significant challenges in the developments for high performance charge storage devices. In this article, metal cobaltite spinels of formula MCo₂O₄ (where M = Mn, Zn, Fe, Ni and Co) are synthesized by oxalate decomposition method followed by calcination at three typical temperatures, viz. 350, 550, and 750 °C and examined their performance variation when used as anodes in lithium ion batteries. Phase and structure of the materials are studied by powder x-ray diffraction (XRD) technique. Single phase MnCo2O4,ZnCo2O4 and Co3O4 are obtained for all different temperatures 350 °C, 550 °C and 750 °C; whereas FeCo₂O₄ and NiCo₂O₄ contained their constituent binary phases even after repeated calcination. Morphologies of the materials are studied via scanning electron microscopy (SEM): needle-shaped particles of MnCo₂O₄ and ZnCo₂O₄, submicron sized particles of FeCo₂O₄ and agglomerated submicron particle of NiCo₂O₄ are observed. Galvanostatic cycling has been conducted in the voltage range 0.005–3.0 V vs. Li at a current density of 60 mA g^?1 up to 50 cycles to study their Li storage capabilities. Highest observed charge capacities are: MnCo₂O₄ – 365 mA h g^?1 (750 °C); ZnCo₂O₄ – 516 mA h g^?1 (550 °C); FeCo₂O₄ – 480 mA h g^?1 (550 °C); NiCo₂O₄ – 384 mA h g^?1 (750 °C); and Co₃O₄ – 675 mA h g^?1 (350 °C). The Co₃O₄ showed the highest reversible capacity of 675 mA h g^?1; the NiO present in NiCo₂O₄ acts as a buffer layer that results in improved cycling stability; the ZnCo₂O₄ with long needle-like shows good cycling stability.     

公民水素养评价表征因素模型构建与分析

针对我国水资源紧缺、水生态损害、水环境污染等相关水问题,以提升全民爱水惜水节水护水的意识和能力为目标,从水素养概念出发,通过扎根理论质性研究方法对公民水素养评价的表征因素进行筛选和提炼,最终得出包含水知识、水态度、水行为为主范畴的10个表征因素并运用解释结构模型(ISM)深入剖析各因素之间的内在联系。在此基础上建立了3级多层次递阶结构模型,进一步精确描述系统各要素两两之间的直接关系。结果表明:公民水素养评价的表征因素自上而下形成了表象层、中间层和根源层等3个层级,可直观、清晰地反映表征因素之间的递阶、层级关系;水态度在各表征因素中具有调节作用以及各表征因素之间存在内在逻辑关系。研究成果为提高公民水素养水平提供一定的科学依据和政策参考。     

一种压电半导体纳米线的热电耦合性能研究

采用有限元分析方法,研究了一种n型压电半导体纳米线(氧化锌)的电热耦合性能,分析了外部温度对氧化锌纳米线内部机械场、电场及电流场分布的影响,并讨论了本构方程线性化对电学参数的影响。研究结果表明,温度对氧化锌纳米线的电场、载流子浓度和电流密度影响很大,采用线性本构和非线性本构求得的电场、电子浓度和电流密度最大相差分别为24%,32%和68%,基于非线性本构分析压电半导体的电学性能会引起很大误差。该研究结果可为压电半导体器件利用温度调控电场、电流提供理论依据。     

表面印迹耦合溶胶-凝胶法制备4-硝基酚印迹材料及性能表征

采用表面印迹和溶胶?凝胶相结合的方法，以4-硝基酚(4-NP)为模板分子、α-甲基丙烯酸(α-MAA)为功能单体、三羟甲基丙烷三甲基丙烯酸酯(TRIM)为交联剂、偶氮二异丁腈(AIBN)为引发剂，制备可选择性识别吸附去除水中4-NP污染物的印迹吸附材料，考察了4-NP与α-MAA和4-NP与TRIM摩尔比、致孔剂种类、提取液种类对所制材料吸附性能的影响，并通过正交实验优化材料制备条件，用扫描电子显微镜、傅立叶红外光谱仪、比表面积分析仪、热重?差热分析仪和纳米粒度仪等设备对最优条件下合成的材料进行了表征。结果表明，4-NP:α-MAA摩尔比为1:8，4-NP:TRIM摩尔比为1:10，致孔剂为乙醇，提取液为甲醇/乙酸(7:3，体积比)时所制印迹材料对4-NP的去除效果最佳，达6.23 μmol/g；4-NP成功印记在硅胶表面，印迹材料能特异性吸附4-NP。