Current Opinion in Solid State & Materials Science最新文献

With the advent of faster computer processors and especially graphics processing units (GPUs) over the last few decades, the use of data-intensive machine learning (ML) and artificial intelligence (AI) has increased greatly, and the study of crystal nucleation has been one of the beneficiaries. In this review, we outline how ML and AI have been applied to address four outstanding difficulties of crystal nucleation: how to discover better reaction coordinates (RCs) for describing accurately non-classical nucleation situations; the development of more accurate force fields for describing the nucleation of multiple polymorphs or phases for a single system; more robust identification methods for determining crystal phases and structures; and as a method to yield improved course-grained models for studying nucleation.

The current status of nanoindentation apparatus and the requirements for extension to more than one dimension of loading is described. It is possible, though not trivial, to adequately characterise the stiffnesses and couplings present in a frictional contact and thus expand the present use of nanoindentation to important new areas. The example of static friction is discussed to show that complete machine characterisation is required if true interface mechanical properties and friction coefficients are to be correctly measured.

The solution of instrumented indentation inverse problems by physically-based models still represents a complex challenge yet to be solved in metallurgy and materials science. In recent years, Machine Learning (ML) tools have emerged as a feasible and more efficient alternative to extract complex microstructure-property correlations from instrumented indentation data in advanced materials. On this basis, the main objective of this review article is to summarize the extent to which different ML tools have been recently employed in the analysis of both numerical and experimental data obtained by instrumented indentation testing, either using spherical or sharp indenters, particularly by nanoindentation. Also, the impact of using ML could have in better understanding the microstructure-mechanical properties-performance relationships of a wide range of materials tested at this length scale has been addressed.

The analysis of the recent literature indicates that a combination of advanced nanomechanical/microstructural characterization with finite element simulation and different ML algorithms constitutes a powerful tool to bring ground-breaking innovation in materials science. These research means can be employed not only for extracting mechanical properties of both homogeneous and heterogeneous materials at multiple length scales, but also could assist in understanding how these properties change with the compositional and microstructural in-service modifications. Furthermore, they can be used for design and synthesis of novel multi-phase materials.

The quest for novel materials used in technologies demanding extreme performance has been accelerated by advances in computational materials screening, additive manufacturing routes, and characterization probes. Despite tremendous progress, the pace of adoption of new materials has still not met the promise of global initiatives in materials discovery. This challenge is particularly acute for structural materials with thermomechanical and environmental demands whose performance depends on microstructure as well as material composition. In this prospective article, we review advances in high-throughput mechanical testing, and the associated specimen fabrication, materials characterization, and modeling tasks that show promise for acceleration of the materials development cycle. We identify a critical need to develop rapid testing and characterization strategies that faithfully reproduce design-relevant properties and circumvent the time and expense of conventional high fidelity testing. We identify small-scale mechanical testing workflows that can incorporate real-time decision making based on feedback from multimodal characterization and computational modeling. These workflows will require site-specific specimen fabrication procedures that are agnostic to the synthesis route and have the ability to modulate microstructure and defect characteristics. We close our review by conceptualizing a fully integrated high-throughput testing platform that addresses the speed-fidelity tradeoff in pursuit of a design-relevant suite of properties for new materials.

Lithium metal is regarded as one of the most ideal anode materials for next-generation batteries, due to its high theoretical capacity of 3860 mAh g⁻¹ and low redox potential (−3.04 V vs standard hydrogen electrode). However, practical applications of lithium anodes are impeded by the uncontrollable growth of lithium dendrite and continuous reactions between lithium and electrolyte during cycling processes. According to reports for decades, artificial solid electrolyte interface (SEI), electrolyte additives, and construction of three-dimensional (3D) structures are demonstrated essential strategies. Among numerous approaches, metals that can alloy with lithium have been employed to homogenize lithium deposition and accelerate Li ion transportation, which attract more and more attention. This review aims to summarize the lithium alloying applied in lithium anodes including the fabricating approaches of alloy-containing lithium anodes, and the action mechanism and challenges of fabricated lithium anodes. Based on summarizing the literature, shortcomings and challenges as well as the prospects are also analyzed, to impel further research of lithium anodes and lithium-based batteries.

Swarming is a collective bacterial behavior in which a dense population of bacterial cells moves over a porous surface, resulting in the expansion of the population. This collective behavior can guide bacteria away from potential stressors such as antibiotics and bacterial viruses. However, the mechanisms responsible for the organization of swarms are not understood. Here, we briefly review models that are based on bacterial sensing and fluid mechanics that are proposed to guide swarming in the pathogenic bacterium Pseudomonas aeruginosa. To provide further insight into the role of fluid mechanics in P. aeruginosa swarms, we track the movement of tendrils and the flow of surfactant using a novel technique that we have developed, Imaging of Reflected Illuminated Structures (IRIS). Our measurements show that tendrils and surfactants form distinct layers that grow in lockstep with each other. The results raise new questions about existing swarming models and the possibility that the flow of surfactants impacts tendril development. These findings emphasize that swarm organization involves an interplay between biological processes and fluid mechanics.

Liquid crystalline elastomers (LCEs) have demonstrated tremendous potential in applications such as soft robotics, biomedical materials, electronics, sensors, and biomimetic systems. The physical properties of LCEs are controlled by the degree of crosslinking, nature of the mesogens, and mesogen orientation in the LCE network structure. A wide range of dynamic covalent bonds (DCBs) capable of dynamic bond exchange reactions (DBERs) have been introduced into LCE structures to obtain intelligent materials in recent decades. In this review article, we discuss the molecular constitution, macrostructure, morphing mechanism, recent advances in LCEs with dynamic covalent bonds, the influence of DCBs on self-healing, reprogramming and reprocessing properties of LCE actuators, and challenges and opportunities in incorporating dynamic chemistry in the field of LCE actuators.

The Gibson-Ashby (G-A) model has been instrumental in the design of additively manufactured (AM-ed) metal lattice materials or mechanical metamaterials. The first part of this work reviews the proposition and formulation of the G-A model and emphasizes that the G-A model is only applicable to low-density lattice materials with strut length-to-diameter ratios greater than 5. The second part evaluates the applicability of the G-A model to AM-ed metal lattice materials and reveals the fundamental disconnections between them. The third part assesses the deformation mechanisms of AM-ed metal lattices in relation to their strut length-to-diameter ratios and identifies that AM-ed metal lattices deform by concurrent bending, stretching, and shear, rather than just stretching or bending considered by the G-A model. Consequently, mechanical property models coupling stretching, bending and shear deformation mechanisms are developed for various lattice materials, which show high congruence with experimental data. The last part discusses new insights obtained from these remedies into the design of strong and stiff metal lattices. In particular, we recommend that the use of inclined struts be avoided.

High-entropy materials provide a versatile platform for the rational design of novel candidates with exotic performances. Recently, it has been demonstrated that high-entropy ceramics (HECs), depending on their compositions, show great application potential because of their superior structural and functional properties. However, the immense phase space behind HECs significantly hinders the efficient design and exploitation of high-performance HECs through traditional trial-and-error experiments and expensive ab-initio calculations. Machine learning (ML), on the other hand, has become a popular approach to accelerate the discovery of HECs and screen HECs with exceptional properties. In this article, we review the recent progress of ML applications in discovering and designing novel HECs, including carbides, nitrides, borides, and oxides. We thoroughly discuss different ingredients that are involved in ML applications in HECs, including data collection, feature engineering, model refinement, and prediction performance improvement. We finally provide an outlook on the challenges and development directions of future ML models for HEC predictions.

The superplastic behavior of medical magnesium alloys is reviewed in this overview article. Firstly, the basics of superplasticity and superplastic forming via grain boundary sliding (GBS) as the main deformation mechanism are discussed. Subsequently, the biomedical Mg alloys and their properties are tabulated. Afterwards, the superplasticity of biocompatible Mg-Al, Mg-Zn, Mg-Li, and Mg-RE (rare earth) alloys is critically discussed, where the influence of grain size, hot deformation temperature, and strain rate on the tensile ductility (elongation to failure) is assessed. Moreover, the thermomechanical processing routes (e.g. by dynamic recrystallization (DRX)) and severe plastic deformation (SPD) methods for grain refinement and superplasticity in each alloying system are introduced. The importance of thermal stability (thermostability) of the microstructure against the grain coarsening (grain growth) is emphasized, where the addition of alloying elements for the formation of thermally stable pinning particles and segregation of solutes at grain boundaries are found to be major controlling factors. It is revealed that superplasticity at very high temperatures can be achieved in the presence of stable rare-earth intermetallics. On the other hand, the high-strain-rate superplasticity and low-temperature superplasticity in Mg alloys with great potential for industrial applications are summarized. In this regard, it is shown that the ultrafine-grained (UFG) duplex Mg-Li alloys might show remarkable superplasticity at low temperatures. Finally, the future prospects and distinct research suggestions are summarized. Accordingly, this paper presents the opportunities that superplastic Mg alloys can offer for the biomedical industries.