Current Opinion in Solid State & Materials Science最新文献

Nanoindentation based techniques were significantly enhanced by continuous stiffness monitoring capabilities. In essence, this allowed to expand from point-wise discrete measurement of hardness and elastic modulus towards advanced plastic characterization routines, spanning the whole rate-dependent spectrum from steady state creep properties via quasi static flow curves to impact or brittle fracture. While representing a significant step forwards already, these techniques can tremendously benefit from additional or complementary input provided by in situ or operando experiments. In fact, by combining and merging these approaches, impressive advances were made towards well controlled nanomechanical investigations at various non-ambient conditions. Here we will discuss some novel experimental avenues facilitated by deliberate extreme environments, and also indicate how future improvements and enhancements will potentially provide previously unseen insights into fundamental material behavior at extreme conditions.

As a common thermomechanical treatment route, “cold rolling and annealing” is widely used for the processing and grain refinement of interstitial-containing high-entropy alloys (HEAs). The interrelationship between the parameters of this process, the content of interstitial elements, and their interactions are outstanding challenges and areas of open discussion. Accordingly, the data-driven machine learning approach is a favorable choice for tuning the microstructure and mechanical properties, which needs to be systematically investigated. In the present work, these subjects were addressed in terms of correlating the thermomechanical processing parameters and chemical composition with the recrystallization and grain growth behaviors, grain size, carbide precipitation, and the resulting tensile yield stress for the model (CrMnFeCoNi)_100-xC_x HEAs. For this purpose, machine learning models based on adaptive neuro-fuzzy inference system (ANFIS), backpropagation artificial neural network (BP-ANN), and support network machine (SVM), as well as mathematical relationships and equations for the contribution of each strengthening mechanism were proposed and verified by extensive experimental work, which shed light on the design and prediction of the microstructure and properties of HEAs.

Amorphous oxide semiconductors (AOSs) have exceptional features of high visible transparency, high carrier mobility, excellent uniformity, and low-temperature growth process, making them promising in the electronic and information industry. InGaZnO is the most widely studied AOS and has been applied in commercial, which, however, contains rare and precious indium. For sustainable development, a diversity of In-free AOSs have been designed and proposed, which are attracted more and more attention. There have been several reviews on AOSs mainly centred on InGaZnO; in contrast, the review on In-free AOSs is not available at present. In this work, we provide a comprehensive review on In-free AOSs from fundamental properties to practical applications. Various In-free AOSs available in literatures are introduced, with the focus on ZnSnO-based AOSs. Thin-film transistors (TFTs) based on In-free AOSs are investigated in detail, which are the key device for next-generation transparent and flexible displays. Also, the applications in transparent electrodes, sensors, memristors, synaptic devices, and circuits are introduced. This review is expected to provide a guide to well understand the state-of-the-art principles, materials, devices, fabrication, applications, and perspectives of In-free AOSs.

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 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.