Current Opinion in Solid State & Materials Science最新文献

High-Speed Nanoindentation Mapping (HSNM) has been recently developed and established as a novel enabling technology for fast and reliable assessment of small-scale mechanical properties of heterogeneous materials over large areas. HSNM allows for one complete indentation cycle per second, including approach, contact detection, load, unload, and movement to the n^th indent location, thus enabling high-resolution, spatially resolved hardness (H) and elastic modulus (E) mapping.

This article reviews the recent advancements in HSNM and its application to support the design, synthesis, and characterization of advanced materials, potentially impacting the ongoing digital and green transitions. A comprehensive review is given of (a) the main experimental features and critical issues of the protocols in comparison with traditional quasi-static nanoindentation, (b) the advanced data analysis tools employed, and (c) the combination with other microscopy and spectroscopy methods for multi-technique correlative applications. Finally, the relevance of HSNM for selected classes of materials is discussed, including (i) additively manufactured metals, (ii) advanced alloys, (iii) composite materials and cement, highlighting the potential for matrix-reinforcement mechanical characterization and optimization routes, (iv) coatings for industrial components and energy/transportation, discussing damage progression identification at the micro-structural level, and (v) natural materials. Ultimately, future perspectives are presented and discussed.

Polypeptides obtained from the ring-opening polymerization of N-carboxyanhydrides, as the synthetic analogues of natural proteins, have drawn broad interests during the recent three decades. Unlike other synthetic polymers, polypeptides form ordered secondary structures like α-helices and β-sheets, which offer conformation-specific functions that are not observed in unstructured polymers. In this article, we summarized the unique structural features of α-helical polypeptides compared to their random-coiled analogues, and reviewed the helix-associated assembly behaviors and biomedical functions based on the structural differences. In addition, the characterization and modulation of polypeptide conformations were also discussed. We believe this review will shed light on the future design of synthetic polypeptides with helix-specific properties, further expanding the scope of polypeptide materials.

Hydrogen is considered as a primary energy carrier for the hydrogen economy. However, hydrogen embrittlement (HE) is an inescapable problem that needs to be solved because metals, particularly steels, are commonly used in the transportation and storage of hydrogen, and because HE occurs in high-performance structural components in contact with moisture or hydrogen. In particular, HE concerns of additively produced alloys should be addressed, because additive manufacturing (AM) can provide significant advantages in the manufacturing of such structural components. This review overviews the recent research progress in HE of metals fabricated using AM. This review introduces AM and HE and summarises and discusses (i) the factors that influence the HE of AM metals, (ii) possible mechanisms of HE, (iii) the differences and similarities of HE behaviour between metals processed by AM and those produced through conventional manufacturing processes, and (iv) the current challenges and research gaps of HE in AM metals. The review covers structural steels, titanium alloys, tool steels, nickel-based superalloys, stainless steels and high-entropy alloys.

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 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.