Current Opinion in Solid State & Materials Science最新文献

Graphene-based materials such as graphene oxide (GO) have demonstrated extraordinary sensitivity towards water molecules due to the hydrophilic nature. The hydrophilicity of GO can be further improved via additional functionalization. Previous studies suggest that the interaction between GO and water molecules results in the formation of a hydrogen bond network and modifies the interlayer structure of GO laminates. Based on the recent developments, we present our opinion on the interaction between moisture and graphene oxide and how this interaction can be utilized for environmental applications such as moisture detection and atmospheric water harvesting.

Stress granules (SGs) are non-membranous organelles driven by the liquid–liquid phase separation (LLPS) of RNA and RNA-binding proteins under various stress conditions. LLPS is mediated by multivalent interactions and affected by RNA modifications and their binders. Most neurodegenerative disease (ND)-related proteins, including TDP-43, FUS, Tau, and TIA1, are components of SGs, indicating the involvement of SGs in ND initiation or progression. Recent studies have reported the enrichment of N⁶-methyladenosine (m⁶A)-modified RNA and its corresponding reader proteins in SGs and the abnormal deposition of m⁶A-modified RNA in ND. Therefore, there is urgent to determine the crosstalk and underlying mechanisms between m⁶A modification and SGs. The main questions that must be answered are as follows: (1) Which reader participates in m⁶A enrichment in SGs? (2) What is the role of m⁶A modification in SG formation? How does it promote LLPS? (3) What is the role of SGs in regulating the fate of m⁶A-modified RNA? (4) Does the interplay between SGs and m⁶A modification contribute to chronic diseases such as ND? Therefore, based on these questions, we summarized recently published literature and tried to provide a comprehensive view of the interplay between SGs and m⁶A modification and their contribution to ND.

Ion irradiation and implantation have wide applications that demand accurate determination of displacement damage profile and distribution of implanted ion concentration. The prediction of vacancies is especially important to determine displacements per atom (dpa), the standard parameter of primary radiation damage in materials. However, significant discrepancies exist in estimations of vacancies between full-cascade (F-C) and quick calculation (Q-C) options in the popular computer code SRIM. This study inspected the SRIM code and a relatively new code called Iradina, which uses a similar methodology, to develop an understanding of the origin of vacancy overestimation in the F-C options for SRIM and Iradina. We found that the default values of thresholds (namely final energy in SRIM and replacement energy in Iradina) in displacement production calculations results in excessively large number of calculated vacancies and very few replacements. After conducting multiple calculations using SRIM, Iradina, and MARLOWE (all based on the binary collision approximation), a comparison of the results indicates that there is a shortcoming in the SRIM and Iradina F-C methodology for treating near-threshold collisions. This issue is responsible for the deficiency of replacements and excess of calculated vacancies in the SRIM and Iradina F-C results. Drawing on the principles of collision physics, we propose recommendations for modifying the source codes to address these issues.

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.