Current Opinion in Solid State & Materials Science最新文献

Machine learning opens up a new avenue for advancing the development of phononic crystals and elastic metamaterials. Numerous learning models have been employed and developed to address various challenges in the field of phononic metamaterials. Here, we provide an overview of mainstream machine learning models applied to phononic metamaterials, discuss their capabilities as well as limitations, and explore potential directions for future research.

Deep learning is currently being hyped as an almost magical tool for solving all kinds of difficult problems that computers have not been able to solve in the past. Particularly in the fields of computer vision and natural language processing, spectacular results have been achieved. The hype has now infiltrated several scientific communities. In (nano-) photonics, researchers are trying to apply deep learning to all kinds of forward and inverse problems. A particularly challenging problem is for instance the rational design of nanophotonic materials and devices. In this opinion article, I will first discuss the public expectations of deep learning and give an overview of the quite different scales at which actors from industry and research are operating their deep learning models. I then examine the weaknesses and dangers associated with deep learning. Finally, I’ll discuss the key strengths that make this new set of statistical methods so attractive, and review a personal selection of opportunities that shouldn’t be missed in the current developments.

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.

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.

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.

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.

As a rapid, inexpensive prototyping and production methodology, additive manufacturing was widely employed for viral diagnosis platforms during the COVID-19 pandemic. Multiple printing methods were utilized including screen printing, aerosol jet printing, 3D printing, and wax printing to develop nanomaterial sensors designed to detect SARS-CoV-2. In this Review, the advantages, and challenges of each of these printing methods are delineated in addition to optimal nanomaterial ink formulations and printing parameters. Furthermore, surface modification schemes are discussed due to their importance in enhancing chemical functionality, electrical and electrochemical performance, and ultimately the sensitivity and selectivity of the final sensing platform. Along with surveying the latest published results, this Review summarizes remaining open questions that will help guide research aimed at ensuring a more effective response to future pandemics.

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.

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.