Current Opinion in Solid State & Materials Science最新文献

The understanding of when a thin film is two-dimensional (2D) varies throughout the literature. It was introduced by advances in nanotechnology that allowed the fabrication of structures that are in the nm scale in one dimension. More recently, materials with atomic thickness, such as graphene and other van der Waals materials, allowed us to isolate structures that have reached the ultimate limit of thickness. Their layered structures allow a straightforward identification of the monolayers as 2D structures. Today, 2D structures are reported from a wide class of materials ranging from molecules to thin non-van der Waals layers, generating interest across a large variety of scientific fields. The thickness of these reported 2D films varies from atomic scale to several tens or even hundreds of nm. This puzzling occurrence of several hundred nm thick ‘2D materials’ calls for a critical assessment of when thin films are present as 2D. Here, we explore aspects such as atomic and electronic structure, chemical bonding, composition, and the relation of bulk-to-thin film characteristics to find criteria that describe 2D structures. With that, we aim to fuel an interdisciplinary dialogue towards establishing clear definitions for when a thin film is a 2D structure.

Ferroelectric and two-dimensional (2D) materials are both heavily investigated classes of electronic materials. This is unsurprising since they both have superlative fundamental properties and high-value applications in computing, sensing etc. In this Perspective, we investigate the research topics where 2D semiconductors and ferroelectric materials both in 2D or 3D form come together. 2D semiconductors have unique attributes due to their van der Waals nature that permits their facile integration with any other electronic or optical materials. In addition, the emergence of ferroelectricity in 2D monolayers, multilayers, and artificial structures offers further advantages since traditionally ferroelectricity has been difficult to achieve in highly thickness scaled materials. Further, we elaborate on the applications of 2D materials + ferroelectricity in non-volatile memory devices, highlighting their potential for in-memory computing, neuromorphic computing, optoelectronics, and spintronics. We also suggest the challenges posed by both ferroelectrics and 2D materials, including material/device preparation and reliable characterizations, to drive further investigations at the interface of these important classes of electronic materials.

High-entropy alloys (HEAs) have become an important topic in modern materials science due to their exceptional properties. Despite their attractive properties, achieving a superior strength-ductility synergy has been, and remains, a major challenge. In practice, overcoming the strength-ductility trade-off in HEAs is an overriding priority which may open the opportunity for the development of high-performance alloys. It is well-established that high-strength steels benefitted from metastability engineering by manipulating the deformation mechanisms to facilitate a deformation-induced martensitic transformation which provides acceptable ductility. Accordingly, and following this same approach, a metastable HEA was developed which exhibited a desirable combination of strength and ductility. This review is designed specifically to give a comprehensive description of the deformation mechanisms in these materials and to provide an overall perspective on the importance of material characteristics and processing variables. The discussion is centred for different HEAs on the significance of the transformation-induced plasticity in breaking the strength-ductility trade-off and thereafter to examine some challenges and research gaps which require future attention. The understanding of the HEAs achieved to date demonstrates that there is a large potential for the future enhancement and optimization of these alloys in developing high-performance materials for a wide range of applications.

High-quality, large-scale graphene holds significant potential for future electronic applications because of its exceptional properties. Among the various graphene production methods, chemical vapor deposition (CVD) has emerged as a promising approach for the industrial-scale fabrication of electronic-grade graphene films. Although large-area graphene films are being produced using advanced variants of conventional CVD systems, their quality can be further improved. In the past decade, significant progress has been made in the CVD-based fabrication of large-area, high-quality graphene, driven by strategies for controlling growth parameters such as the heating mode in CVD, graphene nucleation density, and crystal orientation of the growth substrate. In this review, we present key findings on the CVD-based production of large-area, high-quality graphene using established strategies, and highlight the advantages and challenges. Additionally, we introduce a novel approach to growing high-quality graphene based on recrystallization—the use of a mobile hot-wire CVD system that can provide localized heat energy in a dynamic manner. We cover various synthesis strategies that leverage this system to induce changes in graphene properties and explore their potential applications. Finally, based on a comprehensive understanding of the corresponding growth mechanisms, we offer insights into the CVD-based synthesis of large-area, high-quality graphene films and examine its prospects.

Wearable strain sensors are emerging as promising devices for monitoring human motions and physiological signals in various fields, such as healthcare, robotics, and sports. Among various materials, polymer–graphene nanocomposites (PGNs) have attracted considerable attention due to their excellent mechanical, electrical, and thermal properties, as well as their facile fabrication methods. This review summarised the recent progress and challenges of PGN-based wearable strain sensors for physiological signal monitoring. First, the classification of PGNs based on the structural derivatives of graphene (such as graphene sheets, graphene oxide, reduced graphene oxide, and graphene quantum dots) and the strain sensing mechanisms (such as resistive and capacitive) were introduced. Then, we discussed the fabrication approaches of PGN-based strain sensors, including solution processing, melt blending, in-situ polymerization, spinning, printing, and coating. Afterward, this article highlighted the functional PGN-based strain sensors using various polymers and their applications in monitoring subtle and significant physiological signals. Finally, this work identified the underlying challenges and future perspectives of PGN-based wearable strain sensors for accurate and reliable physiological signal monitoring. This review provides a comprehensive overview of the current state-of-the-art of PGN-based wearable strain sensors and inspires further research in this field.

The ever increasing demand for computational power combined with the predicted plateau for the miniaturization of existing silicon-based technologies has made the search for low power alternatives an industrial and scientifically engaging problem. In this work, we explore spintronics-based Ising machines as hardware computation accelerators. We start by presenting the physical platforms on which this emerging field is being developed, the different control schemes and the type of algorithms and problems on which these machines outperform conventional computers. We then benchmark these technologies and provide an outlook for future developments and use-cases that can help them get a running start for integration into the next generation of computing devices.

Neutron scattering is widely used in a variety of disciplines. Neutrons differ from other structural probes such as X-rays and electrons in that they are neutral, have deep penetration ability, and have high sensitivity to light elements. These characteristics afford neutron based probes unique advantages for investigating the structure and structural evolution in chemical, polymeric, and biological systems, especially in systems where hydrogen is enriched. Moreover, the range of energy and scattering vector accessible to neutrons are consistent with the natural time and length scales of these materials. This review will demonstrate recent applications of both elastic and inelastic/quasi-elastic neutron scattering (IE/QENS). The current capabilities and characteristics of techniques such as small angle neutron scattering (SANS), ultra-small angle neutron scattering (USANS), spin echo small angle neutron scattering (SESANS), neutron diffraction will be reviewed via examples. IE/QENS such as triple-axis spectrometer (TAS), neutron spin echo (NSE), and neutron backscattering spectrometer (BSS) will be introduced as well. Moreover, we will also review the use of instrumentation with recent defining examples around the world as well as on the neutron scattering platform of 20 MW China Mianyang Research Reactor (CMRR).

Over the past 2 decades, the computational materials science community has made great advances in facilitating and supporting the development of new materials, particularly metallic alloys. While the materials community now has impactful computational tools, from Calculation of Phase Diagrams (CALPHAD) methods for computing phase diagrams, to density functional theory (DFT) for computing certain properties of individual phases, to Artificial Intelligence (AI) and Machine Learning (ML) to accelerate computational discoveries, experimental validation methods, in any high-throughput methodology, has been lacking. Metallic alloy synthesis has remained incredibly slow owing to traditional methods, such as arc-melting methods, remaining a one-off approach, which each individual sample requiring a separate sample preparation and characterization process, little if any of which is automated. To overcome these limitations, the High-Throughput Rapid Experimental Alloy Development (HT-READ) platform was developed. The HT-READ platform is a true paradigm change in the field of metallic alloy development, enabling fully automated synthesis and characterization of alloy samples in groups of 16 samples at once. The enabling feature of the HT-READ platform approach is the use of a single sample, with up to 16 individual alloy ‘spokes’ comprising a ‘wagon-wheel’ geometry. This geometry directly enables the automation of each of the characterization steps that can proceed without instrument operation by a trained engineer. In spite of the significant advantages of the HT-READ platform, the rate controlling step remains the physical weighing of the alloy powders used in the 3-D printing of the individual spokes of the ‘wagon-wheel’ sample. In the newly updated HT-READ platform, the powder handling and weighting process has now been automated using a ChemSpeed™ Doser, which can dispense up to 24 different powders, which might be needed to achieve the desired composition for each of the 16-spoke samples. With the Updated HT-READ platform, it is now possible to achieve truly high-throughput of metallic alloy development, with automated characterization across multiple instruments, from GDS, XRD, SEM-EDS, SEM-EBSD, microhardness, and nanoindentation.

Composed of a large number of artificial nanostructures, metasurfaces have found applications in metalenses, structured light generation and optical deflectors through wavefront shaping. After careful design according to optical requirements, metasurfaces can achieve independent functions under different incident light conditions. Deep learning emerges as a transformative design approach in nanophotonics, providing nanostructures tailored to various optical requirements. A statistic relationship between geometric shapes and optical properties is hidden in massive nanostructures. The relationship is learned without any help of physical models, opening a possibility for further research on multifunctional metasurface. Here, different optical dimensions multiplexed in metasurfaces are reviewed, and combining these multiplexing methods into one metasurface can significantly increase functional channels. Then different types of neural networks applied in metasurface design are introduced, opening a possibility to combine the various optical multiplexing. Furthermore, the constructive suggestions are provided on multifunctional metasurface designed by deep learning, and specific opinions on future developments are discussed.

Metamaterials are specially engineered materials distinguished by their unique properties not typically seen in naturally occurring materials. However, the challenge lies in achieving lightweight yet mechanically rigid architectures, as these properties are sometimes conflicting. For example, buckling strength is a critical property that needs to be enhanced since buckling can cause catastrophic failure of the lightweight metamaterials. In this study, we introduce a generative machine learning based approach to determine the superior geometries of metamaterials to maximize their buckling strength without compromising their elastic modulus. Our results, driven by machine learning based design, remarkably enhanced buckling strength (over 90 %) compared to conventional metamaterial designs. The simulation results are validated by a series of experimental testing and the mechanism of the high buckling strength is elucidated by correlating stress field with the metamaterial geometry. Our results provide insights into the interplay between shape and buckling strength, unveiling promising avenues for designing efficient metamaterials in future applications.