Current Opinion in Solid State & Materials Science最新文献

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.

Conformal lattice materials (cell sizes ranging from nanometres to millimetres), including conformal metal lattice metamaterials, are cellular materials or structures that conform to all or part of the physical space of a product with topologically complete boundary cells. Enabled by powder bed fusion (PBF) additive manufacturing (AM), conformal metal lattice metamaterials provide an innovative solution for lightweight engineering or integration of structure and function. A key step in their fabrication is to generate a conformal lattice model suitable for PBF AM. This research reviews their design methods and evaluates each method using seven criteria. These include (i) the sequence of geometric modelling and lattice topology generation (sequential or simultaneous), (ii) integrity of lattice cell topology at boundaries, (iii) compatibility with lattice cell types, (iv) applicability to design geometry, (v) ease of coding, (vi) accessibility via common software tools, and (vii) ability to define strut inclination angles in a complex conformal design space. On this basis, various laser PBF (LPBF) manufacturability issues of conformal metal lattices are considered, and two Ti-6Al-4V conformal lattices are fabricated using LPBF and evaluated. This review provides a necessary foundation for future research and applications of conformal lattice metamaterials in various engineering fields.

In this modern era, the necessity of a safe environment with a swift detection of even minute concentrations of hazardous and combustible gases has spurred significance in the advancement of gas sensor technology. In this aspect, the room temperature operable gas sensors have marked their importance by ensuring the safe detection of combustible gases. Nonetheless, the incomplete recovery of such gas sensors requires thermal activation, which entails several limitations. Therefore, the light-activation of gas sensors has garnered considerable attention owing to its compactness and cost-effective operations. The light-activation generates the electron-hole pairs which activate the sensing surface and modulate the charge carrier concentration, thereby enhancing the gas-sensing performances. In this review, the gas-sensing performances of various photoactive sensing materials including metal oxides and two-dimensional materials under light irradiation have been discussed. The gas sensors based on metal oxide and two-dimensional materials have shown significant performance in terms of response, as well as sharp response and recovery times under both ultra-violet and visible light illumination. Finally, this review emphasizes the challenges and future scopes associated with the light-activated room temperature operable gas sensors, which could lead a pathway toward the development of an ultrafast gas sensor.

Ligands are the key to almost any strategy in the assembly of programmable nanocrystals (or nanoparticles) and must be accurately considered in any predictive model. Hard Spheres (or Shapes) provide the simplest and yet quite successful approach to assembly, with remarkable sophisticated predictions verified in experiments. There are, however, many situations where hard spheres/shapes predictions fail. This prompts three important questions: In what situations should hard spheres/shapes models be expected to work? and when they do not work, Is there a general model that successfully corrects hard sphere/shape predictions? and given other successful models where ligands are included explicitly, and of course, numerical simulations, can we unify hard sphere/shape models, explicit ligand models and all atom simulations?. The Orbifold Topological Model (OTM) provides a positive answer to these three questions. In this paper, I give a detailed review of OTM, describing the concept of ligand vortices and how it leads to spontaneous valence and nanoparticle “eigenshapes” while providing a prediction of the lattice structure, without fitting parameters, which accounts for many body effects not captured by (two-body) potentials. I present a thorough survey of experiments and simulations and show that, to this date, they are in full agreement with the OTM predictions. I conclude with a discussion on whether NC superlattices are equilibrium structures and some significant challenges in structure prediction.

Ultra-wide bandgap semiconductors, with bandgaps greater than 3.5 eV, have immense potential in power-switching electronic applications and ultraviolet light emitters. But the development of these materials faces a number of challenges, many of which relate to controlling electrical conductivity. In this work, we review the major obstacles for a set of these materials (focusing on AlGaN, AlN, BN, Ga₂O₃, Al₂O₃, and diamond) including limitations in n- and p-type doping and the effects of impurities and native point defects. We present an in-depth discussion on ultra-wide-bandgap nitride and oxide semiconductors, which face several similar challenges, as well as diamond, which presents a more unique scenario. The biggest obstacle for these semiconductors is attaining bipolar electrical conductivity, which means achieving both n-type and p-type conductivity within the same material. Toward this end, we also discuss potential future research directions that may lead to the development of bipolar ultra-wide bandgap semiconductor devices.