Current Opinion in Solid State & Materials Science最新文献

A dynamic convergence between metaphotonics and artificial intelligence (AI) is underway. In this review, AI is conceptualized as a tool for mapping input and output data. From this perspective, an analysis is conducted on how input and output data are set, aiming to discern the following three key trends in the utilization of AI within the field of metaphotonics. 1. The advancement of forward modeling and inverse design, utilizing AI for mapping metaphotonic device design and the corresponding optical properties. 2. Optical neural networks (ONNs), an emerging field that implements AI using metaphotonics by processing information within electromagnetic waves. 3. The field of metasensors, employing metamaterials to encode optical information for measurement and processing using AI to demonstrate high performance sensing. We round up the review with our perspectives on AI and metaphotonics research and discuss the future trends, challenges, and developments.

Soft wearable strain sensors with mechanically invisible interactions with skin tissue have enabled precise diagnosis and effective treatment of neurological movement disorders in a closed-loop manner that quantitatively measures motion-related strains without noise intervention and provides feedback information. Because of the immediate interpretation from motion-driven sign language to general conversation, such on-skin strain sensors have recently been considered promising candidates for facilitating communication either within deaf and hard-of-hearing communities or among people with disabilities. Despite advances in soft strain sensors, the lack of intrinsically stretchable neuromorphic modules that mimic biological synapses and efficiently perform neural computation and dynamics has resulted in inaccurate translation of sign language. In this study, we present an intrinsically stretchable organic electrochemical transistor (is-OECT) synapse integrated with crack-based strain sensors conformally mounted onto fingers to implement an interactive sensory-neuromorphic system (iSNS) capable of overcoming auditory impediments. The is-OECT synapse in the iSNS shows stable electrical performance (a large number of states (∼100 states) and a linear weight update) in the skin deformation range (approximately 30%). Based on pre-trained data gathered from on-finger strain-sensing information, the iSNS wirelessly translates sign language while maintaining high accuracy.

The extreme consumption of non-renewable energy sources poses serious concerns of environment pollution and energy crisis across the globe, which stimulate the research on exploration of alternative energy technologies capable of harvesting available energy in the ambient environment. Mechanical energy is ubiquitously available in the ambient environment, which can be converted into electrical energy using piezoelectric energy harvesters (PEH) based on piezoelectric effect. PEH have evolved as a non-conventional, feasible and clean solution to meet energy requirement worldwide and played an important role in powering of several portable electronic devices, wireless sensor nodes, and medical implants. PEH enables self-powered functioning of devices along with a longer lifespan. The merits of this technology lies in its easy implementation, miniaturization, and high energy conversion efficiency. The utilization of waste mechanical energy available from the human body (e.g., natural movements of humans) in piezoelectric energy harvesters is one of the prime interests of researchers. The footwear equipped with piezoelectric material is one such novel innovation in the area of piezoelectric energy harvesting which utilizes the vibration generated during human body movements, thereby converting direct mechanical impacts into useful energy. This review article starts with providing the basic fundamental information on piezoelectric effect, piezoelectric materials and piezoelectric energy harvesting technology. The prime objective of this article is to provide the comprehensive review of recent developments made in designing footwear prototypes for piezoelectric energy harvesting and their emerging applications. Interestingly, this review also discusses the important patented technologies based on piezoelectric footwear energy harvesting. At last, this review discusses the merits and limitations of available footwear prototypes for piezoelectric energy-harvesting and provides the new directions for researchers in this innovative area of energy harvesting.

In-situ mechanical testing in a scanning electron microscope (SEM) equipped with an electron backscatter diffraction (EBSD) system has quickly gained popularity, particularly because of its rich experimental outcomes. In this work, the advantages and challenges of this approach are systemized and critically discussed in relation to testing irradiated metallic materials and novel materials in development. Key observations and experimental results are evaluated for irradiated austenitic stainless steels, an additively manufactured (AM) 316 stainless steel, and a modern accident-tolerant FeCrAl alloy. Various deformation mechanisms are discussed using experimental EBSD datasets, including dislocation channeling in irradiated alloys, strain localization, lattice rotation, texture development, twinning, phase instability, and microfracture events. Several rare strain-induced phenomena are described, such as grain boundary dissolution in FeCrAl alloy and twinning boundary migration in AM 316 stainless steel. These results demonstrate the advantages and capability of EBSD-assisted experiments to inform assessment and understanding of the complexity of deformation processes at different microstructure scales. Some challenges and impediments associated with this approach are also discussed, along with recommendations for future research advancements.

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.

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.

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.