Current Opinion in Solid State & Materials Science最新文献

For over a decade, machine learning (ML) models have been making strides in computer vision and natural language processing (NLP), demonstrating high proficiency in specialized tasks. The emergence of large-scale language and generative image models, such as ChatGPT and Stable Diffusion, has significantly broadened the accessibility and application scope of these technologies. Traditional predictive models are typically constrained to mapping input data to numerical values or predefined categories, limiting their usefulness beyond their designated tasks. In contrast, contemporary models employ representation learning and generative modeling, enabling them to extract and encode key insights from a wide variety of data sources and decode them to create novel responses for desired goals. They can interpret queries phrased in natural language to deduce the intended output. In parallel, the application of ML techniques in materials science has advanced considerably, particularly in areas like inverse design, material prediction, and atomic modeling. Despite these advancements, the current models are overly specialized, hindering their potential to supplant established industrial processes. Materials science, therefore, necessitates the creation of a comprehensive, versatile model capable of interpreting human-readable inputs, intuiting a wide range of possible search directions, and delivering precise solutions. To realize such a model, the field must adopt cutting-edge representation, generative, and foundation model techniques tailored to materials science. A pivotal component in this endeavor is the establishment of an extensive, centralized dataset encompassing a broad spectrum of research topics. This dataset could be assembled by crowdsourcing global research contributions and developing models to extract data from existing literature and represent them in a homogenous format. A massive dataset can be used to train a central model that learns the underlying physics of the target areas, which can then be connected to a variety of specialized downstream tasks. Ultimately, the envisioned model would empower users to intuitively pose queries for a wide array of desired outcomes. It would facilitate the search for existing data that closely matches the sought-after solutions and leverage its understanding of physics and material-behavior relationships to innovate new solutions when pre-existing ones fall short.

Fluorescence lifetime imaging microscopy (FLIM) is a powerful imaging tool offering molecular specific insights into samples through the measurement of fluorescence decay time, with promising applications in diverse research fields. However, to acquire two-dimensional lifetime images, conventional FLIM relies on extensive scanning in both the spatial and temporal domain, resulting in much slower acquisition rates compared to intensity-based approaches. This problem is further magnified in three-dimensional imaging, as it necessitates additional scanning along the depth axis. Recent advancements have aimed to enhance the speed and three-dimensional imaging capabilities of FLIM. This review explores the progress made in addressing these challenges and discusses potential directions for future developments in FLIM instrumentation.

Spintronic logic devices require efficient spin-charge interconversion: converting charge current to spin current and spin current to charge current. In spin–orbit materials that are regarded as the most promising candidate for spintronic logic devices, one mechanism that is responsible for spin-charge interconversion is Edelstein and inverse Edelstein effects based on spin-momentum locking in materials with Rashba-type spin–orbit coupling. Over last decade, there has been rapid progresses for increasing interconversion efficiencies due to the Edelstein effect in a few Rashba-Dresselhaus materials and topological insulators, making Rashba spin-momentum locking a promising technological solution for spin–orbit logic devices. However, despite the rapid progress that leads to high spin-charge interconversion efficiency at cryogenic temperatures, the room-temperature efficiency needed for technological applications is still low. This paper presents our understanding on the challenges and opportunities in searching for Rashba-Dresselhaus materials for efficient spin-charge interconversion at room temperature by focusing on materials properties such as Rashba coefficients, momentum relaxation times, spin-momentum locking relations and electrical conductivities.

High-entropy alloys (HEAs) including Ni and other 3d transition metals present a unique class of materials characterized by single phase solid solutions in face-centered cubic structure with complicated chemical disorder, in terms of atomic size, mass, and force constants. While they are renowned for excellent mechanical properties in extreme environment, their thermal transport properties are underexplored, despite the importance in relevant applications. This article comprehensively reviews the experimental and theoretical research on thermal transport and lattice dynamics in Ni-based alloys focusing on HEAs, along with fundamental theories for electron and phonon thermal conductivity in metals and alloys. The influence of the disorders is discussed for Ni-based alloys, from binary to quinary, which particularly reveals the importance of the interatomic force constant disorder. Future research is expected to further advance the understanding of interactions between electrons and phonons and microscopic mechanisms of phonon transport, as well as methodologies for extreme environment.

Layered solid particles such as hexagonal boron nitride (h-BN) are widely used as thermally conductive fillers in polymer composites. Exfoliated sheets of the layered particles (nanosheets) have been considered a significant asset to enhance thermal conductivity of the composites. Theoretical and experimental studies have reported that maximally exfoliated h-BN nanosheets (BNNS) would possess superior thermal conductivity. Accordingly, considerable efforts have been devoted to development of the single- or few-layered BNNS as thermally conductive fillers. As for thermal conductivity, however, the nanosheet fillers cannot be free from several drawbacks. Taking h-BN as an example, we discuss if the thinner nanosheets are always superior solid fillers. Based on significant preceding papers in the related disciplines, positive and negative factors of the thermally conductive nanosheets are examined in the short review. Contrary to common belief, 10 layers BNNS or slightly thicker ones were found to be the most valuable as thermally conductive fillers. Since the methodology presented here avails for other layered solid particles, it would advance the technological field of the functional composite materials. More broadly, in the present paper, we attempted to bridge the huge gap between knowledge about nano-sized materials and functional advancement of practically utilized materials.

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.