Mrs Bulletin最新文献

Global demand for batteries is increasing at a rapid pace, precipitating the equally rapid generation of hazardous battery waste. Recycling, which holds high potential for both mitigating this waste and recovering raw materials for subsequent battery manufacture, is often recognized as a necessary component of the battery life cycle. A critical step in many battery recycling schemes is the use of solvent to recover valuable metals such as lithium, cobalt, manganese, nickel, and others. This recovery typically involves the use of harsh mineral acids and peroxides, which pose their own environmental and safety hazards. The use of more benign organic acids and other organic compounds has emerged as a promising means to mitigate the hazards posed by purely inorganic solvents. In this article, we review recent research on organics-based metal recovery for battery recycling and provide our perspective on the extant challenges and opportunities in the field.

Graphical abstract

Increasing demand for electric vehicles (EVs) is increasing demand for the permanent magnets that drive their motors, as approximately 80% of modern EV drivetrains rely on high-performance permanent magnets to convert electricity into torque. In turn, these high-performance permanent magnets rely on rare earth elements for their magnetic properties. These elements are “critical” (i.e., at risk of limiting the growth of renewable energy technologies such as EVs), which motivates an exploration for alternative materials. In this article, we overview the relevant fundamentals of permanent magnets, describe commercialized and emerging materials, and add perspective on future areas of research. Currently, the leading magnetic material for EV motors is Nd₂Fe₁₄B, with samarium-cobalt compounds (SmCo₅ and Sm₂Co₁₇) providing the only high-performing commercialized alternative. Emerging materials that address criticality concerns include Sm₂Fe₁₇N₃, Fe₁₆N₂, and the L1₀ structure of FeNi, which use lower cost elements that produce similar magnetic properties. However, these temperature-sensitive materials are incompatible with current metallurgical processing techniques. We provide perspective on how advances in low-temperature synthesis and processing science could unlock new classes of high-performing magnetic materials for a paradigm shift beyond rare earth-based magnets. In doing so, we explore the question: What magnetic materials will drive future EVs?

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Abstract

An oblique helicoidal state of a cholesteric liquid crystal (Ch_OH) is capable of continuous change of the pitch (P) in response to an applied electric field. Such a structure reflects 50% of the unpolarized light incident along the Ch_OH axis in the electrically tunable band determined by (P)/2. Here, we demonstrate that at an oblique incidence of light, Ch_OH reflects 100% of light of any polarization. This singlet band of total reflection is associated with the full pitch (P). We also describe the satellite (P/2), (P/3), and (P/4) bands. The (P/2) and (P/4) bands are triplets, whereas (P/3) band is a singlet caused by multiple scatterings at (P) and (P/2). A single Ch_OH cell acted upon by an electric field tunes all these bands in a very broad spectral range, from ultraviolet to infrared and beyond, thus representing a structural color device with enormous potential for optical and photonic applications.

Impact statement

Pigments, inks, and dyes produce colors by partially consuming the energy of light. In contrast, structural colors caused by interference and diffraction of light scattered at submicrometer length scales do not involve energy losses, which explains their widespread in Nature and the interest of researchers to develop mimicking materials. The grand challenge is to produce materials in which the structural colors could be dynamically tuned. Among the oldest known materials producing structural colors are cholesteric liquid crystals. Light causes coloration by selective Bragg reflection at the periodic helicoidal structure formed by cholesteric molecules. The cholesteric pitch and thus the color can be altered by chemical composition or by temperature, but, unfortunately, dynamic tuning by electromagnetic field has been elusive. Here, we demonstrate that a cholesteric material in a new oblique helicoidal Ch_OH state could produce total reflection of an obliquely incident light of any polarization. The material reflects 100% of light within a band that is continuously tunable by the electric field through the entire visible spectrum while preserving its maximum efficiency. Broad electric tunability of total reflection makes the Ch_OH material suitable for applications in energy-saving smart windows, transparent displays, communications, lasers, multispectral imaging, and virtual and augmented reality.

Graphical Abstract

Abstract

In this study, we present a novel approach to enable high-throughput characterization of transition-metal dichalcogenides (TMDs) across various layers, including mono-, bi-, tri-, four, and multilayers, utilizing a generative deep learning-based image-to-image translation method. Graphical features, including contrast, color, shapes, flake sizes, and their distributions, were extracted using color-based segmentation of optical images, and Raman and photoluminescence spectra of chemical vapor deposition-grown and mechanically exfoliated TMDs. The labeled images to identify and characterize TMDs were generated using the pix2pix conditional generative adversarial network (cGAN), trained only on a limited data set. Furthermore, our model demonstrated versatility by successfully characterizing TMD heterostructures, showing adaptability across diverse material compositions.

Graphical abstract

Impact Statement

Deep learning has been used to identify and characterize transition-metal dichalcogenides (TMDs). Although studies leveraging convolutional neural networks have shown promise in analyzing the optical, physical, and electronic properties of TMDs, they need extensive data sets and show limited generalization capabilities with smaller data sets. This work introduces a transformative approach—a generative deep learning (DL)-based image-to-image translation method—for high-throughput TMD characterization. Our method, employing a DL-based pix2pix cGAN network, transcends traditional limitations by offering insights into the graphical features, layer numbers, and distributions of TMDs, even with limited data sets. Notably, we demonstrate the scalability of our model through successful characterization of different heterostructures, showcasing its adaptability across diverse material compositions.

Abstract

The current work presents and discusses the design and performance qualities of braided electronic yarns for woven textiles to produce red light-intensity effects. The design process involves a simple encapsulation process with adhesive tape and a heat-shrinkable tube to secure stainless-steel conductive threads (SS-CTs) to the solder pads of light-emitting diodes. These are arranged in a series against two SS-CTs to provide single positive and negative terminals at both ends. Findings from the infrared images show that the heat distribution and dissipation of the stainless-steel conductive threads are insignificant in affecting the wear comfort of the electronic textiles on the human body. The washing test shows the robust nature of the braided electronic yarns even after 20 cycles of being subjected to high agitation and mechanical stress. A proof of concept illustrates the effectiveness of the study results, which calls on further research work to enhance the durability and flexibility of the braided electronic yarns and electronic textiles to ensure a higher level of wear comfort. These braided electronic yarns would find end applications for nighttime visibility of pedestrians, a situation that would improve the recognition of drivers for reduced collision.

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Impact statement

Electronic textiles otherwise known as e-textiles have been the subject of scholarly attention in recent years due to their performance properties and wide areas of application for entertainment, monitoring, and safety purposes. The use of appropriate electronic yarns (e-yarns) plays a key role in connectivity and provides the necessary feedback when applied to a textile material. E-yarns are now replacing a few modern electronic textiles (e-textiles) that use rigid copper wires commonly applied in electronic circuits for e-textiles and improve the wear comfort of the garment. The integration of light-emitting diodes (LEDs) into conductive threads to form electronic yarns for textile material can be applied not only for entertainment purposes but also as a safety feature for pedestrians. The use of appropriate components is necessary to ensure and maintain the textile quality and properties for effective wearability. Herein, an e-yarn fabricated with stainless-steel conductive threads and LEDs for e-textiles is presented. As part of ongoing research work to develop smart interactive clothing to increase the nighttime visibility of pedestrians, this work discusses the design and performance qualities of braided e-yarns for woven textiles. The success of these low-cost, flexible, and strong (high wash durability) braided e-yarns facilitates their integration into woven fabrics for smart clothing to enhance the visibility and therefore safety of pedestrians.