Mrs Bulletin最新文献

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 rapid advancement of human–machine interfaces and wearable devices necessitates display platforms that are mechanically modulable and capable of interacting with their environments while effectively communicating with users. However, current display technologies have yet to fully address these demands. This study presents a scalable luminescent fiber (LF) display platform designed to be mechanically modulable and interactive with users. Inspired by the silkworm spinning process, our fabrication technique continuously coats a luminous layer onto parallel dual-strand electrode fibers, resulting in LFs with a skin–core structure composed of core electrodes and a luminescent skin. By selecting conductive fibers with varying mechanical properties as inner electrodes, we can modulate the LF's mechanical characteristics over a range suitable for flexible displays, including stretching, bending, folding, and knotting. Additionally, the hydrophobicity and mechanical flexibility of the luminescent coating, along with the robust binding between the skin–core interfaces, ensure the LF's stable luminescence under complex mechanical stimuli and following multiple washes and extended use. Integration of machine learning and Internet of Things technologies enhances interactions between the LF display platform and users. This comprehensive system achieves voice recognition, numerical computing, semantic analysis, and intelligent interaction, all of which are incorporated into a human–machine interface that facilitates real-time human–display interaction. By emphasizing our fabrication strategy and adaptable design, this mechanically modulable and human–machine interactive LF display platform shows promise for diverse applications in human–machine interfaces, medical devices, soft robotics, and wearable sound–vision systems.

Impact statement

Our study introduces a new concept of a light-emitting fiber display platform with a skin–core structure. This concept differentiates itself from existing research by addressing the key challenges of mechanical strength and user interactivity faced by ultraflexible displays. By utilizing core-electrode fibers with different mechanical properties, we can effectively regulate the mechanical performance of the luminescent fiber, ensuring compliance under diverse mechanical stimuli. Additionally, the resilient, hydrophobic, and luminous skin of the fiber guarantees stable luminance even in harsh conditions. The incorporation of artificial intelligence and Internet of Things technologies further enhances user interaction capabilities, enabling functions such as gender and age recognition, numerical calculations assistance, and semantic dialogue. Our work and the underlying concept bring insights to materials science by pushing the boundaries of fiber and fabric displays. With improved mechanical properties, enhanced user interact

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.

Graphical abstract

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.

Abstract

The increasing concerns associated with petroleum-derived polymers motivate the development of sustainable, renewably sourced alternatives. In ubiquitous applications such as structural materials for infrastructure, the built environment as well as packaging, where natural materials such as wood are used, we rely on nonrenewable and nondegradable polymers to serve as adhesives. In wood panels, such as medium density fiberboards (MDFs), formaldehyde-based resins are predominantly used to bond wood fibers and to provide strength to the materials. To further mitigate the environmental impact of construction materials, more sustainable adhesives need to be investigated. In this article, we introduce Ulva seaweed as an adhesive to enable cohesion and strength in hot-pressed wood panels. Upon hot-pressing, powdered Ulva flows in between the wood particles, generating a matrix, which provides strong binding. We show that the flexural strength of Ulva-bonded wood biocomposites increases with increasing Ulva concentrations. At an Ulva concentration of 40 wt%, our composites reach an average elastic modulus of 6.1 GPa, and flexural strength of 38.2 MPa (compared to 4.7 GPa and 22.6 MPa, respectively, for pure wood compressed at the same pressing conditions). To highlight the bonding mechanisms, we performed infrared and x-ray photoelectron spectroscopy and identified indications of fatty acid mobility during hot-pressing. In addition, we demonstrate that the presence of Ulva improves other properties of the composites such as water resistance and flame retardancy. Ulva is also shown to behave as an excellent adhesive agent between two prepressed beams. Finally, we perform an in-depth analysis of the environmental impact of wood-Ulva biocomposites.

Impact statement

This research introduces a sustainable alternative to petroleum-derived adhesives used in wood-based panels, addressing a pressing environmental concern in our infrastructure and construction materials. Here, we discuss the use of Ulva, a green seaweed species, as a renewable and biodegradable solution for such adhesives. We demonstrate its efficacy as a bonding agent in hot-pressed wood panels, offering enhanced strength and durability. Moreover, the use of Ulva contributes to mitigating the environmental footprint associated with traditional materials, aligning with global efforts toward sustainability and circular economy principles. Through comprehensive spectroscopic analyses and mechanical testing, we provide insights into the underlying mechanisms of Ulva-based adhesion. Furthermore, we report the water resistance and improved flame retardancy of Ulva-bonded wood, which are essential for applications in infrastructure and construction. Finally, we discuss environmental and social advantages of Ulva-bas

This article highlights the applications of integrated unified phase-field methods in guiding the design of high-performance engineering alloys and the optimization of manufacturing processes within an integrated computational materials engineering (ICME) framework. By combining macro process data, solidification, precipitation, and recrystallization conditions, phase-field modeling is used to predict the precipitation, segregation, and crack tendency of NbC as the crack source in austenitic stainless steels, thereby optimizing casting parameters and improving the product qualification rate from 40% to more than 80%. Phase-field modeling is also used to reveal the internal microstructure evolution of Mg–Li-based alloys during spinodal phase separation and help design the Mg–Li–Al alloy with an ultrahigh specific strength (470–500 kN m kg⁻¹) surpassing all engineering alloys. Phase-field simulations of dendritic growth incorporating macro-temperature field and shrinkage defects in solidification allow us to adjust the casting process parameters for optimizing the alloy and casting’s mechanical properties.

Graphical abstract

The phase-field method has become the main computational technique for modeling and predicting the microstructure evolution in materials science and engineering. Its versatility and ability to capture complex microstructure phenomena under different processing conditions make it a valuable tool for researchers and engineers in advancing our understanding and engineering of materials microstructures and properties. This issue of MRS Bulletin is focused on a few recent success stories of applying the phase-field method to understanding, discovering, and designing mesoscale structures and for guiding the design of experiments to optimize properties or discover new phenomena or functionalities. We hope this issue will inspire increasing future focus on utilizing the phase-field method to guide experimental synthesis and characterization for desirable properties.

Graphical Abstract