Matter最新文献

Biobased fibers have demonstrated huge potential as renewable and biodegradable materials in recent years. Traditionally produced through simple spinning of natural fibers such as cotton and silk, biobased fibers have evolved significantly with modern techniques enabling large-scale production of biomass feedstocks and monomers through green solvent extraction and biotechnology processes. Various functional biobased fibers can now be manufactured using wet, electro, and melt spinning technologies, greatly advancing the development of renewable biobased and biosynthetic fibers. These fibers find widespread application across sectors such as functional textiles, biomaterials, energy storage, and wearable technologies. By providing a holistic perspective spanning resource extraction, fiber production, and end use applications, this overview aims to foster cross-disciplinary inspiration and collaboration to accelerate the utilization of biobased fibers. In the future, biobased fibers are projected to gradually replace traditional petroleum-based fibers, driving society toward a greener and more sustainable path.

While polymer membranes are used to remove salts from environmental and industrial electrolytes, it remains a significant challenge to engineer them to isolate a single dissolved species from complex mixtures, which is important for lithium mining, battery and magnet recycling, and microelectronics. Underpinning this challenge has been a lack of understanding of rate-limiting mechanisms in selective ion transport. Here, we show that hydrated ions exhibit higher free energies of activation when crossing solution-membrane interfaces (i.e., partitioning) than when diffusing through polymers, which challenges historical assumptions embedded in widely used models of membrane performance. We further articulate a framework benchmarked with quantitative capabilities for predicting how functionality within polymer membranes or at their surfaces affects selectivity toward individual dissolved species.

Sustainable and biodegradable materials derived from biomass are appealing candidates to replace fossil-based materials. However, the mechanical performance of biomass is insufficient for practical applications. Here, inspired by fish scales, we report a strategy to construct large-area, high-strength cellulose nanocrystal (CNC) nanocomposites with confined polymer nanocrystallization in Bouligand structures. By regulating the electrostatic repulsion of CNCs, the spacing of nanorods was reduced from 8.8 ± 0.4 to 5.0 ± 0.3 nm, and the crystallinity of the interphase extended polymer chains was regulated within such a confined space. The resulting nanocomposite films exhibited a tensile strength of 456.6 ± 18.6 MPa. Moreover, the nanocomposite films could be laminated to bulk materials, which exhibit excellent fracture toughness of 7.1 ± 0.2 MPa m^1/2 and hardness of 6.1 ± 0.6 GPa while being light in weight. This efficient cellulose utilization strategy offered a promising pathway for the production of robust, biodegradable, and sustainable cellulosic bioplastics.

Corrosion of alloys in molten salts is commonly understood from thermodynamics: the higher the content of noble elements in the alloy, the more corrosion resistant the alloy is expected to be. Here, we present an example in the CrFeMnNi compositionally complex space that defies this conventional intuition. Machine learning-facilitated analysis of the extensive dataset reveals that molten salt corrosion in this system is primarily predicted by the Ni mobility within the alloy. This discovery was made possible using high-throughput manufacturing and testing of a set of 110 compositionally complex alloys within the CrFeMnNi element space prepared by additive manufacturing in situ alloying processes and corrosion tested in standardized conditions of temperature and chlorine potential. A standardized, parametric dataset of this magnitude for corrosion in molten salts is a first of its kind. This dataset results in new insights into the corrosion mechanism of CrFeMnNi for clean energy-enabling technologies.

This work reveals that heteroepitaxy alone cannot generate the structure of long-range ordered quantum dots within perovskite solid matrix; only oriented attachment has the capability to form this structure. The analysis enables the growth of lattice-matching layered quantum dot heterostructures and addresses a century-old problem in the semiconductor field.

Rechargeable aqueous zinc batteries (AZBs) suffer from rampant Zn dendrites and detrimental parasite hydrogen evolution corrosion, which impede the broad implementation of AZBs. To address these issues, it is imperative and significant to engineer the aqueous electrolytes to render single-ion conduction. The key aim for single-ion conductive electrolytes (SICEs) is to improve the cation transference number (t) with minimum sacrifice of ionic conductivity (σ). SICEs render the opportunity to effectively mitigate dendrite formation by minimizing ion concentration gradients and concurrently suppressing the loose deprotonated oxide species passivation through the restrained mobility of anions. This perspective encapsulates the fundamental principles and recent progress of SICEs. We suggest ideas for breaking the trade-off between t and σ under lean-water conditions. The testing methods for zinc ion transference numbers are also critically discussed. The primary objective of this perspective is to shed light on further development of SICEs to foster the energy density and lifespan of AZBs.

Transition metal telluride (TMT) nanosheets are an important class of functional materials in condensed matter physics and energy-related fields. Recently in Nature, a novel solid lithiation and exfoliation strategy is reported for fast and scalable synthesis of various TMT nanosheets. This breakthrough will push the boundaries of TMT materials and beyond.

Multimodal data hold paramount significance in the realm of battery science research. Traditional manual tools for data analysis have proven inadequate in meeting the demands of processing and mining multimodal data information. Machine learning emerges as a vital conduit between multimodal data and battery science. This review comprehensively organizes the recent advancements in multimodal data-driven research employing machine learning methodologies within the field of battery research. Specifically, it explores material-data-driven approaches to accelerate the development of advanced battery materials and image-data-driven schemes for cross-scale battery structure analysis and image enhancement, as well as battery assessment driven by condition data using both traditional machine learning and neural-network models. Furthermore, this review delves into the full potential of machine learning in the domain of advanced battery science research, encompassing aspects such as the accumulation of training data, the development of machine learning models, and the application of advanced analysis methods.