The advent of wearable electronics has generated considerable interest in the development of fiber-shaped supercapacitors (FSCs). FSCs have several applications, such as integration into wearable power fabrics for modular energy storage, coupling with specific devices, forming composite fibers, and combining with energy-harvesting fibers to develop integrated energy-harvesting and storage-usage fabrics. This review provides a comprehensive overview of FSCs based on their fundamental principles, detailing various structural configurations (e.g., parallel, wrapped, twisted, and coaxial) and substrate materials (e.g., carbon-based, polymeric, and metallic fibers), along with strategies for enhancing their electrochemical and mechanical performance. Furthermore, it outlines large-scale fabrication techniques, such as wet spinning, synchronous coupling, direct ink writing, and thermal drawing. This review identifies the challenges currently facing FSCs research and suggests directions for future development.

To foster sustainable development, a pivotal trend lies in harnessing sustainable energy supplies that propel modern economic and societal progress. Recent advancements in living materials for energy applications have sparked a groundbreaking research area: engineered living energy materials (ELEMs), which seamlessly integrate biological and artificial systems for efficient energy conversion and storage. To consolidate and propel this research area, herein, we summarize and delve into the evolution of ELEMs. Firstly, we provide an overview of the structural features and energy conversion mechanisms employed by bio-modules spanning proteins, organelles, and entire organisms. They can be directly used as components for constructing ELEMs or provide inspirations for the design of such entities. Then, we comprehensively review the latest research strides in ELEMs based on their distinct energy conversion modes. Finally, we discuss the challenges confronting ELEMs and envision their future trajectories. The progress of ELEMs holds immense potential to catalyze interdisciplinary research endeavors encompassing medicine, environmental science, and energy technologies.

Data-driven artificial intelligence provides strong technical support for addressing global energy and environmental issues. The powerful data processing and analysis capabilities of machine learning (ML) can quickly predict electrocatalytic performance, improving the efficiency of catalyst design and addressing the time-consuming and inefficient nature of traditional catalyst design. By integrating ML with theoretical calculations and experiments, catalytic reaction processes can be precisely regulated. This not only accelerates the discovery of new catalysts but also drives the development of more efficient and environmentally friendly sustainable energy technologies. In this article, we discuss new approaches to discovering novel catalysts driven by ML, focusing on catalytic activity prediction, reaction energy barrier optimization, and the design of innovative catalytic materials. We systematically analysis the application of ML in the field of electrocatalysis and explore the future prospects of ML in this domain. We provide a comprehensive and in-depth analysis of the application of ML in the field of electrocatalysis and explore its potential for future development.

Magnesium (Mg) is globally abundant in resources, and Mg-based compounds—such as magnesium based hydrides, hydroxides, oxides, and magnesium metal-organic frameworks (Mg MOFs)—have shown significant application prospects in gas separation. This is largely due to the electronic characteristics of Mg or Mg²⁺ ions, which facilitate the capture of hydrogen (H₂) and acidic gases such as carbon dioxide (CO₂) and sulfur dioxide (SO₂) from other gases. Consequently, exploring the use of Mg-based materials in gas separation and purification applications could not only advance the scientific understanding of solid-gas interaction mechanisms but also provide cost-effective solutions for gas separation technology at an industrial level. This review summarizes the recent practices and explorations of Mg-based solid-state materials in various gas separation and purification methods, including physical adsorption-based separation, chemical absorption-based separation, and membrane-based separation. For each separation method, the relevant Mg-based materials are discussed in detail, and key findings from existing research are presented and analyzed. Additionally, inspired by the straightforward design of air-stable hydrogen storage materials, this review specifically addresses anti-passivation strategies for Mg-based hydrides, which are crucial for their applications in hydrogen gas separation and purification. Finally, this review highlights key issues and fields for future research and development in Mg-based gas separation materials.

The intricately complex structures of natural biological materials, which endow them with exceptional properties, serve as unparalleled models and sources of inspiration for the design of synthetic materials. However, translating these structures into metallic systems poses formidable challenges due to the demanding conditions required for metal processing. This brief perspective spotlights the 3D interpenetrating-phase structures evolved in biological materials and distills key insights for bioinspired structural design in metallic materials. We highlight recent advancements in creating bioinspired metal composites, particularly through advanced processing techniques like metal melt infiltration into porous scaffolds, achieving remarkable synergies between various mechanical properties and functionalities. Additionally, AI-driven approaches show immense potential to accelerate the iterative process of optimizing structures and properties in bioinspired designs.

Functionalities of materials tightly relate to the atomic and electronic structures, the coupling between which through lattice and charge gives birth to thermoelectricity, enabling a direct heat-electricity conversion. Booming wearable electronics nowadays urgently demand thermoelectric film generators as self-powered units using body and environment heats, of which highly recoverable deformability and power are the core challenges. This indicates the great importance of elasticity since a plastic deformation otherwise actuates lattice slips to unsecure both thermoelectricity and recoverability. It is illustrated in this work texturization and dislocations for enhancing elasticity in cold-rolled constantan foils, a metal thermoelectric enabling one of the highest power outputs near room temperature for deformable wearables. The device can work in a purely elastic region, to secure orders of magnitude improvement in recoverable bendability with an extraordinary specific power density, at a bending radius down to 5 mm fitting the curvature of an adult's little finger. This work delivers a strategy for bringing robust deformability to thermoelectricity for powering wearable electronics.

Covalent organic frameworks (COFs) have emerged as highly promising materials for high-performance memristors due to their exceptional stability, molecular design flexibility, and tunable pore structures. However, the development of COF memristors faces persistent challenges stemming from the structural disorder and quality control of COF films, which hinder the effective regulation of active metal ion migration during resistive switching. Herein, we report the synthesis of high-quality, long-range ordered, imine-linked two-dimensional (2D) COF_TP-TD film via the innovative surface-initiated polymerization (SIP) strategy. The long-range ordered one-dimensional (1D) nanochannels within 2D COF_TP-TD film facilitate the stable and directed growth of conductive filaments (CFs), further enhanced by imine-CFs coordination effects. As a result, the fabricated memristor devices exhibit exceptional multilevel nonvolatile memory performance, achieving an ON/OFF ratio of up to 10⁶ and a retention time exceeding 2.0 × 10⁵ s, marking a significant breakthrough in porous organic polymer (POP) memristors. Furthermore, the memristors demonstrate high-precision waveform data recognition with an accuracy of 92.17%, comparable to software-based recognition systems, highlighting its potential in advanced signal processing tasks. This study establishes a robust foundation for the development of high-performance COF memristors and significantly broadens their application potential in neuromorphic computing.

TiAl plays a crucial role in the field of aero-engine as a new lightweight high-temperature alloy. The γ/α₂ lamellar TiAl single crystals exhibit the highest recorded plasticity, much higher than the soft phase γ-TiAl. This suggests that the hard phase α₂-Ti₃Al may have a unique plastic deformation mechanism, which is important for essentially understanding the origin of unusual plasticity and further improving the mechanical properties of TiAl. Here, we found the dynamic sequential phase transformation between HCP and FCC under shear loading in α₂-Ti₃Al, which is a novel plastic deformation mechanism comparable to twinning. We attribute this to the bond-breaking formation process called “catching bond”, which is the origin of atomic mechanism of phase transformation occurrence. This “catching bond” process is an effective way of energy dissipation that can release the internal stress while maintaining the integrity of structure. The higher cleavage energy than the generalized stacking fault energy (GSFE) guarantees the continuity of phase transformation during shearing. Moreover, the γ/α₂ coherent interface can reduce the GSFE, thus decreasing the critical resolved shear stress (CRSS) of the phase transformation by 35%, which suggests that the phase transformation induced plastic mechanism easily occurs in the lamellar structure. This study reveals the plastic deformation mechanism of α₂-Ti₃Al and explores the role of γ/α₂ coherent interface on the plasticity, which is expected to provide guidance for further improving the mechanical properties of TiAl alloys.