Interdisciplinary Materials最新文献

Inside Back Cover: Graphite is a key structural component in some of the world's oldest nuclear reactors and many of the next-generation designs being built today. But it also condenses and swells in response to radiation. This paper, doi: 10.1002/idm2.70008, covered a link between properties of graphite and how the material behaves in response to radiation.

Gallium nitride (GaN)-based ultraviolet (UV) photodetectors (PDs) are promising for advanced UV detection. However, the development faces challenges in cost reduction, process complexity, and the need for enhanced detection performance. In this study, an alternating current photovoltaic (AC PV) effect was identified in a GaN Schottky junction, achieving UV photoelectric responsivity improvements of up to two orders of magnitude and superior response speed compared to conventional photocurrent. Heating tests confirm PD stability at 600°C, attributable to the AC PV effect that maintains high response speed. Additionally, integrating a magnetically levitated structure with the UV PD enables a highly sensitive photoelectric wind speed sensor with an ultra-low startup wind speed of 0.5 m/s and a rapid response time of 25.3 ms. This study offers a promising approach for fabricating high-performance UV photoelectric devices and precise monitoring in challenging environments.

Outside Front Cover: Brain-like artificial intelligence systems with multimodal fusion (e.g., visual, tactile, auditory) are poised to revolutionize biomimetic technology and humanmachine interfaces. However, the dependence on external circuitry to integrate these distinct sensory modalities introduces inefficiencies. A novel material group NbOX₂ (X = Cl, Br, I) for integrating such brain-inspired multimodal perception and computation in a single device has been demonstrated. Further details can be found in doi: 10.1002/idm2.70012 by Yuan Li, Tianyou Zhai, and co-workers.

Outside Back Cover: The cover image of doi: 10.1002/idm2.70003 presents a bioactive magnesium silicate composite patch manufactured via electrospinning technology. This medical dressing features controllable degradation and local release of Mg²⁺ and SiO3^2−, effectively balancing inflammation while promoting neurovascularization. This innovative solution offers significant potential for clinical diabetic wound management, particularly in enhancing the recovery of the neurovascular network.

This article is a response to the comment “Reassessing Machine Learning Techniques for Electrocatalyst Design: A Call for Robust Methodologies”. First, we clarify that the artificial neural network–SHapley Additive exPlanation (ANN–SHAP) method mentioned in the comment originates from the original work of Ding et al., which we only briefly summarized. In that study, nine different machine learning models were employed to predict the performance of proton exchange membrane fuel cells, among which the ANN model performed best. SHAP, together with multiple interpretability techniques (PDP, Tree-based Rule, EIX, etc.), was used to cross-validate feature importance, which was further compared with the results from manual feature selection, PCA, and t-distributed stochastic neighbor embedding, and complemented by experimental validation to reduce the risk of bias amplification. We agree with the commenter that model interpretability should be approached with caution, as the absence of a definitive “ground truth” for feature importance remains a current challenge. However, benchmarking SHAP explanations against domain knowledge or validating them using synthetic datasets can help reduce the risk of misinterpretation. Regarding the unsupervised methods suggested in the comment (FA and HVGS), we consider them to have exploratory value for certain data structures, but caution is needed when applying them to experimental systems involving nonlinearity or high noise.

Multimodal perception, pivotal for artificial intelligence (AI) systems demanding real-time decision-making and environmental adaptability, might be significantly improved through two-dimensional (2D) piezo-ferro-opto-electronic (PFOE) semiconductors, like, NbOX₂ (X = Cl, Br, I). Such improvement may enable in-sensor fusion of sense organ signals (e.g., vision, audition, gustation, and olfaction) within a single functional component, overcoming limitations of conventional discrete sensor architectures. Such function cohesion, combined with their recently uncovered properties, not only provides a robust foundation for expanding sensory modalities and developing novel mechanisms to establish an all-in-one multimodal perception platform, but also paves the way for multisensory-integrated artificial systems beyond human sensory systems. This single-component system employing such PFOE semiconductors substantially mitigates intermodule communication latency while boosting integration density of information, thereby circumventing persistent inefficiencies in AI hardware architectures for real-time applications, such as embodied robotics and immersive human–machine interfaces. This fusion of multimodal perception and computation, enabled by multiphysics coupling of 2D NbOX₂, drives AI systems toward biological-grade efficiency while maintaining environmental adaptability, representing a critical leap toward autonomous intelligence operating in dynamic real-world settings.

Composite quasi-solid-state electrolytes are pivotal for enabling high-energy-density lithium metal batteries (LMBs), yet their practical application is hindered by discontinuous ion transport, poor interfacial stability, and limited high-voltage endurance. Here, a universal in situ growth strategy is developed to construct a metal-organic framework (MOF)/polymer composite electrolyte (ZCPSE) with hierarchically ordered ion-conducting networks. The ultra-uniform MOF nanoparticles (e.g., ZIF-8) are anchored onto polymer nanofibers, creating abundant nanopores and Lewis acid sites that synergistically enhance Li⁺ transport and oxidative stability. The resulting ZCPSE exhibits unprecedented ionic conductivity (0.46 mS cm⁻¹ at 25°C), a wide electrochemical window (5.15 V vs. Li/Li⁺), and exceptional mechanical strength (151.2 MPa, 4× higher than pristine polymer membrane). Theoretical simulations reveal that the 3D continuous MOF/polymer interface facilitates rapid Li⁺ dissociation and uniform flux distribution, endowing ZCPSE with a high Li⁺ transference number (0.74) and dendrite-free Li plating/stripping (2000 h in Li|Li symmetric cells). Practical applicability is demonstrated in Li|LiFePO₄ cells (stable cycling at 25°C–100°C) and high-voltage Li|LiNi_0.8Co_0.1Mn_0.1O₂ full cells (4.5 V, 100 cycles with 99.2% capacity retention). This study provides a paradigm for designing MOF-based hybrid electrolytes with simultaneous ionic, mechanical, and interfacial optimization, paving the way for safe and high-energy LMBs.

The persistent pursuit of miniaturization and energy efficiency in semiconductor technology has driven the scaling of complementary metal-oxide-semiconductor field-effect transistors (CMOS FETs, i.e., the MOSFETs) to their physical limits. Conventional MOSFETs face intrinsic challenges, especially the Boltzmann limit that imposes a fundamental lower bound on the subthreshold swing (SS ≥ 60 mV dec⁻¹ at room temperature). This limitation severely restricts voltage scaling and exacerbates static power dissipation. To overcome these bottlenecks, tunnel field-effect transistors (TFETs) have emerged as a promising post-CMOS alternative. The advantages of ultra-small SS well below the Boltzmann limit, as well as ultralow leakage currents, make TFETs ideal for low-power electronics and energy-efficient computing in the future information industry. However, its current development has encountered significant resistance to further performance improvement requirements; new breakthroughs have evolved to be based on interdisciplinary research that covers materials science, device technology, theoretical physics, and so on. Here, we provide a review on the design and development of TFET, which mainly describes the device physics model of tunnel junctions, and discusses the optimization direction of key parameters, the design direction of potential structures, and the development direction of the innovation system based on the device physics. Also, we visualize the framework for the figures of merit of TFET performance and further forecast the future applications of TFET.

The growing demand for advanced electrochemical energy storage devices highlights challenges in battery materials, such as limited storage sites, slow ion/electron transport, and structural instability, which collectively impede improvements in energy density, rate performance, cycle life, and battery safety. To address these challenges, high-entropy design—a strategy integrating multiple elements through doping, compositional gradients, or alloying—has emerged as a transformative approach to simultaneously enhance thermodynamic stability and unlock synergistic “cocktail effects” in battery materials. By strategically combining elements with tailored atomic-scale interactions, such systems can achieve unprecedented performance between structural robustness and electrochemical activity. However, the design principles and synergistic effects within high-entropy materials (cathodes, electrolytes, anodes) remain poorly understood, complicated by their vast compositional and structural possibilities. In this review, we present a systematic analysis of how high-entropy strategies optimize material properties across three interdependent dimensions: (1) structural engineering (e.g., surface/interface engineering), (2) physical effects (e.g., lattice strain and size mismatch), and (3) electronic/chemical interactions (e.g., valence state modulation and electron delocalization). While entropy alone does not guarantee superior performance, we highlight that rational element selection and configuration design are critical to activating these mechanisms. Importantly, AI-driven framework integrating machine learning with first-principles modeling, can enable data-guided material discovery to decode the complexity of high-entropy systems. This framework systematically deciphers design principles, predicts performance trade-offs, and accelerates the translation of high-entropy materials into practical energy storage solutions.