Interdisciplinary Materials最新文献

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.

With the emergence of triboelectric nanogenerators (TENGs), the monitoring technology based on the triboelectric effect is becoming more and more popular due to the advantages of the wide selection of materials and flexible working modes. Traditional condition monitoring technologies for machines, infrastructure, and environment (MIE) are usually based on piezoelectric effects, thermal effects, and acoustic effects, which need external power to drive. The advancement of TENGs provides more possibilities to enable condition monitoring technologies with self-driving ability in the society of artificial intelligence of things (AIoT) systems. The flexible structure design and materials selection facilitate the condition monitoring of modern MIE in a more economical and effective way. An increasing number of related works are emerging. In these regards, this paper reviews the state of the art in condition monitoring based on TENGs for the applications of MIE and related interdisciplinary research, such as materials science, information, engineering, and so forth. The introduction of condition monitoring for MIE is illustrated and the basic mechanism of TENG is introduced first. Subsequently, the condition monitoring based on TENG technologies for machines, infrastructure, and environment is elucidated respectively. The most popular and hot research trends are pointed out and the current challenges are also discussed and illustrated, thus giving hints and guidance for future research trends.

Graphite's resilience to high temperatures and neutron damage makes it vital for nuclear reactors, yet irradiation alters its microstructure, degrading key properties. We used small- and wide-angle X-ray scattering to study neutron-irradiated fine-grain nuclear graphite (Grade G347A) across varied temperatures and fluences. Results show significant shifts in internal strain and porosity, correlating with radiation-induced volume changes. Notably, porosity volume distribution (fractal dimensions) follows non-monotonic volume changes, suggesting a link to the Weibull distribution of fracture stress.

All solid-state lithium batteries (ASSLBs) are identified as the next-generation energy storage technology due to their prospects of nonflammability and improved energy density. Elevating the charging cutoff voltage of cathode materials is an effective strategy to improve the energy density of ASSLBs. However, the limited oxidative stability of solid-state electrolytes (SEs) and structural and chemically irreversible changes in the cathode active material result in inferior electrochemical performance. Here, we synthesized nano-Li_1.2Al_0.1Ta_1.9PO₈ (LATPO) coatings on the surface of lithium cobalt oxide (LCO) by a facile ball-milling method combined with heat treatments. This artificial intermediate phase effectively enhances the structural stability and interfacial transport kinetics of the cathode and mitigates continuous side reactions at the cathode/solid electrolyte interface. As a result, the ASSLBs with modified LCO cathode exhibit a reversible capacity of 203.5 mAh g⁻¹ at 0.1 C and 4.0 V (corresponding to the potential of 4.6 V vs. Li⁺/Li), superior cycling stability (85.4% capacity retention after 500 cycles), a high areal capacity (4.6 mAh cm⁻²), and a good rate capability (62 mAh g⁻¹ at 3 C). This study emphasizes the importance of cathode surface modification in achieving stable cycling of halide-based ASSLBs at high voltages.

Given the confluence of dysregulated inflammation, vasculopathy, and neuropathy, diabetic wounds pose a significant clinical challenge. Commercially available wound dressings often lack sufficient bioactivity, failing to meet clinical demands. Herein, we developed a PCL-PLLA-MgSiO₃ (PP-MgSi) patch with promising therapeutic effects. The PP-MgSi composite patch was manufactured via electrospinning and characterized by controllable degradation and local release of Mg²⁺ and SiO₃²⁻. The patch showed favorable in vitro biocompatibility and bioactivity, notably increased angiogenesis, myelination, and neurite outgrowth. In type 2 diabetic mice, the PP-MgSi patch exhibited MgSi dose-dependent effects on enhancing diabetic wound healing by modulating the expression of TNF-α, iNOS, and CD206 to balance inflammation, while boosting CD31 and β3-tubulin levels to promote neurovascularization. With the significant suppression of pro-inflammatory-related TNF and IL-17 pathways, while activating the peripheral nerve-associated axon guidance pathway, blood vessel-associated HIF-1α and VEGF pathways, the PP-MgSi patch ultimately achieved accelerated healing compared to the control group. Ultimately, the PP-MgSi patch exhibited an accelerated repair rate, with comparable neovascularization and superior peripheral nerve regeneration capacity compared to three representative commercially available products. This proof-of-concept work presents a promising bioactive PP-MgSi patch for future clinical diabetic wound management, particularly in terms of its neurovascular network recovery properties.

Outside Back Cover: In the article of doi: 10.1002/idm2.12254, uniform Pt-modified mesoporous cerium oxide (Pt/mCeO₂) microspheres are designed for constructing hierarchically macro-/mesoporous sensing layer on MEMS chips. Thanks to the improved gas diffusion, enhanced gas-solid interaction interfaces and rich active metal/metal oxide sites, the as-fabricated gas sensor exhibits an excellent sensing performance.

Outside Front Cover: For the article of doi: 10.1002/idm2.12255, the perovskite solar cell is protected by a transparent and robust “shield”. This “shield” can insulate perovskite solar cell from external environmental erosion such as rainwater and inhibit the lead leakage from the device.

Hypertrophic scar (HS) is a common pathological fibrous hyperplasia with high incidence and recurrence rates. The limited understanding of the pathological characteristics of HS restricts the therapeutic efficacy of current strategies. In this study, we first identified the elevated mitophagy and suppressed apoptosis in hypertrophic scar fibroblasts (HSFs), which combined with excess inflammation to constitute the pathological microenvironment of HS, driving us to develop a functionalized microneedle (MN) patch for inhibiting mitophagy, promoting apoptosis and modulating inflammation to adapt HS treatment. The MNs integrate curcumin-loaded HSFs-derived extracellular vesicles (Cur@EV) and a decellularized extracellular matrix from umbilical cord-derived mesenchymal stem cells (UC-MSCs-dECM, UdECM). The homologous Cur@EV with enhanced cellular uptake significantly induced HSFs apoptosis via mitophagy inhibition, meanwhile reducing collagen deposition. Meanwhile, the UdECM exerted immunomodulation capacity by facilitating the M2 polarization of macrophages, aiding in the suppression of HSFs. Notably, the Cur@EV/UdECM-functionalized MN patches exhibited regenerative therapeutic outcomes on a rabbit HS model, with HS inhibition and new hair follicle formation. Overall, this study presents a synergistic strategy based on the regulation of “mitophagy-apoptosis-inflammation,” offering a novel, minimally invasive approach for HS management. The integration of homologous Cur@EV and UdECM into an MN patch represents an innovative, multifunctional approach that combines mitophagy inhibition, apoptosis induction, and immunomodulation. The regenerative outcomes observed in the rabbit HS model, including hair follicle formation, further underscore the translational potential of this strategy. Future research will focus on optimizing patch design for scalable production, assessing long-term safety and efficacy, and exploring its broader applicability.

Rapid, reliable, and quantitative formaldehyde detection has become increasingly important in the processing industry and environmental protection. As an intelligent electronic instrument, the realization of electronic noses (e-noses) for quantitative gas detection relies on enhanced specificity. Here, we propose a materials-algorithm co-optimization (MACO) method that enables quantitative detection of formaldehyde in e-nose. This approach employs thermokinetic feature engineering to optimize data quality and algorithm selection, thereby reducing dependence on data scale and computing power resources. Specific thermokinetic activation patterns for formaldehyde can be generated through a single materials processing strategy. Through a combination of thermokinetic feature-driven machine learning, we demonstrated an e-nose—comprising only five Co₃O₄-based gas sensors—capable of discriminating formaldehyde from ethanol. The mathematical model reveals that the physicochemical mechanism of odor coding logic in our e-nose is dictated by the mass action law. A quantitative detection of formaldehyde in 0.1–20 ppm with a precision of 5% full-scale (F.S.) has been demonstrated. We also showcase the adaptability of e-nose for binary mixture analysis. The detection model of the MACO-driven e-nose is simple and interpretable, showing broad prospects to achieve quantitative gas detection rapidly and at a low cost.

Room-temperature organic ferromagnetic semiconductors represent a promising frontier in developing next-generation electronic and spintronic devices. However, the origin of magnetic moments in organic ferromagnets and the acquisition of critical evidence for magnetic ordering remain incompletely understood. This study presents compelling evidence for room-temperature ferromagnetism in N,N′-diamino perylene bisimide (2NH₂-PBI) radical aggregates through a comprehensive analysis utilizing X-ray magnetic circular dichroism (XMCD), low-field microwave absorption (LFMA) techniques and magnetic characterization. The 2NH₂-PBI samples, prepared via hydrothermal reduction, exhibit a significant saturation magnetization of 0.8 emu g⁻¹ (336.3 emu mol⁻¹) at 300 K, with a coercive field of 170 Oe. The XMCD measurements at the carbon K-edge exhibited a pronounced dichroic signal (~8.7%), confirming the origin of ferromagnetism in the π-conjugated electrons of the perylene core. Density functional theory calculations further support this finding by demonstrating that spin density is primarily delocalized on the π-conjugated skeleton, giving a microscopic explanation for the magnetic properties of 2NH₂-PBI radicals. Furthermore, LFMA studies provide additional evidence of ferromagnetic ordering, showcasing hysteretic behavior consistent with domain wall dynamics. Our work indicates that imide-based radical molecules with extended π-conjugated structures constitute a class of effective magnetic functional units.