Interdisciplinary Materials最新文献

{"title":"Response to [Reassessing Machine Learning Techniques for Electrocatalyst Design: A Call for Robust Methodologies]","authors":"Yulan Gu,&nbsp;Jiangwei Zhang","doi":"10.1002/idm2.70010","DOIUrl":"https://doi.org/10.1002/idm2.70010","url":null,"abstract":"<p>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.</p>","PeriodicalId":100685,"journal":{"name":"Interdisciplinary Materials","volume":"4 5","pages":"788-789"},"PeriodicalIF":24.5,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/idm2.70010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145197036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Raising 2D Piezo-Ferro-Opto-Electronic Semiconductor for Brain-Inspired Multimodal Perception and Computation","authors":"Decai Ouyang,&nbsp;Mengqi Wang,&nbsp;Yue Yuan,&nbsp;Na Zhang,&nbsp;Yan Zhou,&nbsp;Jianshu Fu,&nbsp;Mario Lanza,&nbsp;Yuan Li,&nbsp;Tianyou Zhai","doi":"10.1002/idm2.70012","DOIUrl":"https://doi.org/10.1002/idm2.70012","url":null,"abstract":"<p>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.</p>","PeriodicalId":100685,"journal":{"name":"Interdisciplinary Materials","volume":"4 5","pages":"709-713"},"PeriodicalIF":24.5,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/idm2.70012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145197002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In Situ-Engineered MOF/Polymer Hybrid Electrolyte With 3D Continuous Ion Channels for High-Voltage and Thermal-Resistant Lithium Metal Batteries","authors":"Manxi Wang,&nbsp;Lijuan Tong,&nbsp;Shiwen Lv,&nbsp;Manxian Li,&nbsp;Jingyue Zhao,&nbsp;Xuan Li,&nbsp;Chuanping Li,&nbsp;Xiaochuan Chen,&nbsp;Junxiong Wu,&nbsp;Xiaoyan Li,&nbsp;Qinghua Chen,&nbsp;Yuming Chen","doi":"10.1002/idm2.70005","DOIUrl":"https://doi.org/10.1002/idm2.70005","url":null,"abstract":"<p>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.</p>","PeriodicalId":100685,"journal":{"name":"Interdisciplinary Materials","volume":"4 5","pages":"763-774"},"PeriodicalIF":24.5,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/idm2.70005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145196980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Device Physics and Architecture Advances in Tunnel Field-Effect Transistors","authors":"Zehan Wu,&nbsp;Yifei Zhao,&nbsp;Fumei Yang,&nbsp;Jianhua Hao","doi":"10.1002/idm2.70011","DOIUrl":"https://doi.org/10.1002/idm2.70011","url":null,"abstract":"<p>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 (<i>SS</i> ≥ 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 <i>SS</i> 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.</p>","PeriodicalId":100685,"journal":{"name":"Interdisciplinary Materials","volume":"4 5","pages":"686-708"},"PeriodicalIF":24.5,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/idm2.70011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145196939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reassessing Machine Learning Techniques for Electrocatalyst Design: A Call for Robust Methodologies","authors":"Yoshiyasu Takefuji","doi":"10.1002/idm2.70009","DOIUrl":"https://doi.org/10.1002/idm2.70009","url":null,"abstract":"&lt;p&gt;Gu et al. conducted a comprehensive survey on the design and application of electrocatalysts powered by machine learning techniques [&lt;span&gt;1&lt;/span&gt;]. They presented a novel approach that utilizes Artificial Neural Networks (ANN) in conjunction with the SHAP (SHapley Additive exPlanations) method to optimize membrane electrode assemblies. The ANN model demonstrated high accuracy in predicting key performance metrics, achieving root mean square error (RMSE) values of 43.536 mW cm&lt;sup&gt;−2&lt;/sup&gt; for power density and 0.070 gPt kW&lt;sup&gt;−1&lt;/sup&gt; for platinum utilization. Additionally, the SHAP method was employed to identify the most influential features affecting the target outputs, providing valuable insights into the optimization process [&lt;span&gt;1&lt;/span&gt;].&lt;/p&gt;&lt;p&gt;However, this paper raises significant theoretical and empirical concerns regarding the use of ANN in conjunction with SHAP due to the model-specific nature of these techniques, which can lead to erroneous interpretations. It appears that Gu et al. may not fully grasp the fundamental principles underlying machine learning. In supervised machine learning models like ANN, two types of accuracy are crucial: target prediction accuracy and feature importance reliability. While target prediction accuracy can be validated against known ground truth values, the derived feature importances from models lack equivalent ground truth for validation. As a result, achieving high target prediction accuracy does not ensure that the feature importances are also reliable, since there are no established ground truth values for these features. The function call “explain = SHAP(model)” further indicates that SHAP may inherit and potentially amplify any biases present in the feature importances derived from the underlying model (ANN), leading to misleading interpretations of the results [&lt;span&gt;2-5&lt;/span&gt;]. This highlights the importance of critically evaluating both the predictions and the interpretability provided by model-agnostic methods like SHAP.&lt;/p&gt;&lt;p&gt;In light of these concerns, the paper advocates for a more robust and multifaceted approach utilizing unsupervised machine learning techniques, such as Feature Agglomeration (FA) and Highly Variable Gene Selection (HVGS). FA is a dimensionality reduction technique that aggregates similar features, thereby simplifying the data set and reducing noise, which can enhance the interpretability of the model and the reliability of its predictions. HVGS focuses on selecting a subset of features that exhibit significant variability across samples, ensuring that only the most informative features are retained for further analysis.&lt;/p&gt;&lt;p&gt;Following the feature selection process, the authors suggest employing nonlinear nonparametric statistical methods, such as Spearman's correlation, to assess the relationships between features and outcomes. Spearman's correlation evaluates the strength and direction of the association between ranked variables, making it particularly useful","PeriodicalId":100685,"journal":{"name":"Interdisciplinary Materials","volume":"4 5","pages":"786-787"},"PeriodicalIF":24.5,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/idm2.70009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145196784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-Entropy Design in Battery Materials for High Performance Electrochemical Energy Storage","authors":"Xin Hu,&nbsp;Zixu Wang,&nbsp;Hao Zhang,&nbsp;Yaduo Song,&nbsp;Junfeng Cui,&nbsp;Jinming Guo,&nbsp;Minglei Cao,&nbsp;Zhiqiang Wang,&nbsp;Yonggang Yao,&nbsp;Yunhui Huang","doi":"10.1002/idm2.70013","DOIUrl":"https://doi.org/10.1002/idm2.70013","url":null,"abstract":"<p>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.</p>","PeriodicalId":100685,"journal":{"name":"Interdisciplinary Materials","volume":"4 6","pages":"795-811"},"PeriodicalIF":24.5,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/idm2.70013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145625626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Triboelectric Nanogenerators for Condition Monitoring of Machines, Infrastructure and Environment","authors":"Mang Gao,&nbsp;Zhiyuan Yang,&nbsp;Yafeng Pang,&nbsp;Guozhang Dai,&nbsp;Chengkuo Lee,&nbsp;Junho Choi,&nbsp;Junliang Yang","doi":"10.1002/idm2.70004","DOIUrl":"https://doi.org/10.1002/idm2.70004","url":null,"abstract":"<p>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.</p>","PeriodicalId":100685,"journal":{"name":"Interdisciplinary Materials","volume":"4 5","pages":"645-685"},"PeriodicalIF":24.5,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/idm2.70004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145197090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Linking Lattice Strain and Fractal Dimensions to Non-monotonic Volume Changes in Irradiated Nuclear Graphite","authors":"David J. Sprouster,&nbsp;Sean Fayfar,&nbsp;Durgesh K. Rai,&nbsp;Anne Campbell,&nbsp;Jan Ilavsky,&nbsp;Lance L. Snead,&nbsp;Boris Khaykovich","doi":"10.1002/idm2.70008","DOIUrl":"https://doi.org/10.1002/idm2.70008","url":null,"abstract":"<p>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.</p>","PeriodicalId":100685,"journal":{"name":"Interdisciplinary Materials","volume":"4 5","pages":"714-718"},"PeriodicalIF":24.5,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/idm2.70008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145196738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Constructing Uniform Ionic Conductor Coatings on LiCoO2 Cathode to Realize 4.6 V High-Voltage All-Solid-State Lithium Batteries","authors":"Dabing Li,&nbsp;Yang Li,&nbsp;Hong Liu,&nbsp;Meng Wu,&nbsp;Xiang Qi,&nbsp;Ce-Wen Nan,&nbsp;Li-Zhen Fan","doi":"10.1002/idm2.70006","DOIUrl":"https://doi.org/10.1002/idm2.70006","url":null,"abstract":"<p>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.</p>","PeriodicalId":100685,"journal":{"name":"Interdisciplinary Materials","volume":"4 5","pages":"775-785"},"PeriodicalIF":24.5,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/idm2.70006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145196739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Magnesium Silicate Composite Patch With Neurovascular Regenerative Properties Promotes Diabetic Wound Healing in Mice","authors":"Shunxiang Xu,&nbsp;Hongwei Shao,&nbsp;Zheyu Jin,&nbsp;Jiankun Xu,&nbsp;Fanyan Deng,&nbsp;Yuantao Zhang,&nbsp;Liangbin Zhou,&nbsp;Samuel Ka-kin Ling,&nbsp;Congqin Ning,&nbsp;Wenxue Tong,&nbsp;Ling Qin","doi":"10.1002/idm2.70003","DOIUrl":"https://doi.org/10.1002/idm2.70003","url":null,"abstract":"<p>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.</p>","PeriodicalId":100685,"journal":{"name":"Interdisciplinary Materials","volume":"4 5","pages":"745-762"},"PeriodicalIF":24.5,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/idm2.70003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145197288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}