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.
{"title":"Device Physics and Architecture Advances in Tunnel Field-Effect Transistors","authors":"Zehan Wu, Yifei Zhao, Fumei Yang, 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<sup>−1</sup> 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}
<p>Gu et al. conducted a comprehensive survey on the design and application of electrocatalysts powered by machine learning techniques [<span>1</span>]. 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<sup>−2</sup> for power density and 0.070 gPt kW<sup>−1</sup> 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 [<span>1</span>].</p><p>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 [<span>2-5</span>]. This highlights the importance of critically evaluating both the predictions and the interpretability provided by model-agnostic methods like SHAP.</p><p>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.</p><p>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
{"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":"<p>Gu et al. conducted a comprehensive survey on the design and application of electrocatalysts powered by machine learning techniques [<span>1</span>]. 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<sup>−2</sup> for power density and 0.070 gPt kW<sup>−1</sup> 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 [<span>1</span>].</p><p>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 [<span>2-5</span>]. This highlights the importance of critically evaluating both the predictions and the interpretability provided by model-agnostic methods like SHAP.</p><p>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.</p><p>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}
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.
{"title":"High-Entropy Design in Battery Materials for High Performance Electrochemical Energy Storage","authors":"Xin Hu, Zixu Wang, Hao Zhang, Yaduo Song, Junfeng Cui, Jinming Guo, Minglei Cao, Zhiqiang Wang, Yonggang Yao, 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}
Mang Gao, Zhiyuan Yang, Yafeng Pang, Guozhang Dai, Chengkuo Lee, Junho Choi, Junliang Yang
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.
{"title":"Triboelectric Nanogenerators for Condition Monitoring of Machines, Infrastructure and Environment","authors":"Mang Gao, Zhiyuan Yang, Yafeng Pang, Guozhang Dai, Chengkuo Lee, Junho Choi, 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}
David J. Sprouster, Sean Fayfar, Durgesh K. Rai, Anne Campbell, Jan Ilavsky, Lance L. Snead, Boris Khaykovich
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.
{"title":"Linking Lattice Strain and Fractal Dimensions to Non-monotonic Volume Changes in Irradiated Nuclear Graphite","authors":"David J. Sprouster, Sean Fayfar, Durgesh K. Rai, Anne Campbell, Jan Ilavsky, Lance L. Snead, 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}
Dabing Li, Yang Li, Hong Liu, Meng Wu, Xiang Qi, Ce-Wen Nan, Li-Zhen Fan
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-Li1.2Al0.1Ta1.9PO8 (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.
全固态锂电池（ASSLBs）由于其不可燃性和提高能量密度的前景而被确定为下一代储能技术。提高正极材料的充电截止电压是提高assb能量密度的有效策略。然而，固态电解质（SEs）有限的氧化稳定性和阴极活性材料的结构和化学不可逆变化导致其电化学性能较差。本文采用简单球磨结合热处理的方法，在锂钴氧化物（LCO）表面合成了纳米li1.2 al0.1 ta1.9 po8 （LATPO）涂层。这种人工中间相有效地提高了阴极的结构稳定性和界面传递动力学，减轻了阴极/固体电解质界面上的连续副反应。结果表明，具有改性LCO阴极的asslb在0.1 C和4.0 V下具有203.5 mAh g−1的可逆容量（对应于4.6 V vs. Li+/Li），优异的循环稳定性（500次循环后容量保持率为85.4%），高面积容量（4.6 mAh cm−2）和良好的倍率容量（3 C时62 mAh g−1）。本研究强调了阴极表面改性对实现高电压下卤化物基asslb稳定循环的重要性。
{"title":"Constructing Uniform Ionic Conductor Coatings on LiCoO2 Cathode to Realize 4.6 V High-Voltage All-Solid-State Lithium Batteries","authors":"Dabing Li, Yang Li, Hong Liu, Meng Wu, Xiang Qi, Ce-Wen Nan, 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<sub>1.2</sub>Al<sub>0.1</sub>Ta<sub>1.9</sub>PO<sub>8</sub> (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<sup>−1</sup> at 0.1 C and 4.0 V (corresponding to the potential of 4.6 V vs. Li<sup>+</sup>/Li), superior cycling stability (85.4% capacity retention after 500 cycles), a high areal capacity (4.6 mAh cm<sup>−2</sup>), and a good rate capability (62 mAh g<sup>−1</sup> 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}
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.
{"title":"Magnesium Silicate Composite Patch With Neurovascular Regenerative Properties Promotes Diabetic Wound Healing in Mice","authors":"Shunxiang Xu, Hongwei Shao, Zheyu Jin, Jiankun Xu, Fanyan Deng, Yuantao Zhang, Liangbin Zhou, Samuel Ka-kin Ling, Congqin Ning, Wenxue Tong, 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-MgSiO<sub>3</sub> (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 Mg<sup>2+</sup> and SiO<sub>3</sub><sup>2</sup><sup>−</sup>. 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}
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.
{"title":"Outside Back Cover: Volume 4 Issue 4","authors":"","doi":"10.1002/idm2.70007","DOIUrl":"https://doi.org/10.1002/idm2.70007","url":null,"abstract":"<p><b>Outside Back Cover</b>: In the article of doi: 10.1002/idm2.12254, uniform Pt-modified mesoporous cerium oxide (Pt/mCeO<sub>2</sub>) 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.\u0000\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure>\u0000 </p>","PeriodicalId":100685,"journal":{"name":"Interdisciplinary Materials","volume":"4 4","pages":""},"PeriodicalIF":24.5,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/idm2.70007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144705555","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}
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.
{"title":"Outside Front Cover: Volume 4 Issue 4","authors":"","doi":"10.1002/idm2.12189","DOIUrl":"https://doi.org/10.1002/idm2.12189","url":null,"abstract":"<p><b>Outside Front Cover</b>: 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.\u0000\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure></p>","PeriodicalId":100685,"journal":{"name":"Interdisciplinary Materials","volume":"4 4","pages":""},"PeriodicalIF":24.5,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/idm2.12189","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144705667","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}
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.
{"title":"Remolding Microenvironmental Homeostasis for Enhancing Regenerative Hypertrophic Scar Treatment Based on Functionalized Microneedles","authors":"Shengjie Jiang, Jialiang Zhou, Liyun Wang, Xin Liu, Jiaming Sun, Yu Zhuang, Li Yu, Haiyan Li, Dejian Li, Zhaoyong Zou, Zhen Gao, Kaili Lin","doi":"10.1002/idm2.70000","DOIUrl":"https://doi.org/10.1002/idm2.70000","url":null,"abstract":"<p>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.</p>","PeriodicalId":100685,"journal":{"name":"Interdisciplinary Materials","volume":"4 5","pages":"728-744"},"PeriodicalIF":24.5,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/idm2.70000","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145196806","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}
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