Interdisciplinary Materials最新文献

{"title":"Inside Front Cover: Volume 4 Issue 5","authors":"","doi":"10.1002/idm2.70016","DOIUrl":"https://doi.org/10.1002/idm2.70016","url":null,"abstract":"<p><b>Inside Front Cover</b>: In the article of doi: 10.1002/idm2.12252, the evaporation of Mg leave vacancies, and is taken by Ag atoms which is unstable in their original sites. This helps to adjust carrier concentration without detriment carrier mobility and decrease the precipitation of Ag in the matrix for <i>α</i>-MgAgSb thermoelectric.\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 5","pages":""},"PeriodicalIF":24.5,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/idm2.70016","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145197265","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":"Inside Back Cover: Volume 4 Issue 5","authors":"","doi":"10.1002/idm2.70017","DOIUrl":"https://doi.org/10.1002/idm2.70017","url":null,"abstract":"<p><b>Inside Back Cover</b>: 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.\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 5","pages":""},"PeriodicalIF":24.5,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/idm2.70017","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145197266","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":"Outside Front Cover: Volume 4 Issue 5","authors":"","doi":"10.1002/idm2.12191","DOIUrl":"https://doi.org/10.1002/idm2.12191","url":null,"abstract":"<p><b>Outside Front Cover</b>: 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.\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 5","pages":""},"PeriodicalIF":24.5,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/idm2.12191","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145197302","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":"Outside Back Cover: Volume 4 Issue 5","authors":"","doi":"10.1002/idm2.70018","DOIUrl":"https://doi.org/10.1002/idm2.70018","url":null,"abstract":"<p><b>Outside Back Cover</b>: 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.\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 5","pages":""},"PeriodicalIF":24.5,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/idm2.70018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145197301","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":"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":"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}