Composites Science and Technology最新文献

{"title":"Multiscale magnetic-electric synergy in CIP/MXene/epoxy nano-micro composites for ultra-broadband absorption and enhanced thermal conductivity","authors":"Zihao Chen ,&nbsp;Min Luo ,&nbsp;Weishi Jiao ,&nbsp;Yuheng Jiang ,&nbsp;Jinlong Xie ,&nbsp;Xiao Sun ,&nbsp;Shangzhi Chen ,&nbsp;Qiye Wen","doi":"10.1016/j.compscitech.2025.111360","DOIUrl":"10.1016/j.compscitech.2025.111360","url":null,"abstract":"<div><div>Electromagnetic waves (EMWs) in the millimeter-wave (MMW) to terahertz (THz) range have attracted significant attention due to their applications in wireless communications, security imaging, radar detection, and atmospheric or astrophysical research. However, the utilization of high-frequency EMWs presents challenges, particularly in electromagnetic compatibility and thermal management. In this study, we developed a composite coating with both excellent EMWs absorption and high thermal conductivity by integrating carbonyl iron powder (CIP) microspheres, MXene nanosheets, and epoxy (EP) resin. By harnessing the multiscale complementarity of CIP microspheres and MXene nanosheets, as well as the synergistic effects of low-frequency magnetic loss and high-frequency electric loss mechanisms, the composite coating achieved ultra-broadband absorption across the MMW (26.5 GHz) to THz (3 THz) range with a thickness of only 1.5 mm. The average reflection loss (ARL) of the coating is as low as −15.36 dB, and it maintains stability at temperatures up to 300 °C, with a high thermal decomposition temperature of 439.6 °C. These results indicate that the coating can effectively mitigate external electromagnetic interference (EMI) and device heating when applied to electronic components. This research offers an innovative solution to address both EMI effects and thermal management challenges in high-frequency broadband electromagnetic systems.</div></div>","PeriodicalId":283,"journal":{"name":"Composites Science and Technology","volume":"271 ","pages":"Article 111360"},"PeriodicalIF":9.8,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decoupling the effects of topographical roughness and oxidation on the interfacial properties of carbon fiber-epoxy composites","authors":"Filip Vuković ,&nbsp;Ben Newman ,&nbsp;Yanting Yin ,&nbsp;Gunther G. Andersson ,&nbsp;Luke C. Henderson ,&nbsp;Tiffany R. Walsh","doi":"10.1016/j.compscitech.2025.111354","DOIUrl":"10.1016/j.compscitech.2025.111354","url":null,"abstract":"<div><div>Carbon fiber composites under mechanical loading conditions must effectively transfer stresses from the relatively weak structural polymer matrix to the load-bearing carbon fiber. Oxidation treatments of the carbon fiber surface are a common strategy for improving the interface between fiber and matrix, and is understood to increase both the fiber surface roughness, as well as modify the fiber surface chemistry for better resin compatibility. However, it is challenging to decouple the effects of oxidation treatments on the fiber–matrix interface by experiment alone. Here, molecular dynamics simulations of topographically rough carbon fiber surfaces, both with and without oxidation, are interfaced with a thermoset epoxy matrix to decouple the impact of surface roughness and chemical interactions on the interfacial interaction between fiber and matrix. Smoother surfaces yield a greater enhancement of interfacial shear stress in fiber displacement simulations after oxidation, with the pristine graphite surface yielding the greatest increase relative to its non-oxidized value. Additionally, the results suggest that nanoscale fiber surface corrugation perpendicular to the fiber axis could be employed as a strategy to enhance the interfacial shear strength of composites. Overall, these simulations provide nanoscale insights regarding the interplay between surface roughness and chemistry of composite interfaces, which may inform future fiber surface treatments.</div></div>","PeriodicalId":283,"journal":{"name":"Composites Science and Technology","volume":"271 ","pages":"Article 111354"},"PeriodicalIF":9.8,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145019200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A triplet attention-enhanced deep learning approach to predict full-field stress of unidirectional CFRP composites with microvoids","authors":"Mengze Li ,&nbsp;Yifan Zhang ,&nbsp;Weiwei Qu ,&nbsp;Weidong Zhu ,&nbsp;Yinglin Ke","doi":"10.1016/j.compscitech.2025.111361","DOIUrl":"10.1016/j.compscitech.2025.111361","url":null,"abstract":"<div><div>Stress analysis is a critical step in composite material design. The high computational cost of multiscale finite element analysis necessitates the development of efficient surrogate modeling frameworks. To this end, this study develops a U-Net deep learning model enhanced with Triplet Attention mechanism for efficient prediction of full-field spatially nonlinear stress distribution in unidirectional CFRP composites with microvoids. Firstly, a Python based parametric modeling script is developed by integrating greedy and hard-core algorithms, enabling the construction of a microstructural database with randomly distributed voids and fibers. Then, the high-resolution stress field are computed by micromechanics-based finite element method. Subsequently, a U-Net architecture embedded with a Triplet Attention module is designed to improve the model's capability in extracting critical stress features. Finally, the proposed method is compared with several mainstream attention mechanisms including NonLocal, CBAM, and CrissCross using weighted MSE, MS-SSIM, R², and SNR. The comprehensive evaluation demonstrates significant performance improvements over the traditional U-Net framework: Weighted MSE is reduced by 74.7 %, MS-SSIM improved by 4.7 %, R² increased by 21.3 %, and SNR enhanced by 25.2 %. Further validation through varying dataset size and five-fold cross validation also confirms the model's reliability and robustness. This integrated approach simultaneously meets the technical requirements for computational efficiency, prediction accuracy, and engineering applicability in composite multiscale simulations, and thus provides a promising tool for applications in multiscale simulation, microstructural optimization, and uncertainty quantification.</div></div>","PeriodicalId":283,"journal":{"name":"Composites Science and Technology","volume":"271 ","pages":"Article 111361"},"PeriodicalIF":9.8,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hierarchically structured basalt fiber reinforced poly (arylene ether nitrile) composites for enhanced mechanical, electrical, and damage self-sensing performance","authors":"Libing Liu ,&nbsp;Dong Xiang ,&nbsp;Eileen Harkin-Jones ,&nbsp;Zhiyuan Liu ,&nbsp;Lei Lin ,&nbsp;Guoqian Xie ,&nbsp;Yusheng Gong ,&nbsp;Chunxia Zhao ,&nbsp;Hui Li ,&nbsp;Haibao Liu ,&nbsp;Menghan Wang ,&nbsp;Yuanpeng Wu","doi":"10.1016/j.compscitech.2025.111359","DOIUrl":"10.1016/j.compscitech.2025.111359","url":null,"abstract":"<div><div>Basalt fiber reinforced polymer (BFRP) composites have been finding more applications in the aerospace, automotive, energy, and civil engineering sectors, driving demand for versions of such material with more advanced properties such as damage self-sensing. In this study, a novel multifunctional BFRP has been developed. Firstly, a hybrid coating composed of graphene nanoplate, carbon nanotube, and poly (phthalazinone ether sulfone ketone) was developed. The coating was then used to modify the basalt fiber, while additional carbon nanotubes were integrated into a poly (arylene ether nitrile) matrix. This hierarchical approach, combining micro-scale fiber surface modifications and nano-scale matrix reinforcements, significantly increases the mechanical performance of the BFRP, boosting tensile strength and modulus by 44.1 % and 26.4 % respectively, and flexural strength and modulus by 47.9 % and 121.3 % respectively. Finite element analysis and molecular dynamics simulation reveal that the thickened fiber-matrix interface and enhanced interfacial interaction contribute to these mechanical improvements. Furthermore, the hierarchical structure elevates electrical conductivity from an insulating state to 7.0 × 10⁻⁴ S/m with only 0.9 wt% nanofillers, while enabling damage self-sensing functionality. The composite exhibits a high sensing sensitivity (gauge factor = 591) during the damage stage and an excellent cyclic stability during the elastic stage. Notably, enhanced self-sensing sensitivity at elevated temperatures was observed, highlighting the composite's potential for high-temperature applications. The presented work provides an effective solution for developing multifunctional BFRP with enhanced mechanical, electrical, and damage self-sensing properties, making such material suitable for a wide range of demanding applications such as structural-functional integrated load-bearing components.</div></div>","PeriodicalId":283,"journal":{"name":"Composites Science and Technology","volume":"271 ","pages":"Article 111359"},"PeriodicalIF":9.8,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144921169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A scalable, dispersion-aware framework for quantifying clustering severity in polymer nanocomposites and assessing mechanical impact","authors":"Behzad Hashemi Soudmand ,&nbsp;Amirhossein Najafi ,&nbsp;Rasool Mohsenzadeh ,&nbsp;Karim Shelesh-Nezhad","doi":"10.1016/j.compscitech.2025.111358","DOIUrl":"10.1016/j.compscitech.2025.111358","url":null,"abstract":"<div><div>Identifying a reliable threshold for cluster size in polymer nanocomposites remains a key challenge, limiting quantitative evaluation of dispersion and its effect on mechanical behavior. To address this, a novel Clustering Propensity Index (CPI) was introduced for automated quantification of nanoparticle clustering in polyoxymethylene (POM)/carbon black (CB)/calcium carbonate (CC) nanocomposites. Deep learning–based segmentation using YOLOv8 was first applied to SEM micrographs to extract particle area distributions (PADs). CPI was then computed by applying kernel density estimation (KDE) with adaptive bandwidths to define optimal bin intervals. As an innovative approach, a weighted frequency analysis, emphasizing larger particles, was used to quantify clustering severity. According to the results, CPI values dropped significantly to 0.0058 and 0.0040 at 1.5 and 3 wt% CC—representing 72.90 % and 81.31 % reductions from POM/CB (0.0214)—but increased to 0.0403 at 4.5 wt% due to re-agglomeration. An XGBoost-based two-variable model with CC content and CPI, as the inputs, was employed to predict mechanical responses. Feature importance analysis revealed CPI² as the most influential factor for impact toughness (SHAP ≈ 0.4), while CC content governed stiffness. The proposed framework provides a scalable, dispersion-aware methodology for quantifying clustering and systematically assessing its impact on mechanical properties.</div></div>","PeriodicalId":283,"journal":{"name":"Composites Science and Technology","volume":"271 ","pages":"Article 111358"},"PeriodicalIF":9.8,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144895820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-performance tribopositive PEG-PVA blends for smart energy harvesting: A pathway to self-powered security and healthcare monitoring","authors":"Sebghatullah Amini ,&nbsp;Rumana Farheen Sagade Muktar Ahmed ,&nbsp;Sindhuja Basarakodu ,&nbsp;Kavya Kallahalli Mohankumar ,&nbsp;Sangamesha Madanahalli Ankanathappa ,&nbsp;Krishnaveni Sannathammegowda","doi":"10.1016/j.compscitech.2025.111356","DOIUrl":"10.1016/j.compscitech.2025.111356","url":null,"abstract":"<div><div>The development of efficient and sustainable materials for triboelectric nanogenerators (TENGs) is critical for advancing self-powered technologies. In this study, a novel tribopositive polymer composite of polyethylene glycol (PEG) and polyvinyl alcohol (PVA) is introduced to fabricate high-performance TENGs. The incorporation of PEG in varying quantities of (0.1, 0.2, 0.4, 0.6, 0.8, 1.0, and 1.2 g) introduces additional polar functional groups (-OH), forming a robust hydrogen-bonding network with PVA and creating abundant charge interaction sites, which was confirmed through the DFT calculations. Systematic investigations of the PEG-to-PVA ratio reveal a significant improvement in triboelectric output, achieving an output voltage of 426.52 V and 82.65 μA. The practicality of the PVA/PEG-TENG is demonstrated through its ability to energize small electronic devices, including smartwatch, a sequence of LEDs and commercial capacitors. Additionally, the device is successfully integrated into a door security system, showcasing its potential for real-time security applications. Further, the tactile movement detection of bedridden or comatose patients is monitored using the PVA/PEG-TENG, highlighting its potential in healthcare applications. This study establishes the PEG-PVA composite as a promising material for versatile, high-performance, and sustainable energy-harvesting systems.</div></div>","PeriodicalId":283,"journal":{"name":"Composites Science and Technology","volume":"271 ","pages":"Article 111356"},"PeriodicalIF":9.8,"publicationDate":"2025-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144903786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A two-stage deep learning framework for predicting crack patterns and mechanical properties of unidirectional composites with void defects","authors":"Bochen Wang,&nbsp;Kai Huang,&nbsp;Licheng Guo","doi":"10.1016/j.compscitech.2025.111357","DOIUrl":"10.1016/j.compscitech.2025.111357","url":null,"abstract":"<div><div>Void defects in unidirectional composites critically govern crack initiation and propagation, leading to substantial degradation of transverse mechanical properties. To accurately characterize the influence of void defects on composites, this study proposes a novel two-stage deep learning framework that integrates U-net for crack pattern predictions and convolutional neural network for predicting stiffness and strength of unidirectional composites with void defects. To improve accuracy of mechanical property prediction, the innovative feature-fusion mechanism utilizes both material microstructures and corresponding crack patterns generated by the crack prediction network as input features. For the training of deep learning framework, a comprehensive dataset, generated through micromechanical modeling, contains randomly distributed fibers, inter-fiber voids, matrix voids, and resin-rich areas. The proposed framework achieves high-precision predictions of crack patterns and mechanical properties while significantly reducing computational costs, demonstrating strong potential for applications in material design.</div></div>","PeriodicalId":283,"journal":{"name":"Composites Science and Technology","volume":"271 ","pages":"Article 111357"},"PeriodicalIF":9.8,"publicationDate":"2025-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144913213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mesoporous silica reinforced triple-network poly-zwitterionic hydrogel with high-strength and excellent anti-fouling performance","authors":"Lei Wang ,&nbsp;Wen Li ,&nbsp;Lin Cao ,&nbsp;Huimeng Feng ,&nbsp;Yan Liu ,&nbsp;Xiao Sun ,&nbsp;Jia Cheng ,&nbsp;Meiyan Yu ,&nbsp;Shougang Chen","doi":"10.1016/j.compscitech.2025.111355","DOIUrl":"10.1016/j.compscitech.2025.111355","url":null,"abstract":"<div><div>Zwitterionic hydrogels have garnered considerable attention due to their excellent anti-fouling properties and biocompatibility. However, the application of zwitterionic hydrogels in the seawater is usually limited by excessive swelling and weak mechanical properties caused by the “anti-polyelectrolyte” effect. Moreover, relying on a single anti-fouling mechanism cannot provide long-term protection. In this study, a zwitterionic nanocomposite triple-network hydrogel (UM-T) fabricated through a combination of nanoparticle reinforcement and a multiple network strategy is proposed. This hydrogel exhibits outstanding mechanical strength and exceptional anti-fouling performance. Surface-modified nanoparticles (Ugi-MSNs) served as “anchoring” effect for the molecular chains within the hydrogel enhance network entanglement, while the robust electrostatic interactions within the triple-network structure dissipate energy, thus improving toughness. As a result, the hydrogel demonstrated remarkable mechanical properties in saline environments with a compressive fracture stress of 19.3 MPa, toughness of 2.4 MJ m⁻³, and modulus of 0.63 MPa. Additionally, capsaicin loaded in Ugi-MSNs enables sustained release of capsaicin for bactericidal effects. The hydrogel effectively resists the attachment of proteins, bacteria, and cells, making it highly effective in marine anti-fouling applications.</div></div>","PeriodicalId":283,"journal":{"name":"Composites Science and Technology","volume":"271 ","pages":"Article 111355"},"PeriodicalIF":9.8,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144892884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensive quantification of electrically conductive networks with complex morphologies in carbon nanotube–polymer composites","authors":"Won Ho Shin ,&nbsp;Sung Youb Kim","doi":"10.1016/j.compscitech.2025.111344","DOIUrl":"10.1016/j.compscitech.2025.111344","url":null,"abstract":"<div><div>Carbon nanotube (CNT)-based polymer nanocomposites exhibit highly variable electrical properties due to the complex interplay between filler geometry and network formation. However, previous studies have largely lacked quantitative analysis of the internal network structure, and more importantly, the underlying mechanisms by which individual geometrical factors affect network connectivity remain poorly understood. To address this limitation, the present work provides a systematic investigation into the role of key CNT parameters on network morphology and resulting electrical conductivity. A Monte Carlo model was developed to incorporate realistic CNT features, including statistical length distributions and waviness represented via splines. The model was validated against experimental results and subsequently used to analyze the influence of CNT geometry on conductive network formation. A depth-first search algorithm was then applied to decompose the simulated networks into discrete conduction paths, enabling quantification of both path count and path length. Based on these findings, unified metrics are proposed that encapsulate the combined effects of multiple morphological factors and provide practical descriptors for predicting and optimizing the electrical performance of CNT networks.</div></div>","PeriodicalId":283,"journal":{"name":"Composites Science and Technology","volume":"271 ","pages":"Article 111344"},"PeriodicalIF":9.8,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144890987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synthesis of Ni-diamond hybrid reinforced carbon fiber/epoxy thermally conductive composites with \"Rod-on-particle microstructure\" via composite electrodeposition","authors":"Tao Jiang ,&nbsp;Ying Wang ,&nbsp;Lixue Xiang ,&nbsp;Bo Tang ,&nbsp;Shanshan Shi ,&nbsp;Chunxia Jiang ,&nbsp;Rongbin Li ,&nbsp;Yifan Li ,&nbsp;Wei Yu ,&nbsp;Xinfeng Wu ,&nbsp;Wenge Li ,&nbsp;Yuantao Zhao ,&nbsp;Kai Sun ,&nbsp;Runhua Fan ,&nbsp;Jinhong Yu","doi":"10.1016/j.compscitech.2025.111351","DOIUrl":"10.1016/j.compscitech.2025.111351","url":null,"abstract":"<div><div>Epoxy-based composites, leveraging their lightweight nature and design flexibility, have emerged as critical materials for thermal management and electromagnetic shielding applications. The rapid advancement of high-functional fields have driven the demand for multifunctional epoxy composites. In this study, nickel-diamond (N-D) hybrid thermally conductive fillers were co-deposited onto polyacrylonitrile (PAN)-based carbon fiber felts via composite electrodeposition. Using Watts bath as the electrodeposition solution, we precisely controlled both current density and diamond particle concentration to achieve uniform dispersion of diamond particles and strong interfacial bonding within the carbon fiber matrix, thereby optimizing the comprehensive performance of epoxy composites. Under the optimal conditions of 2 A/dm² current density and 16 g/L diamond concentration, the fabricated epoxy composite demonstrated superior thermal conductivity (3.17 W/mK), excellent electrical conductivity (37.7 S/cm), and a significantly reduced friction coefficient (0.44). Further thermal management tests demonstrated the composite's exceptional heat-transfer performance, offering a viable solution for thermal dissipation in highly integrated electronics.</div></div>","PeriodicalId":283,"journal":{"name":"Composites Science and Technology","volume":"271 ","pages":"Article 111351"},"PeriodicalIF":9.8,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}