ABSTRACT Natural fiber reinforced polymer composites are attracting increasing interest due to their sustainability and promising tribological performance. In this work, Acacia Arabica and Pencil Cactus fibers were employed as reinforcements to study the dry sliding wear behavior of polyester composites. Wear tests were conducted using a pin-on-disc apparatus at sliding speeds of 600, 800 and 1000 rpm under normal loads of 40, 60 and 80 N with a constant fiber content of 25%. Treated composites exhibited markedly lower wear loss and coefficient of friction compared to untreated counterparts, with wear loss ranging from 0.00501 to 0.01429 g and coefficient of friction between 0.301 and 0.615. The treated hybrid composite (TACFPC) demonstrated superior performance, achieving a maximum wear reduction of 41.6%. FESEM and EDX analyses confirmed enhanced fiber – matrix interfacial bonding, reduced fiber pull-out and improved surface stability in treated composites. Machine learning-based wear prediction models showed good predictive capability, with the deep neural network achieving an R2 = 0.8037, RSME = 0.000366 mm3/m, and MAE = 0.000300 mm3/m. The optimum condition for minimum wear was identified at 600 rpm, 40 N load and TACFPC, corresponding to a predicted wear rate of 0.002515 mm3 /m and an S/N ratio of 51.99 dB.

The pervasive occurrence of microplastics (MPs) in aquatic environments presents growing challenges for environmental monitoring. Conventional MP detection methods often require extensive pretreatment and struggle to differentiate mixed polymer compositions in complex matrices. Here, we present a pretreatment-free analytical method that integrates an electrostatically functionalized surface-enhanced Raman scattering (SERS) substrate with an interpretable deep learning framework. A hierarchically porous gold sponge modified with poly(diallyldimethylammonium chloride) facilitates efficient electrostatic enrichment and size-selective capture of negatively charged MPs, while embedded gold nanoparticles generate plasmonic hotspots for enhanced Raman signal amplification. A modular binary convolutional neural network framework (CNN) employing a one-vs-rest architecture enables accurate and interpretable classification of five representative MPs, i.e., polytetrafluoroethylene, polypropylene, polystyrene, polyvinyl chloride, and polyethylene terephthalate, achieving a precision of 0.9896 within the evaluated Raman data set. Gradient-weighted class activation mapping (Grad-CAM) analysis highlights key Raman bands characteristic of each polymer type, e.g., the C-C vibrations in the benzene ring of PET, supporting the chemical interpretability of the CNN model. The system was validated in urban tap water and natural surface waters, which represent both low-interference and heavy-metal-impacted complex matrices. This integrated platform provides a sensitive and adaptable approach for pretreatment-free identification of MPs in complex water matrices, demonstrating its potential for practical environmental analysis.

A new kind of flexible conductive polymer composites with positive temperature coefficient effect

This study aimed to develop and optimize a solid dispersion adsorbate (SDA)-based tablet of rivaroxaban to enhance its dissolution behavior and improve its oral delivery performance. Rivaroxaban SDAs were prepared using hydrophilic polymers (Soluplus® and Kollidon® VA64) via the fusion method, followed by adsorption onto porous carriers to obtain free-flowing powders suitable for tablet compression. A factorial experimental design was applied to evaluate the influence of polymer-to-drug ratio, polymer composition, and SDA-to-carrier ratio on drug release characteristics. The optimized formulation was characterized using FTIR, DSC, XRD, and SEM to assess drug–polymer compatibility and solid-state properties. In vitro dissolution studies were conducted and compared with a marketed rivaroxaban tablet. The optimized SDA tablet demonstrated significantly enhanced dissolution, achieving rapid and nearly complete drug release compared with pure rivaroxaban and the marketed product. Solid-state analyses confirmed the amorphous dispersion of rivaroxaban within the polymeric matrix without evidence of chemical incompatibility. The factorial model showed strong predictive performance, with validation bias below 4%. The SDA-based formulation effectively improved rivaroxaban dissolution and represents a promising strategy for enhancing the oral delivery of poorly soluble drugs; however, in vivo studies remain necessary.

Fiber-reinforced polymer (FRP) composites are increasingly crucial in the global effort to shift toward sustainable energy sources and more fuel-efficient transportation technologies. These materials are excellent in service; however, poor end-of-life (EoL) recycling options compromise the sustainability of FRPs. Current methods, including pyrolysis and mechanical recycling, are inadequate at preserving value from the fibers or polymers. By contrast, chemical recycling methods demonstrate promise to recover high-value fibers and chemicals from composite materials. Novel, chemically engineered resin systems further enable fiber/matrix separation to facilitate the recycling process. Particularly within the past 5 years, there has been a growing interest in targeting monomer recovery from composite matrices. This review provides a comprehensive overview of recent developments in selective depolymerization of current (legacy) composite materials and strategies for facile polymer disconnection in novel resin systems. Advantages and limitations across recycling strategies for both legacy and novel composites underscore key remaining challenges in composite sustainability.

Microplastics (MPs) are emerging contaminants in freshwater ecosystems, yet their ingestion by zooplankton remains poorly documented in large European lakes. This study provides the first evidence of MPs in zooplankton from Lake Como (Northern Italy), a major subalpine lake of ecological and socioeconomic relevance. Using high-resolution digital microscopy (detection limit: 2 µm), we quantified MPs across four sampling years (2016, 2017, 2018, 2025), capturing small size fractions typically overlooked by conventional methods. MPs were consistently detected, with mean concentrations of 0.06 ± 0.08 MPs ind.-1 and 1.14 ± 1.22 MPs mg-1 d.w., values comparable to those reported for freshwater zooplankton worldwide. No significant differences were observed between the lake's two main branches, supporting a lake-wide interpretation of exposure. Clear seasonal patterns emerged, with higher MPs loads in autumn and winter. These findings highlight the potential for MPs to enter pelagic food webs and contribute to a lake-wide baseline for future harmonized monitoring and polymer-specific assessments. The main limitation of this study is the exclusive quantitative approach, which does not provide qualitative information on polymer composition. Overall, these results underscore the need to integrate zooplankton-based monitoring into freshwater microplastic risk assessment frameworks.

Natural fibre-reinforced polymer composites are increasingly explored for sustainable structural applications but only limited information is available on the mechanical performance of composites reinforced with Vachellia nilotica, Prosopis juliflora and Vachellia leucophloea. This study aims to investigate whether the incorporation of these novel fibres with ceramic fillers can significantly improve the mechanical (elastic and tensile) properties of epoxy composites. The testing samples (15) are fabricated, containing hexagonal boron nitride, alumina and silicon carbide using the hand layup method and tested according to ASTM standards through impulse excitation and tensile testing. A Response Surface Methodology-Box-Behnken design combined with ANOVA / regression analysis is employed, followed by multi-response optimisation using Grey Relational Analysis. The optimal composite containing 4 wt.% Vachellia nilotica fibre with SiC filler exhibits a Young's modulus of 5.49 GPa, shear modulus of 2.18 GPa and tensile strength of 28.7MPa, with confirmation errors below 5%. The model exhibits good adequacy (R2 = 90.08%, Adj. R2 = 82.64%) with acceptable predictive capability (Pred. R2 = 62.70%) for optimisation. SEM results show that the SiC-filled composite possesses uniform dispersion and strong interfacial bonding, leading to superior mechanical performance. These results demonstrate that the proposed natural fibre hybrid composites provide enhanced stiffness and strength, confirming their suitability for lightweight structural and electrical insulation applications.

ABSTRACT This study investigates the mechanical, thermal, and morphological behaviours of hybrid polymer composites reinforced with basalt fiber, carbon fiber, and natural fillers, developed for structural purposes. Four composite types (S1–S4) were fabricated using the hand lay-up followed by compression molding, varying in fiber orientation and incorporation of coconut shell powder as a natural filler. Mechanical evaluations revealed that specimen S4 (C4B2–BD/UD – P – NF) demonstrated outstanding tensile strength of 334.02 N/mm2 and flexural strength of 618.33 N/mm2, attributed to its bi-directional carbon fiber layout and enhanced interfacial bonding facilitated by the filler particles. Thermal analysis using TGA – DSC showed superior thermal resistance in S4, which retained 43% of its mass at 800°C compared with 37% for S2. SEM observations of the fractured surfaces confirmed improved fiber-matrix interlocking and cohesive failure patterns in S4. Water absorption and hardness assessments further supported the material’s structural stability and durability. Overall, the findings highlight the potential of hybrid basalt – carbon fiber composites integrated with natural fillers for high-performance and eco-sustainable structural applications.

Radiation shielding performance of natural rubber composites: A Monte Carlo and XCOM-based study.

As a highly promising surface modification technique, spray coating technology has opened pathways for fabricating high-performance carbon fiber-reinforced polymer (CFRP) composites. This review systematically illustrates recent progress in spray-based fabrication of CFRPs, with a core focus on elucidating the intrinsic relationships among "process-structure-performance". First, the basic design principles of spray coating technology are elaborated in detail, encompassing fiber pretreatment, spray material selection, fundamental parameter control, and typical spray techniques. These factors collectively highlight the significant potential of spray technology for high-end applications in aerospace, marine engineering, and electronic devices. Furthermore, advanced characterizations of the microstructures formed via spraying are introduced, revealing that precisely constructed interface engineering and multidimensional networks can actively optimize the distribution and transport of stress, heat, and charge within the composite. In addition, the intrinsic mechanisms of performance enhancement are deeply discussed, establishing a complete framework for mechanical reinforcement, thermal management, and electromagnetic integration. Finally, the review systematically summarizes the current research landscape and future challenges, emphasizing the need for breakthroughs in process standardization, characterization techniques, and performance design. This review provides valuable theoretical guidance and technical support for the rational design, process optimization, and industrial popularization of spray coating technology on CFRP composites.

X-ray micro-computed tomography enables three-dimensional inspection of fiber-reinforced polymer composites. Quantitative mesostructural characterization remains challenging when voxel size does not permit reliable phase segmentation. This study presents a CT-based methodology for mesostructural characterization of unidirectional continuous-fiber polymer composites using line-profile descriptors. The approach extracts fixed one-dimensional intensity profiles within a defined internal volume of interest and computes a compact set of statistical and spatial descriptors. These include distributional moments, entropy, gradient-based measures, autocorrelation-derived correlation length, spectral band-energy ratios, and percentile-based run-length metrics. A technical quality control procedure verifies numerical consistency of the extracted feature tables. A one-at-a-time sensitivity analysis quantifies the influence of descriptor hyperparameters and identifies parameter groups that alter signal partitioning, particularly spectral cut-offs and run-length thresholds. Applied to pultruded composites, the descriptors resolve transverse heterogeneity across the section and systematic through-thickness trends in attenuation level, dispersion, spatial scale, and persistence of low-attenuation domains. The methodology provides a traceable low-dimensional representation of attenuation structure that can inform finite-element modeling through spatially parameterized material fields. Mechanical validation and descriptor–property calibration remain subjects for future work.

Integrating process replicability in the life cycle assessment of manufacturing fiber-reinforced polymer composites

This article aims at investigating the mechanical properties of kenaf fibres epoxy biocomposites with various fibre volume contents. Ukam plant (kenaf) fibres are natural fibers which have many advantages capable of making them suitable reinforcement for composite development. In this article, density tests were carried out on kenaf fibers and epoxy resin while tensile tests were carried out on kenaf fiber polymer composites. Six samples ranging from 0% to 50% fiber volume fractions were tested. From the results, the liquid density of epoxy resin is 1.10g/cm3 and cured density of epoxy resin is 1.03g/cm3. The ultimate strength obtained from the stress against strain graphs were between 14.00MPa and 88.8MPa while the highest value of Young's Modulus at 50% fiber volume fraction is 10.67GPa. This shows that mechanical properties of fibre reinforced composites increase with increase in fibre volume fractions.

ABSTRACT Adding flame‐retardant fillers to Glass Fiber Reinforced Polymer (GFRP) composites used for cabin car panels can improve flammability resistance and enhance passenger safety. Research on GFRP composites with nano Active Filler of Pumice Particle (nAFPP) and Sodium Silicate (SS) fillers has been conducted to evaluate the composites with FTIR, thermal properties (TGA‐DSC), flammability performance (ASTM D 635), and emission (smoke particle matter). The GFRP composite with 5 wt.% nAFPP and 5 wt.% SS exhibits the highest burning residue at 27.5% among the others, whereas the filler‐free composite shows the lowest burning residue at 20.99%. The DTG test indicates that composites with fillers decompose longer than those without. All composites with fillers exhibit amorphous nAFPP, indicated by sharp exothermic curves of crystallization. The DSC test shows higher heat flow during crystallization and melting, indicating exothermic and endothermic processes, respectively. Adding SS filler offers greater flammability resistance advantages than nAFPP. The filler‐free composite has the lowest flammability resistance with a burning rate of 0.494 mm/s, whereas the specimen with 7 wt.% nAFPP and 3 wt.% SS achieved the best flammability resistance with a burning rate of 0.215 mm/s. The SS proves more effective in enhancing flammability resistance. The use of fillers reduces smoke‐particle pollutants in GFRP composites, and SS is a better inhibitor of particle matter in smoke. Industries have an opportunity to improve GFRP composite products, and further investigation is needed to achieve the excellence of GFRP composites.

Load bearing characteristics of fiber sourced from nature impregnated polymer composites

The quick development of biomedical monitoring devices and the Internet of Things (IoT) has increased the need for communication systems that are not only compact and efficient but also capable of operating without frequent battery replacements. Energy-harvesting smart antennas offer a promising pathway by collecting energy from the surrounding environment, such as radio frequency (RF) signals, heat from the human body, and ambient light, and using it to power miniature sensors. This paper explores the theoretical foundations, design principles, and application opportunities of smart antennas based on advanced nanomaterials such as graphene, MXene, carbon nanotubes (CNTs), and flexible polymer composites. These materials allow antennas to be lightweight, highly conductive, flexible, and suitable for both wearable and implantable devices. The proposed antenna design shows reconfigurable behaviour they can adjust their frequency, polarisation, or beam direction depending on communication needs. Simulations based on electromagnetic and thermal multiphysics show how these Nanomaterial antennas perform when placed on the human body, exposed to varying environmental conditions, or used inside high-density IoT networks.

Defect-Engineered MOF-Regulated Solvation and Interfacial Stability in Composite Polymer Electrolytes for Dendrite-Free Lithium-Metal Batteries

ABSTRACT The post‐processing of 3D‐printed polymers is promising in the potential enhancement of the mechanical properties of porous components. However, the scant research on comprehensive understanding about the influence of infill density and pattern on resin infiltration efficiency and the consequent mechanical performance in epoxy‐filled carbon fiber‐reinforced poly(ethylene terephthalate)‐glycol (CF‐PETG) structures provides a significant research gap. To fill this gap, this study presents the post‐3D‐printing property enhancement of the CF‐PETG component via epoxy filling. The process starts with the additive manufacturing of CF‐PETG components with different infill patterns (cubic, trihexagonal, and gyroid) and then fills the printed voids with epoxy resin to enhance their mechanical attributes. Three different infill densities (20%, 35%, and 50%) were considered for each of the cubic (CU20, CU35, CU50), trihexagonal (TH20, TH35, TH50), and gyroid patterns (GY20, GY35, GY50), resulting in a total of nine types of specimens. The highest compressive strength (60 MPa) and specific compressive strength (13 MPa/g) were achieved for the TH35 specimen. The same specimen showed the highest specific flexural strength (7.6 MPa/g) and stiffness (2.4 kN/mm) as well. The specific energy absorption (SEA) assessment revealed that CU20 exhibits the best SEA value of 1.4 × 10 −2 kJ/g. The highest fracture toughness of 2.6 MPam 1/2 was shown by TH35. These findings underscore the significance of standardizing infill density and pattern in efficient resin distribution in 3D‐printed polymer composites.

High precision abrasive water jet machining of graphene/SiO₂ polymer composites: experimental investigation, optimization and surface integrity assessment

This article discusses thermal prediction and data-based prediction of E-glass fiber polymer composites reinforced with multi-walled carbon nanotube (MWCNT). The hand lay-up technique was used to make the laminates by the use of woven roving E-glass mats oriented at 0º/90º, 0º/45, and 0º/135º. The volume fractions of MWCNTs into the polymer matrix were 0%, 1%, 3%, 5% and 7%, which developed fifteen different composite configurations. The Thermogravimetric Analysis (TGA) was used to find out the thermal properties of the material including weight loss, degradation and decomposition behavior. These findings indicate that the orientation of fibers and MWCNT content are crucial determinants of thermal stability and that increased resistance exists at maximized filler loadings. A number of machine learning models were developed and trained using experimental data to increase predictive power. The performance of the models was measured using standard evaluation metrics and the importance of features analysis was conducted to identify important parameters on the thermal behavior. The integrated experimental and machine learning method offers a developed framework to predict thermal properties and optimize composite design, valuable information to be used in the advanced structural and thermal applications.