Abstract The need related to the continuous rise of sustainable structural materials with superior vibration attenuation has intensified the study of bio-based sandwich materials in recent times. To conduct this research, a hybrid sandwich composite was fabricated using woven banana-cotton fabric face sheets and cork agglomerate cores via using vacuum bagging methodology and systematically tested on its mechanical, vibrations and impact performance characteristics. Tensile, free-decay vibration, modal vibration, Charpy and Izod impact tests were used to test experimentally the behavior of monolithic banana fabric laminates and sandwich configurations with different core cork thicknesses. Finite element models were prepared in Abaqus to reproduce the mechanical and vibration response and has been compared with experimental work. A sustainable sandwich composite made of banana-cotton woven fabric face sheets and cork agglomerate cores was designed and tested for vibration attenuation and multifunctional structural applications. The findings indicate that although the addition of cork cores causes the tensile strength to reduce in a controlled fashion, it has a considerable effect in complementing the vibration and absorption of impact energy. It was found that damping ratios had increased over 200% with increased cork core thickness and that natural frequency had decreased roughly 25%-40% which signifies that the damping capability of the panel is demonstrated.
Abstract Intelligent and multifunctional composite materials have transitioned from passive structural systems to adaptive platforms that can sense, actuate, convert energy, and self-repair. Nonetheless, despite its expansion, the domain remains disjointed. Mechanisms, manufacturing methods, and performance measurements are frequently examined in isolation, hindering the transition to dependable engineering systems. This review rigorously assesses contemporary smart composite designs by linking functional mechanisms with interface engineering, manufacturing scalability, and long-term dependability. It demonstrates that several documented high-performance systems depend on laboratory-specific circumstances, but practical implementation is hindered by conflicting property requirements, interfacial instability, and environmental degradation. This review’s distinctiveness lies in creating a cohesive framework that connects multiscale modelling, sophisticated manufacturing, and multifunctional performance evaluation to discern design trade-offs rather than focusing solely on material enhancements. Current advances, such as artificial intelligence-assisted material discovery, programmable metamaterials, structural energy storage, and bio-inspired designs, are evaluated in terms of manufacturability and durability rather than solely functional output. Special emphasis is placed on novel sustainable composites and digital-twin-enabled predictive maintenance. Significant hurdles persist in scalable manufacturing, consistent multifunction integration, power management, and lifetime stability under cyclic loads and adverse conditions. Resolving these difficulties necessitates integrating material design with system-level optimisation and standardised assessment methodologies. This paper offers a prospective framework for advancing smart composites from experimental materials to reliable engineering components in aircraft, robotics, healthcare, and energy infrastructure.
Abstract The carbon felt electrode plays a critical role in vanadium redox flow batteries. However, its electrochemical performance is often limited by poor reaction reversibility arising from the strong solvation of vanadium ions in the electrolyte. This strong solvation hinders efficient electron transfer at the electrolyte-electrode interface, leading to increased interfacial resistance. In this study, a MXene-SnO 2 composite is coated onto carbon felt to regulate the interfacial environment and overcome these limitations. The oxygen-containing surface groups of SnO 2 are expected to interact with hydrated vanadium ions, which may facilitate partial weakening of the solvation shell prior to the redox reaction, while MXene provides high electrical conductivity and stabilizes interfacial charge transfer. Electrochemical results confirm that the modified electrode exhibits enhanced reversibility and reduced interfacial resistance compared with pristine carbon felt. Therefore, regulating interfacial interactions at the electrode surface through MXene-SnO 2 modification represents a promising strategy.
Abstract The growing demand for sustainable materials has positioned lignocellulosic fiber-reinforced polypropylene composites as promising alternatives to conventional composites. However, accurately predicting key mechanical properties, including Young’s modulus (YM), ultimate tensile strength (UTS), and Elongation at Break (ELO), remains difficult because of the inherent variability in natural fiber composition. This study addresses this challenge by developing artificial neural network (ANN) models specifically tailored to Mediterranean lignocellulosic fibers derived from lemon and fig leaves. A computational dataset was generated from a limited set of mechanical, physical, and chemical-property observations reported in published studies on lemon- and fig-leaf-reinforced polypropylene composites. The literature-based property ranges were discretized into representative values and systematically combined to create ANN training observations. Controlled noise, outlier screening, and normalization were then applied to enhance data variability and suitability for model development. Several ANN architectures were evaluated, including narrow, medium, wide, bilayered, and trilayered networks. The bilayered ANN with a 10 × 10 architecture achieved the best performance for YM, with an R 2 of 0.99 and RMSE values of 0.0378 for training and 0.0411 for testing. For UTS, the trilayered 10 × 10 × 10 ANN achieved an R 2 of 0.99, with RMSE values of 0.0409 for training and 0.0414 for testing. The results demonstrate a clear trade-off between network complexity and predictive performance, while simpler architectures remained competitive for ELO prediction. Overall, the study highlights the potential of ANN models to optimize green composites, support industrial adoption, and advance the development of high-performance, eco-friendly materials. Future work will extend the models to additional natural fibers and composite formulations.
Bio-based reinforcements are being progressively integrated into the polymer matrix to produce lightweight, environmentally friendly composites with superior functional properties. These reinforcements reduce the environmental impact of the composite while improving thermal, damping, electrical, and mechanical properties so that they offer promise for light-weight structural and industrial applications. This study investigates the influence of Caryota fiber and almond biochar microfiller loading on vibration damping, dielectric constant (DC), and thermal conductivity. Taguchi L9 array of hybrid CF (10-30 wt.%)/ABC (4-12 wt.%) reinforced epoxy composites are developed by wet hand lay-up with compression pressure. ABC filler enhances thermal conductivity, DC, and frequency damping of interleaved composites. The maximum TC and DC for the designation CF10/ABC12 composite were 6.72 and 0.88 W mK-1, respectively. Additionally, the investigation of the vibration behavior demonstrated improved damping capabilities, suggesting the possibility of vibration dampening applications. The maximum natural fiber (NF) for the CF20/ABC8 composite is 155 Hz MPa, which is an improvement of 58.1% and 51.9% over the FC20/epoxy and ABC8/epoxy composite NF, respectively. Due to enhanced thermal, dielectric, and damping properties, CF/ABC epoxy composites suit packaging, machinery, automotive interiors, and lightweight structural parts.
Abstract Nowadays, the uses for nanomaterials are continuously expanding; their applications have been found to have new and interesting uses in bio-oriented fields. Copper and graphene-containing composites are especially interesting for medical applications, both for therapeutic and toxicological reasons. Therefore, this work aims to investigate two new composites of titanium dioxide nanoparticles containing copper (I) and (II) oxides (2% w/w) and (reduced or unreduced) graphene oxide (10% w/w). Their cytotoxic effects and photodynamic attributes were investigated on healthy skin cells (HaCaT) and melanoma cells (A375) exposed to visible light (blue, green, or red), in the search for a promising candidate for red LED therapy. The physical-chemical characterization was performed by transmission/scanning electron microscopy, x-ray photoelectron spectroscopy, x-ray powder diffractometry, and UV–Vis spectrometry. MTT, LDH, and Griess assays were used to assess cytotoxicity, cell membrane damage, and NO induced cell stress. Our results demonstrate that the composite with reduced graphene oxide does not affect healthy cells but has an antitumoral effect when activated by red LED. In addition, it also protected healthy skin cells from the known deleterious effects of long exposure to blue light. The study was supplemented with the antimicrobial effect, and our findings indicate that these composites have biological activity against bacteria ( Escherichia coli or Staphylococcus aureus ).
Piezoelectric composite-based nanogenerators are attracting attention as self-powered sources for next-generation wearable and portable electronic devices. The performance of piezoelectric composites is highly dependent on the connectivity structure. Conventional 0–3 type composites, in which piezoelectric fillers are randomly dispersed within a polymer matrix, suffer from reduced piezoelectric performance due to inefficient stress transfer. This review paper systematically investigates research that has enhanced piezoelectric performance by strategically designing the connectivity structure of piezoelectric composites. The correlation between various connectivity patterns, such as 1–3, 2–2, 3–1, and 3–3 types, and the piezoelectric output performance is analyzed. In particular, the three-dimensionally interconnected 3–3 structure has been demonstrated to be effective in improving output performance by facilitating continuous pathways for mechanical stress transfer. Additionally, fabrication strategies for designing these structures using various manufacturing techniques are discussed. In conclusion, this paper suggests the potential applicability of these high-performance composites in fields such as self-powered sensors, biomedical devices, and wearable electronics.
This review provides analysis of polymer composite scintillators, examining their fabrication techniques, optical and scintillation properties. Polymer composite scintillators represent an important class of radiation detection materials that combine the mechanical flexibility and processability of polymers with the high stopping power and scintillation efficiency of inorganic materials. Recent advances in nanomaterial synthesis, interface engineering, and manufacturing technologies have significantly expanded the performance envelope. This review systematically examines solution processing, melt processing, electrospinning, and additive manufacturing approaches for fabrication; light yield, energy resolution, and radiation hardness as critical performance metrics. Future research directions involving novel materials, advanced manufacturing techniques, and artificial intelligence-driven optimization are explored.
To improve the tensile properties of continuous carbon fiber-reinforced polylactic acid composites fabricated by fused deposition modeling 3D printing, this paper optimized process parameters including printing layer height, extrusion width, temperature and speed through single-factor experiments and an L9(34) orthogonal test, and obtained the optimal combination A1B3C1D2. Tensile tests show that the strength and modulus of the specimens under these parameters are greatly improved compared with neat PLA, and are superior to those of optimal groups from single-factor and orthogonal tests. Microscopic morphology reveals uniform fiber dispersion, favorable interfacial bonding and low porosity in the optimized specimens. The fracture is dominated by fiber-matrix synergistic load bearing, leading to better tensile properties and ductility. This work provides data and process support for the application of such composites in lightweight components.
The pH of a catalyst precursor solution used for wet impregnation strongly influences catalyst formation and, consequently, catalytic activity. However, its role in chemical vapor deposition synthesis of carbon nanotube (CNT) remains unexplored. Here, we systematically investigate how the initial pH of the impregnation solution (pH(i)) affects Co adsorption on SiO2 and, in turn, CNT growth performance. Co uptake increased with pH(i), following the order pH(i) = 13 > 7 > 10 > 4, whereas CNT yield was maximized at pH(i) = 7 and 10 and minimized at pH(i) = 13. X-ray diffraction patterns for all catalysts showed only the broad amorphous SiO2 halo (2 theta approximate to 22 degrees), with no detectable reflections attributable to metallic Co or CoOx, suggesting highly dispersed and/or poorly crystalline Co species across all pH(i). Raman spectroscopy revealed a progressive attenuation of the 950-1100 cm(-1) region with increasing pH(i), with this band nearly extinguished at pH(i) = 13, suggesting disruption of the silica network under strongly basic conditions. Scanning transmission electron microscopy-energy dispersive x-ray spectroscopy mapping further revealed pH(i)-dependent Co distributions: sparse and intermittent Co signals at pH(i) = 4, more uniform dispersion at pH(i) = 7 and 10, and strong but locally enriched Co signals at pH(i) = 13. Collectively, these results show that higher metal uptake does not necessarily yield growth-effective Co active sites. Instead, preserving structural integrity of support while stabilizing uniformly dispersed Co species is critical for high CNT yield. Accordingly, impregnation at a pH near (or slightly above) the support's point of zero charge is most favorable for maintaining the structural integrity of support and promoting CNT growth.
This study introduces a novel and scalable method for improving the dispersion of carbon nanotubes (CNTs) in composite materials by utilizing hollow aluminosilicate fly ash cenospheres (FACs) as micro-scale carriers. The fabrication process leverages cohesion of the FACs and CNTs in an aqueous suspension, leading to the formation of a hybrid CNT-FAC filler. This approach mitigates CNT agglomeration and ensures their uniform distribution within the composite matrix. Two types of composites were fabricated and characterized: a transformer oil-based system to investigate pressure-dependent conductivity and a polyurethane (PU)-based system to evaluate electromagnetic (EM) shielding properties. The oil-based composites containing single-walled CNTs (SWCNTs) demonstrated a reproducible and reversible increase in electrical conductivity above a pressure threshold of 1 MPa, attributed to the formation of a percolating conductive network. In contrast, anisotropic compression caused an irreversible conductivity enhancement due to matrix densification. The PU-based composites with 20 wt% of the SWCNT-FAC filler exhibited a superior EM shielding performance, with a minimum return loss of -17 dB at similar to 5 GHz, significantly outperforming composites with conventionally dispersed CNTs. The findings demonstrate that using FACs as CNT carriers is an effective and sustainable strategy for developing lightweight, multifunctional composites with tunable electrical and EM properties for applications in sensing and EMI shielding.
Starting with the 3D structure of interconnected pores of a shape memory metal foam, interpenetrating phase composites were obtained by filling its cells with a viscoelastic polymer. A powder metallurgy method to obtain a highly porous Cu-14Al-3Ni shape memory foam with millimeter size cells is detailed. Composites were then fabricated infiltrating silicone rubber with different viscosities under vacuum. A complete structural characterization with the aid of x-ray tomography is presented, identifying the best combination of foam porosity and silicone viscosity required to achieve full foam impregnation. The mechanical response under compression of the metal foam is compared with that of the composites. The presence of a polymer phase inside the cells, subjects the foams to a combined compression-tension stress state, diminishing their load bearing capacity. However, the silicone rubber improves the strain resistance of the foam by supporting the cells during crack propagation within the metal phase.
This study presents the structural design and structural integrity evaluation of carbon fiber reinforced plastic(CFRP) link arms applied on a ropeway structure inspection manipulator. The ropeway structure inspection manipulator is a device that remotely inspects defects and wear of ropeway wheel devices of rope facilities used as transportation, such as cable cars or ski lifts. Conventional inspection of ropeway wheel assemblies has relied on on-site visual checks by personnel at elevated locations, which poses safety risks and limits measurement consistency. In contrast, a ropeway inspection manipulator system is being developed domestically to enhance inspector safety and enable precise measurements using a laser scanner and camera mounted on the end device. To enhance the stiffness of the manipulator links and achieve a lightweight design, CFRP was adopted. To confirm the applicability of a composite manipulator to ropeway system inspection, the laminate stacking pattern was treated as a design variable and a deflection analysis of the composite link arms was performed. For the motion reflected in the analysis, the maximum deflection of the end device equipped with the camera was confirmed in the motion state where the inspection manipulator, which occurred to have the maximum deflection, was horizontally spread out. Based on the deflection analysis, we selected a stacking pattern which reduced deflection about 72.13% relative to the baseline model. Structural integrity was evaluated using a flexible multibody dynamics (FMBD) model that incorporated the selected layup pattern under the actual ropeway wheel inspection motion. For the failure evaluation of the composite materials, the Tsai-Wu failure criterion was adopted. As the results, the maximum Tsai-Wu index was 0.08, in the link arms were less than 1, confirming structural integrity.
The reliable design of fiber-reinforced epoxy composites is still challenging because of their complex heterogeneous nature. The limitations of existing predictive models are the joint estimation of tensile and flexural properties for both pure and hybrid constituents. Traditional characterization of these properties is dependent on costly time-consuming destructive testing and, at the same time, simulated predictive models are prone to suffering from oversimplified assumptions. To fill this gap, machine learning (ML) models have been implemented in this study with experimental data of 54 laminates with different fiber constituents (pure, bi- and tri-hybrids), stacking sequences and ply count. ML models included baseline, ensemble and neural networks, which were trained, validated and tested where design and testing parameters were input features for the prediction. Experimentally, it was observed that Kevlar -cross 4 Ply showed the highest tensile strength of 326.40 MPa and carbon-cross 4 ply showed the highest flexural strength of 513.33 MPa. Out of the hybrids, Kevlar-glass cross 4 ply showed the best tensile performance (373.46 MPa). In the prediction, the k-nearest neighbor model was found to be the most robust model (mean squared error = 634.76, mean absolute error = 10.98 and R2 = 0.83), followed by the ensemble random forest model with a balanced performance (R2 = 0.74) and poor performance of the artificial neural network (R2 = 0.41). This work sets up a comprehensive ML system which shows the viability in material selection, which averts the need for extensive experimentation and speeds up composite design.
The increasing demand for high-performance and sustainable lithium-ion batteries (LIBs) has led to the exploration of alternative anode materials beyond traditional graphite. Hard carbon, a disordered form of carbon characterized by expanded interlayer spacing and a high density of defect sites, exhibits promising electrochemical properties, particularly under fast-charging and low-temperature operation conditions. In this study, waste polyethylene terephthalate (PET) was converted into hard carbon anodes through pyrolysis at two different temperatures: 1000 degrees C and 1500 degrees C. The objective was to examine how the thermal treatment affects the structural characteristics and lithium ion storage behavior of the materials. X-ray diffraction, Raman spectroscopy, x-ray photoelectron spectroscopy, and transmission electron microscopy analyses indicated that the lower-temperature (1000 degrees C) heat-treated PET-derived hard carbon (pHC-L) had a more disordered structure with larger interlayer spacing and a higher concentration of defects. In contrast, the higher-temperature (1500 degrees C) heat-treated sample PET-derived hard carbon (pHC-H) showed increased graphitic ordering and fewer surface-active sites. At 20 mA g-1, the hard carbon produced at the lower heat-treatment temperature delivered 186.94 mAh g-1, compared with 130.15 mAh g-1 for the higher-temperature product; this improvement is attributed to the larger interlayer spacing and higher defect/micropore population, which promote sloping-type lithium-ion adsorption and pore-filling. Meanwhile, pHC-H demonstrated better rate performance with reduced polarization and enhanced reversibility. Both electrodes displayed a gradual increase in capacity during cycling, indicating structural activation attributed to solid-electrolyte interphase stabilization and improved accessibility of the electrolyte. These findings indicate that waste PET-derived hard carbon can be effectively optimized through pyrolysis temperature to achieve a balance between capacity and rate capability. This presents a sustainable and versatile platform for anode materials in next-generation LIBs.
This study presents a numerical investigation of the failure load of curved composite beams subjected to four-point flexural loading. Two key parameters were separately considered: (1) the stacking sequence and (2) the curvature radius of the composite beams. Delamination, identified as the predominant damage mode in curved composite laminates, was modeled in Abaqus (R) using the cohesive zone model (CZM). Additionally, failure loads and load-displacement curves were generated for comparative analysis. Beams with a higher number of unidirectional layers demonstrated greater critical bending loads. Specifically, the [0]20 sample exhibited the highest predicted failure load of 590.3 N, compared to 484.0 N for the [0/90]5S sample and 455.8 N for the [45/0/- 45/90/0]2S sample. Furthermore, the failure load increased significantly with larger curvature radii, ranging from 3.0 mm to 12.0 mm with 3.0 mm increments. The predicted failure loads were 394.8 N, 555.8 N, 782.4 N, and 1010.8 N for radii of 3.0 mm, 6.0 mm, 9.0 mm, and 12.0 mm, respectively. The simulation results showed good agreement with previous experimental data. Overall, the findings confirm that the CZM approach is effective for analyzing the out-of-plane strength of curved composite beams.
Bio-inspired staggered composites, renowned for their exceptional mechanical properties, offer a promising blueprint for advanced material design. While linear viscoelasticity is commonly used to model their energy dissipation, the unique strain-rate-dependent behavior of shear-thickening materials presents an untapped potential for superior damping performance. The fundamental mechanisms governing the interaction between a nonlinear matrix and the classic 'brick-and-mortar' architecture remain, however, largely unexplored. This study addresses this gap by developing a dynamic shear-lag model that incorporates a nonlinear, power-law viscous matrix. The model investigates the dynamic response and energy dissipation mechanisms of these composites under cyclic loading. Validated by finite element analysis, our results reveal that the matrix's shear-thickening nonlinearity significantly enhances energy dissipation, with the loss factor exceeding unity-a performance rather difficult for conventional linear viscoelastic models. A comprehensive parametric study demonstrates that the damping performance is non-monotonically dependent on the reinforcement-to-matrix modulus contrast, thickness ratio and the reinforcement aspect ratio. Optimal energy dissipation is achieved not at the extremes, but within specific ranges of these parameters, highlighting a clear pathway for microstructural design. This work elucidates the role of nonlinear viscosity in bio-inspired composites and provides a theoretical framework for designing damping materials with superior energy absorption capabilities.
In this work, we have synthesised ZnO nanoparticles (Nps) with an average diameter of 9.5 nm via a soft chemical route. These ZnO Nps were encapsulated within a mesoporous SiO2 matrix through a microemulsion-assisted sol-gel procedure to form ZnO@SiO2 (ZS) composite. Then, silver Nps were successfully impregnated onto the SiO2 matrix surface to form ZnO@SiO2@Ag (ZSAg) nanocomposite. Transmission electron micrographs and scanning electron microscope analysis confirmed that nano-sized Ag of size about 5 nm is homogeneously incorporated into the SiO2 matrix, which formed a Schottky barrier in the structure and narrowed the band gap energy from 3.1 to 2.58 eV. The samples have been evaluated for photocatalytic degradation of methylene blue (MB) under irradiation of UV and sunlight. ZnO demonstrated excellent photocatalytic performance for the degradation of MB under both UV and sunlight irradiation with degradation rates of 0.04 and 0.03 min-1, respectively and swiftly degraded the MB within 90 min. On the other hand, ZSAg degraded the MB dye faster with a rate of 4.5 x 10-3 under sunlight as compared to UV light with a rate of 3.5 x 10-3. In addition, ZSAg with NaBH4 exhibited superior catalytic activity for the reduction of Rhodamine B and 2-nitrophenol. These research findings highlight that the ZSAg composite is a promising potential catalyst, providing stability, reduction capability and utilising sunlight for the catalysis process.
Wearable sensors based on the triboelectric effect have been developed as powerful tools for healthcare monitoring recently, enabling the acquisition of physiological signals. However, in the case of the pulse, the mechanical characteristics make it imperative due to the tiny pressure and low-frequency that the sensor measuring it be highly sensitive. This work focuses on designing a highly sensitive, diameter-tuned triboelectric pressure sensor (DPS) by controlling the applied voltage during P(VDF-TrFE) electrospinning, to achieve a higher triboelectric effect and thus greater sensitivity of the DPS. Specifically, by varying the applied voltage, we quantitatively compared changes in fiber diameter, beta-phase fraction, and crystallinity. We present that higher electrospinning voltage yields thinner fibers with increased beta-phase content and crystallinity, which enhance dielectric polarization and strengthen charge retention, thereby boosting the triboelectric effect. Consequently, the resulting high-sensitivity DPS precisely detects human pulse signals. The fabricated DPS achieved remarkable sensitivity (0.03044 nA Pa-1) and a high coefficient of determination (similar to 99.7%), maintaining stable performance over 20 000 cycles. Reliable pulse waveforms can be detected at the carotid, radial, and brachial arteries, and validation against a commercial pulse sensor confirmed its accuracy of 97%. The DPS exhibits excellent sensitivity, durability, and validated applicability in human pulse monitoring, underscoring its potential for integration into next-generation wearable healthcare and self-powered physiological monitoring systems.
Minor changes in process variables, such as temperature, processing speed, and cooling rate, can significantly impact the properties of the final product in a sheet extrusion process. As a result, many optimization efforts focus on each of these variables. This study explored a machine learning-assisted process design for a polypropylene/carbon black composite sheet. The extrusion process parameters were selected as input variables, while tensile strength, void content, width, and thickness were measured, resulting in a dataset of 180 entries. A deep learning neural network was employed to identify and propose optimal combinations of process parameters and validate these proposals by comparing predicted values to experimental data. The polymer melt index had a significant effect on tensile strength, which is attributed to the degree of crystallinity. The process optimization led to a 20% increase in the tensile strength of continuous fiber composites, enhancing the matrix's toughness and improving interfacial load-carrying capacity.
