The global push for sustainable materials has intensified the research on natural fiber-reinforced composites. This study investigates the potential of sugarcane bagasse fibers, combined with a bio-based epoxy matrix, as a sustainable alternative for high-performance composites. A comprehensive approach was adopted, including wear testing, thermal and structural characterization, and machine learning predictive modeling. Ethylene dichloride-treated fibers exhibited the lowest wear rate (0.245 mg/m) and the highest thermal stability (T20% = 260 °C, char yield = 1.3 mg), highlighting the role of optimized surface modifications. XRD (X-ray diffraction) analysis revealed that pre-treated fibers achieved the highest crystallinity index of 62%, underscoring the importance of structural alignment in fiber-matrix bonding. Machine learning insights using a Random Forest model identified fiber treatment as the most significant parameter influencing wear performance, with accurate predictions validated through experimental results. This work demonstrates the transformative potential of sugarcane bagasse fibers in sustainable polymer composites, offering a pathway for environmentally friendly, lightweight, and durable material solutions. These findings integrate experimental rigor with computational insights, paving the way for advancements in natural fiber-based composite technologies.

The Solar Air Heater (SAH) is considered to be one of the promising devices for the utilization of solar radiation. Extracting more heat to the flowing air is the focus of researchers, and many novel ideas are adopted to improve the efficiency of such collectors. The objective of the present work is the enhancement of thermal performance using a numerical analysis of a single flow double pass solar air heater with two types of arrangements of aerofoil fin configurations. The effect of the aerofoil fin configurations and the height of the fin are investigated for their thermal and thermohydraulic efficiencies. The height of the fin varied parametrically for the Reynolds number ranging from 3000 to 24,000 by keeping the axial pitch of the fin as a constant. It is found that the thermal efficiency increases with the increase in fin height due to an increased flow turbulence causing more absorption of heat to the working fluid. However, it is seen that the varying height of the fin beyond a certain height has yielded an adverse effect in terms of lesser thermal efficiency due to the expected flow blockage in the main stream. The thermohydraulic efficiency increases as the height of the fin decreases. The Computational Fluid Dynamics (CFD) results revealed that the optimum aerofoil fin configuration yields approximately a 23.24% higher thermal efficiency when compared with that of the single pass solar air heater (base model). The thermohydraulic efficiency exceeds the optimum aerofoil fin configuration compared with the base model by approximately 20.94%.

This paper presents a numerical analysis of the thermal performance improvement in a flat plate solar air heater equipped with chamfered turbulators attached below the absorber plate for evaluating performance for Reynolds numbers ranging from 3,000 to 21,000. According to the research, chamfered turbulators caused the flow to become highly turbulent. This flow behaviour with flow separation around the turbulators positively affects performance. This paper attempts to explain the complex flow behaviour found during the analysis. The turbulator diameter varies in 1 mm increments from 3 to 7 mm at a constant longitudinal pitch of 200 mm. The number of turbulator rows in the transverse direction is kept constant at three. The chamfer is represented by the flow attack angle, which can be 30°, 45°, or 60° facing the direction of flow and opposing the direction of flow. The results showed that a 7mm diameter turbulator with a 30° chamfer angle placed against the flow of air yielded a considerably more significant thermal enhancement factor of 1.15 over the spectrum of flow Reynolds number studied

This study develops a unified multiscale–machine learning framework to interpret and predict thermo-mechanical wear regime transitions in MWCNT- and nanoclay-reinforced bio-based epoxy composites. A physics-informed master wear formulation integrating real contact mechanics, geometry-dependent shear transfer, interfacial adhesion energetics, and fracture-controlled matrix detachment was combined with interpretable machine learning analytics on a unified tribological dataset. In the CNT system, increasing loading from 0.1 to 0.4 wt.% enhanced interfacial adhesion energy density from 0.00813 to 0.01906 J/m², resulting in a monotonic reduction in the wear rate from 0.00918 to 0.00613 mm³/N·m (~33% reduction). In contrast, nanoclay exhibited an optimum behavior, with a minimum wear at 0.25 wt.% (0.000093 mm³/N·m; 7.9% reduction vs. neat clay baseline), followed by deterioration at a higher loading due to dispersion loss. The unified probabilistic regime classification of low-wear conditions (k < 0.007 mm³/N·m) achieved an ROC − AUC = 0.9256 and balanced accuracy = 94.3%, with thermo-mechanical severity identified as the dominant regime-switching driver. Reinforcement identity significantly modulated regime stability, confirming distinct shear transfer (Carbon Nano Tubes(CNT)) and confinement/tribofilm (clay) mechanisms within a common mathematical framework. By enabling the durability-oriented design of bio-based tribological systems and extending component service life through predictive stability mapping, this work contributes to resource-efficient materials engineering and reduced lifecycle waste, supporting Sustainable Development Goals SDG 9 (Industry, Innovation and Infrastructure), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action).

Accurate prediction of wear behavior in polymer nanocomposites remains challenging due to the coupled influence of operating conditions and evolving surface morphology. In this study, a surface-aware triboinformatics framework is proposed to predict the dry sliding wear behavior of multi-walled carbon nanotube (MWCNT) reinforced epoxy composites by integrating operating parameters with run-wise atomic force microscopy (AFM) surface descriptors. Wear experiments were conducted using a Taguchi L16 design by varying CNT content (0–0.75 wt.%), applied load (10–40 N), sliding speed (183–458 rpm), and sliding distance (500–1250 m). AFM-derived parameters, including Ra, Rq, Z-range, and surface area difference, were extracted from the worn surface corresponding to each experimental run. Multiple regression-based machine learning models were evaluated using leave-one-out cross-validation, with ensemble-based models providing the best predictive performance (R² > 0.85 with low RMSE and MAE). Feature importance and partial dependence analyses identified CNT content as the dominant factor controlling wear reduction, followed by Z-range and Ra, highlighting the critical role of surface damage severity. Neat epoxy exhibited a maximum wear loss of 0.444 mg, whereas the 0.75 wt.% CNT composite showed values as low as 0.003 mg under comparable conditions, corresponding to a reduction of approximately 99%. The proposed framework enables mechanistically interpretable wear prediction and supports the design of durable polymer composites, contributing to SDG 9 (Industry, Innovation and Infrastructure) and SDG 12 (Responsible Consumption and Production).

The dry sliding wear behaviour of nanoclay-filled bio-based epoxy composites was systematically investigated using a Taguchi L16 experimental design by varying nanoclay content (0–0.35 wt.%), normal load, sliding speed, and sliding time against an EN24 steel counterface. Wear loss, specific wear rate (SWR), frictional response, thermal rise, and energy-based descriptors were quantified, followed by mathematical and machine-learning (ML) based modelling. The results demonstrate that nanoclay addition significantly improves tribological performance up to an optimal content of 0.25 wt.%, beyond which wear instability increases. Compared with neat epoxy, the 0.25 wt.% nanoclay composite exhibited a reduction in steady-state coefficient of friction from ~0.53 to ~0.42, along with a 25–30% decrease in specific wear rate and the lowest energy-to-wear conversion efficiency, indicating more effective utilization of frictional energy. Taguchi analysis identified normal load as the dominant factor governing wear variation (~68% contribution), followed by sliding speed (~17%), while nanoclay content contributed ~5%. An energy-based wear model showed improved correlation with experimental wear volume (R² ≈ 0.93) compared to a classical Archard-type formulation. ML prediction using a random forest model with leave-one-out cross-validation achieved an R² ≈ 0.64 for SWR. Scanning electron microscopy (SEM) and atomic force microscopy (AFM) analyses confirmed a transition from severe abrasive wear in neat epoxy to stable tribofilm formation at 0.25 wt.% nanoclay, followed by heterogeneous debris-mediated wear at higher filler content. The observed reduction in wear loss and frictional energy dissipation supports sustainable materials innovation aligned with SDG 9 (Industry, Innovation and Infrastructure) and SDG 12 (Responsible Consumption and Production), while improved operational efficiency is consistent with SDG 7 (Affordable and Clean Energy).