The COVID-19 pandemic has highlighted the critical need to improve indoor air quality (IAQ) through efficient air filtration, especially in heating, ventilation, and air-conditioning (HVAC) systems. While dedicated high-performance filters are effective, their high-pressure drops result in significant energy consumption when used in HVAC systems. Herein, we report the application of machine learning (ML) models to predict filtration efficiency and pressure drop, enabling the design and optimisation of filter media in HVAC. Specifically, three ML models, Gaussian process regression (GPR), artificial neural network (ANN), and decision tree (DT), have been trained on a dataset obtained from the literature. The dataset comprised key structural parameters of a wide range of filter media. The GPR model emerged as the most reliable predictor, exhibiting the highest coefficient of determination (R2) and lowest root mean squared error (RMSE) in predicting filtration efficiency and pressure drop, rendering it the most reliable predictor for small and uncertain datasets. The robustness of the GPR model is further confirmed via validation with commercially available filter media. In addition, the ML models accurately capture the established relationship between filtration efficiency and its characteristic drop at the most penetrating particle size (MPPS).

Our research found that the shedding of microplastic fibers (MPFs) from textiles is exacerbated by repeated mechanical recycling, raising environmental concerns as the use of recycled fibers increases in industry. This study examined MPF release from fabrics containing 30% mechanically recycled polyester fibers subjected to one, two, or three recycling cycles, compared to primary (virgin) polyester (PES). Shedding was assessed under both simulated wear and laundering conditions using Martindale, ICI Pilling Box, and ISO 4484–1:2023 (microplastic from textile sources) protocols. Laundering tests showed no clear difference in MPF release between primary PES and once-recycled PES (rPES-1; ∼ 1.4-fold). In contrast, fabrics with fibers recycled twice (rPES-2) and three times (rPES-3) released about 4.3-fold and 6.2-fold more MPFs than PES, respectively. Fiber release was different under dry-state abrasion than in laundry tests, highlighting the limitations of current wet-state focused assessments. Progressive fiber fragmentation and increased yarn hairiness suggest cumulative structural degradation with each recycling cycle. These findings underscore the need for standardized dry-state shedding assessments and improved recycling strategies to mitigate MPF emissions. While mechanical recycling remains environmentally preferable to uncontrolled disposal, these findings reveal a trade-off in the form of increased MPF release after multiple recycling cycles, which could be mitigated through improved recycling processes and fabric design. Achieving a balance between textile circularity and environmental sustainability remains a critical challenge for the industry.

This study reports the development of polylactic acid (PLA)-based biocomposites modified with microwave-acetylated lignin and reinforced with regenerated cellulose fibers, targeting enhanced mechanical and thermal properties. Lignin acetylation was performed using a catalyst-free microwave-assisted method, yielding improved compatibility with the PLA matrix. Composite blends with varying ratios of PLA, lignin, impact modifier, and fiber loading were processed via extrusion, 3D printing, carding, needle punching, and compression molding methods. Mechanical characterization revealed that composites with higher cellulose fiber content and lignin incorporation demonstrated enhanced impact strength and energy dissipation capabilities. Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR) and thermogravimetric analysis (TGA) confirmed the successful modification of lignin and its influence on thermal stability and char residue formation. Scanning electron microscopy (SEM) provided insights into microstructural changes, such as improved interfacial bonding and reduced fiber pull-out with increasing lignin content. These findings underline the potential of lignin-cellulose PLA composites as a sustainable alternative to traditional materials in automotive and other high-performance applications, combining lightweight design with environmental benefits. 

The surface profile and structural alignment of fibers and yarns in fabrics are critical factors affecting the electrical properties of conductive textile surfaces. This study aimed to investigate the impact of fabric surface roughness and the geometrical parameters of woven fabrics on their electrical resistance properties. Surface roughness was assessed using the MicroSpy® Profile profilometer FRT (Fries Research & Technology) Metrology™, while electrical resistance was evaluated using the Van der Pauw method. The findings indicate that rougher fabric surfaces exhibit higher electrical resistance due to surface irregularities and lower yarn compactness. In contrast, smoother fabrics improve conductivity by enhancing surface uniformity and yarn contact. Fabric density, particularly weft density, governs the structural alignment of yarns. A 35% increase in weft density (W19–W27) resulted in a 13–15% reduction in resistance, confirming that denser fabrics facilitate current flow. Higher weft density also increases directional resistance differences, enhancing anisotropic behavior. Angular distribution analysis showed lower resistance and greater anisotropy at perpendicular orientations (0° and 180°, the weft direction; 90° and 270°, the warp direction), while diagonal directions (45°, 135°, 225°, and 315°) exhibited higher resistance. Surface roughness further hindered current flow, whereas increased weft density and surface mass reduced resistance and improved the directional dependencies of the electrical resistances. This analysis was conducted based on research using woven fabrics produced from silver-plated polyamide yarns (Shieldex® 117/17 HCB). These insights support the optimization of these conductive fabrics for smart textiles, wearable sensors, and e-textiles. Fabric variants W19 and W21, with lower resistance variability and better isotropic behavior under the S electrode arrangement, could be proposed as suitable materials for integration into compact sensing systems like heart rate or bio-signal monitors.

The combination of utilisation of fibres from industrial hemp waste and degumming using deep eutectic solvents can contribute to a more sustainable textile industry. This study therefore evaluates the effect of deep eutectic solvent treatment on fibres from Finola hemp grown for food production. A deep eutectic solvent (DES) consisting of choline chloride and urea with molar ratio 1:2 was chosen as well as a microwave-assisted treatment method to replace the traditional oil bath which consumes more energy and time. The results showed that the treatment had a degumming effect on the fibres. It was indicated by a significant smaller diameter. After evaluation, the most efficient treatment combination was concluded to be treatment at 100 W/g with bath ratio 1:20 (weight: volume) and 12 heating cycles consisting of 0.5 min microwave heating with 1 min between. The fibres had a significant reduction in mean length, from 37.8 mm to 29.8mm. The other results were however deemed to outweigh the length reduction.

This study presents numerical and experimental investigations of wool fiber-reinforced polylactide (PLA) biocomposites. In the first stage, a MAT_COMPOSITE_DAMAGE_54/55 (LS-DYNA) material model was developed based on mechanical tests performed on samples with two fiber-to-PLA mass ratios: W40/PLA60 and W60/PLA40. In the second stage, the models were applied in Charpy impact simulations to evaluate impact strength and identify dominant failure mechanisms. The results show that fiber content, orientation, and impact direction strongly influence energy absorption and failure behavior. Samples impacted along the fiber direction exhibited significantly higher strength and stiffness compared to transverse loading. Increasing PLA content enhanced stiffness and load-bearing capacity but led to more brittle failure. The W40/PLA60 composite absorbed the highest energy but failed abruptly, while W60/PLA40 exhibited a more gradual damage progression and distributed deformation of the composite structure. Microscopic analysis confirmed these differences, with fiber pull-out dominating in axial impacts and matrix cracking and delamination in transverse ones. The developed simulation model captures these behaviors and supports the structural design of wool/PLA composites, offering a basis for future predictive modeling of sustainable biocomposites.

As the textile industry faces growing challenges related to sustainability, recycled fiber blending for making new yarns has emerged as a key area for reducing environmental impacts. This study aims to investigate the role of fiber length features in predicting the quality of blended yarns, particularly focusing on natural-based fiber blends such as recycled cotton (ReCo) and Lyocell. Machine learning models, including Random Forest, Gradient Boosting, and Support Vector Regression, alongside linear and polynomial regressions, are used to predict fiber properties based on empirical data. The results show fiber length features from the Staple Diagram and Fibrogram as the most significant factors. Hyperparameter tuning has enhanced model accuracy, especially for Random Forest and Gradient Boosting, showing significant reductions in error metrics. Cross-validation is performed to ensure the reliability of the models and prevent overfitting during the predictive analysis of fiber length features. Shapley Additive Explanations (SHAP) analysis reveals that specific fiber length ranges have the most influence on model predictions, highlighting their importance in optimizing blended yarn properties. These findings contribute to advancing sustainable textile production through data-driven approaches and textile fiber blend optimization.