Waste hair represents an underutilized keratin-rich residue with potential value in sustainable polymer technology. This review examines the composition, mechanical behavior, thermal characteristics, processing requirements, and polymer applications of waste human hair, with emphasis on its use as a filler, reinforcement, modifier, and keratin source. The reviewed literature shows that hair-derived keratin and hair fibers can improve selected mechanical, thermal, sorption, and biocompatibility properties of polymer composites, including polyurethane, epoxy, polyester, polypropylene, polyethylene, and bio-based matrices. Particular attention is given to extraction and pretreatment strategies, interfacial adhesion, biodegradation, biomedical materials, water remediation membranes, and hybrid composites incorporating other natural wastes. Despite promising laboratory-scale results, wider implementation is limited by inconsistent waste collection, contamination concerns, keratin extraction efficiency, matrix compatibility, and scalability. Future progress will depend on greener processing methods, standardized characterization, improved surface functionalization, and pilot-scale validation. Overall, waste hair from sewage and related streams offers a viable route for circular material development, reducing environmental burdens while supporting the design of cost-effective and sustainable polymer composites.
Additive manufacturing enables the production of complex components, but the layered deposition process can result in pronounced surface irregularities, particularly when wood particles are incorporated into the polymer matrix. This study investigates the mechanical performance and surface characteristics of wood–PLA materials and evaluates CNC milling as a post-processing method. Three wood–PLA composites, PLA, and ASA, were examined. Tensile and flexural properties as well as density were determined for the investigated materials. Cutting forces were measured during milling, while surface roughness was evaluated before and after milling for specimens produced in different orientations. The wood–PLA composites exhibited lower mechanical properties than neat PLA, with tensile strength reduced by 24–43% and flexural strength by 36–57%. Internal voids and imperfect fusion between deposited strands were identified as important factors affecting the measured performance. Their initial roughness was higher than that of the neat polymers, indicating a substantial contribution from material heterogeneity. Milling substantially reduced roughness for all wood–PLA composites and required cutting forces up to 48% lower than the neat polymers under the investigated conditions. CNC milling therefore constitutes an effective approach for improving the surface quality of these materials, although machining parameters should be selected with their layered structure in mind.
Fiber sensors encompass optical-fiber platforms and electrically functional fibers, yarns, and textiles that produce spectral, temporal, and spatial data across wearable, biomedical, robotic, infrastructure, and environmental applications. Their responses are shaped by nonlinear transduction, cross-sensitivity, conformal contact, manufacturing variation, drift, and limited calibration coverage. This review examines how machine learning (ML) can be used across the fiber-sensing pipeline, from algorithm-assisted physical design and fabrication-aware optimization to denoising, demodulation, representation learning, multimodal inference, and application-level decision making. Direct demonstrations in optical and electrically functional fiber systems are distinguished from transferable methods developed in adjacent photonic, additive manufacturing, tactile, and soft-sensor platforms. The available evidence is strong for calibration, signal recovery, and task inference within a defined device and operating domain. By contrast, experimental evidence for ML-guided physical co-design, cross-device and cross-batch transfer, long-term stability, and hardware-constrained deployment remains limited. Accordingly, ML is most promising as part of a coupled sensor–model–hardware system that preserves task-relevant information and reports when an input lies outside its validated operating range. This review therefore emphasizes validation boundaries over headline accuracy alone and identifies uncertainty quantification, physics-guided learning, fabrication metadata, and edge-aware co-design as priorities.
Natural lignocellulosic fibers are potential reinforcements for lightweight polymer composites, but their geometrical heterogeneity complicates mechanical characterization and material selection. This study investigated the influence of fiber diameter on the tensile behavior of commercial Typha fibers through integrated geometrical, morphological, chemical, thermal, mechanical, and statistical analyses. The fibers exhibited an apparent geometrical density of approximately 0.71 g/cm3, a moisture-related mass loss of 10.62%, and a main thermal degradation onset at 278.61 °C. Tensile properties varied markedly among diameter classes: mean tensile strength decreased from 438.85 MPa for fibers measuring 0.06–0.16 mm to 37.31 MPa for fibers measuring 0.50–0.80 mm, while Young’s modulus decreased from 7.49 to 1.43 GPa. An empirical power-law model described the relationship between class-averaged tensile strength and diameter (R2 = 0.983). Weibull moduli ranged from 2.71 to 8.30 within individual diameter classes but decreased to 1.25 when the classes were pooled. These findings indicate that combining dimensionally distinct fiber populations can substantially broaden the apparent strength distribution. Although the influence of cross-sectional geometry and intrinsic structural variability could not be separated, the results demonstrate the importance of diameter-aware characterization for reliable mechanical-property reporting and preliminary reinforcement selection. Further composite-level investigations are required to establish the practical implications of these findings.
Bamboo textile fiber (BTF) is a renewable alternative to synthetic textiles in textile-reinforced mortar (TRM), but the effects of yarn architecture and coating on its tensile and bond behavior are not well established. This study tested four BTF-reinforced TRM configurations with a 15 × 15 mm grid: textiles woven from single-ply (SP) and two-ply (DP) yarns, each in dry and epoxy-coated (SPI, DPI) conditions. Twenty direct tensile coupons and twenty textile–mortar bond specimens bonded to concrete substrates were tested under displacement control. The effects of ply configuration, coating and their interaction were evaluated by two-way ANOVA, and the coated yarn surface was examined by scanning electron microscopy. Epoxy coating significantly increased peak strain and toughness (p < 0.001) and peak tensile stress (p = 0.008), with the largest effect for the two-ply textile, for which toughness increased from 0.09 to 0.54 MJ/m3 and peak tensile stress from 16.11 to 28.36 MPa. Ply configuration (p < 0.001) and coating (p = 0.018) both significantly increased the peak textile-normalized bond stress, from 3.52 MPa (SP) to 9.65 MPa (DPI), with no significant interaction; all bond specimens failed by textile rupture, so these values are lower bounds. DPI gave the highest peak strain, toughness and peak bond stress, supporting its selection for structural-level validation.
