Lignin, a renewable aromatic biopolymer abundantly found in nature, has attracted increasing attention as a sustainable flame retardant additive owing to its high carbon content, intrinsic thermal stability, and strong charring ability. This review provides a comprehensive overview of the structural and thermal characteristics of lignin and highlights key chemical modification strategies—including phosphorus-, nitrogen-, and siliconbased functionalization as well as synergistic multi-element modification-to enhance its flame retardant performance. Representative flame retardant evaluation techniques, such as thermogravimetric analysis (TGA), limiting oxygen index (LOI), UL-94 vertical burning tests, and cone calorimetry, are introduced with emphasis on their relevance to condensed-phase and gas-phase flame retardant mechanisms. The incorporation of ligninbased flame retardant additives into thermoplastic and thermosetting polymer composites is discussed in terms of flammability suppression, mechanical performance retention, and interfacial compatibility. Key challenges, including dispersion stability, processability at high additive loadings, and compatibility with polymer matrices, are critically addressed. Finally, recent advances in lignin-derived flame retardant systems are analyzed, and future perspectives toward the development of commercially viable, eco-friendly flame retardant additives are proposed. This review aims to support material innovation in flame-retardant polymer composites through the strategic valorization of lignin as a multifunctional and sustainable bio-based additive.

Polypropylene fibers are difficult to dye because of their extreme hydrophobicity and high crystallinity. Therefore, we have developed and reported superhydrophobic dyes which have high affinity towards polypropylene fibers. In the previous studies, dyeability (K/S) was analyzed using a single molecular descriptor (logP) which effectively represented the hydrophobicity of the dyes. However, as more and more dyes were synthesized and their dyeing results obtained, the dyeability could no longer be explained by the single molecular descriptor alone. In order to numerically analyze the relationship between the dye structures and dyeability on polypropylene fibers, a machine learning approach based on multiple molecular descriptors was applied. Linear regression and random forest models were used, and model interpretation was performed using weight analysis and SHAP for explainable artificial intelligence (XAI). Both models achieved high redictive performance, indicating that the relationships between molecular descriptors and dyeability were successfully captured. Model interpretation revealed that logP, degree of alkyl substitution, and hydrogen-bond donors were consistently identified as the key molecular descriptors affecting dyeability. These results demonstrate that dyeability is determined by the combined effects of multiple molecular characteristics, providing a quantitative basis for the rational design of dyes for polypropylene fibers.

In this study, cationic dyeable polyester (CDP) was synthesized using recycled terephthalic acid (r-TPA) recovered from waste PET, and its properties were comparatively analyzed with those of CDP synthesized from virgin terephthalic acid (v-TPA) in terms of chemical structure, molecular weight, thermal behavior, and dyeing performance. The results confirmed that r-TPA possesses an identical chemical structure and high purity to v-TPA, as verified by ¹H-NMR and FT-IR analyses. In addition, both v-CDP and r-CDP synthesized with varying contents of sodium dimethyl 5-sulfoisophthalate (DMS) exhibited comparable molecular weight levels and molecular weight distribution characteristics, including Mn, Mw, and PDI, indicating negligible differences in polymerization behavior depending on the raw material source. Differential scanning calorimetry (DSC) analysis revealed that increasing DMS content commonly led to decreases in the glass transition temperature, melting temperature, and degree of crystallinity for both v-CDP and r-CDP. Furthermore, dyeing performance evaluations demonstrated that both CDPs showed enhanced dyeability and washing fastness with increasing DMS content. Overall, v-CDP and r-CDP exhibited equivalent chemical, molecular, thermal, and dyeing properties under identical synthesis conditions, demonstrating that r-TPA has commercial competitiveness comparable to that of virgin raw materials for CDP synthesis.

Thermal comfort is a crucial factor in human well-being, and clothing serves as the primary medium regulating heat exchange between the body and its surrounding environment. Traditional evaluations of clothing insulation, often expressed in Clo units, rely on thermal manikins. However, commercial thermal manikins are prohibitively expensive, limiting their accessibility for widespread research and industrial applications. This study proposes the development of a low-cost human manikin by utilizing Arduino-based control systems, 3D printing (PLA filament), ceramic heaters, fans for forced convection, and temperature–humidity sensors. The manikin shell was fabricated using FDM 3D printing technology, reflecting average human body dimensions, and the internal heating module was controlled through PID algorithms to ensure stable temperature regulation. Experimental evaluations focused on (i) temperature differences across the manikin shell to analyze thermal resistance, (ii) the impact of external environmental factors such as ambient temperature and airflow, and (iii) the relationship between power consumption and heat loss. Results indicated that the prototype manikin effectively measured thermal insulation and produced outcomes comparable to values reported in previous literature for major body regions (torso, arms, legs, and face). Some discrepancies were observed at extremities (hands and feet), where PLA’s relatively low thermal conductivity resulted in slower heat transfer compared to metal- or liquid-based manikins. Nevertheless, the system demonstrated stable performance, low energy consumption per test, and sufficient accuracy for basic clothing thermal insulation assessments. The findings confirm the feasibility of an economical and practical alternative to commercial thermal manikins, with potential applications in clothing performance evaluation, heat exchange studies, sports science, military equipment development, and educational settings.

Electrospun nanofibers are promising materials for biomedical and cosmetic applications owing to their high porosity, large surface area, and effective moisture control. In this study, polyvinyl alcohol (PVA) nanofibers loaded with Sophora Japonica L. extracts were fabricated by electrospinning, and their structural, antioxidant, and antibacterial properties were evaluated. Extracts from Sophorae flos and Sophorae fructus were prepared using water and 60% ethanol, and their antioxidant activities were assessed by total polyphenol and flavonoid contents as well as DPPH and ABTS radical scavenging assays. The antioxidant activity varied depending on the plant part and extraction solvent, with Sophorae flos ethanol extracts showing the highest activity. PVA nanofibers containing different extract concentrations were successfully fabricated, and uniform fiber morphology was obtained at optimal loading levels. FT-IR and DSC analyses revealed physical interactions and hydrogen bonding between PVA and the extracts, accompanied by reduced crystallinity at higher extract contents. The antioxidant activity of the extracts was retained after incorporation into the nanofibers, with ABTS radical scavenging activity approaching complete scavenging over time. In addition, the extract-loaded PVA nanofibers exhibited antibacterial activity against Escherichia coli and Staphylococcus aureus. These results suggest that Sophora Japonica L. extract-loaded PVA nanofibers have strong potential as biofunctional materials for wound dressings and skincare applications.

In this research, a PTC/GNP mixture was manufactured by mixing GNP with various mass fractions (0.1–0.5 wt.%), and then PTC/GNP was coated on a meta-aramid plain fabric mixed with a conductive yarn to manufacture a planar heating element for a vehicle heating device. After analyzing the thermal and electrical characteristics, the optimal manufacturing process conditions were derived. A heat transfer simulation study on a human dummy model in a virtual space was also conducted in parallel, and the study was conducted to derive predictions for the thermal environment of the human body without actually performing complex tests, thereby reducing development costs and time. The results of the analysis of the characteristics of PTC/GNP mixtures according to GNP content are as follows. As the GNP content increased, the room-temperature electrical resistance decreased, but at 0.1 wt.%, the PTC ratio increased significantly at 150°C due to excessive network collapse between GNPs. When the GNP content was 0.3 wt.%, the balance between room-temperature and high-temperature resistances was the best, and it was judged to be the optimal composition for stably implementing self-temperature control characteristics. As a result of heat transfer analysis in a virtual vehicle environment, the heat flux felt by the human body dummy was the highest at 126.5 W/m² in a GNP composition of 0.3 wt.%, and it is expected to be effective in improving actual vehicle heating efficiency. In the future, we plan to conduct additional research on the comfort and stability felt by human dummy models in virtual vehicle spaces.

In this study, the fracture behavior of molybdenum metal mesh fabrics was systematically investigated by measuring the tensile strength of molybdenum metal yarns with different diameters and applying the obtained properties to a three-dimensional meso-scale model. Tensile tests were conducted on molybdenum metal yarns with diameters to determine yarn-level mechanical properties, which were subsequently incorporated into the three-dimensional meso-scale structural model to numerically predict the mechanical response and fracture behavior of the metal mesh fabric. In addition, fracture tests were performed on actual molybdenum metal mesh fabrics, and the experimentally measured bursting strength was compared with the modeling results to validate the accuracy and predictive reliability of the proposed three-dimensional meso-scale model. The results of this study are expected to provide fundamental insights for the structural design and advanced fracture prediction of warp-knit metal mesh fabrics.