Fashion and Textiles最新文献

A basic study was conducted to manufacture conductive composite threads for use in textile-type electrodes and circuits for smartwear that can be sewn, embroidered, and knitted. The two-types of poly(ethylene terephthalate(PET) filament with a circular cross-section(SD), and an oval cross-section and curved surface(DT), and the conductive yarn was selected as a silver-coated polyamide(AP). The samples were produced two-ply PET yarn as the first twist, then using AP as the second twist, and both processes were applied with 100, 200, 500, and 1,000 TPM. As the number of twists increased, the packing density of samples increased, and the bulkiness of DT was decreased. In addition, the elongation at break tended to increase as the number of twists increased, especially for samples manufactured with DT with a curved surface appeared larger. The linear resistance results increased as the number of second twists in which the conductive yarn was twisted increased. It was confirmed that the area of AP exposed to the outside was more prominent in SD-AP, resulting in decreased linear resistance. Based on the results, samples that have the excellent mechanical and electrical performance were selected to manufacture textile-type electrodes that embedded leg sleeves to measure the surface electromyography(sEMG) signal collection performance. The signal-to-noise ratios(SNR) of textile-type electrode manufactured by SD - AP and DT - AP were 4.54 and 14.96, respectively, confirming the possibility of collecting EMG signals. Thus, this study is expected to be applicable to a wider range of fields than smartwear.

In recent years, significant advancements have been made in smart and multifunctional materials through the integration of 4D printing and shape memory polymers (SMPs). This article highlights key SMP fabrication technologies for 4D printing, focusing on the functionality of stimuli-responsive polymers. Bio-based thermoplastic polyurethanes are produced through the prepolymer polymerization method, with 100% bio-based polyester polyols, polypropylene succinate, and 1,3-propanediol by corn sugar. The resulting SMTPU, which contains bio-polyol in the soft segment, along with a chain extender and isocyanate (4,4-methylene diphenyl diisocyanate, MDI), demonstrates excellent shape recoverability even after significant deformation. Atomic force microscopy, Fourier transform infrared spectroscopy, and X-ray diffraction were employed to analyze hydrogen bonding, microphase separation, and crystallinity, providing insights into the interactions between hard segments (HSs) and soft segments (SSs), an extent of phase separation, and a proportion of hydrogen-bonded urethane groups. The tensile strength of 15–21 MPa, elongation between 534 and 585%, and a hardness of 82–85 Shore A were shown. This study further explores the sustainability and unique properties of SMTPU, making it well-suited for shape memory applications at different temperatures with varying hard segment content. The findings are expected to contribute to future innovations and advancements in the field of 4D printing.

Graphical Abstract

The purpose of this study was to investigate the thermal insulation of military boots using a thermal foot manikin, and to examine environmental and technical factors to determine their thermal insulation. Three Korean and four US military boots were chosen along with four occupational boots. All measurements were conducted at air temperatures of 21, 0, and − 5 °C with an airflow of < 0.15 m s⁻¹. The following two calculations of thermal insulation were compared: (1) a method using values obtained from all nine zones of the foot manikin [entire-zone method], and (2) a method using values from only zones covered by the boot [partial-zone method]. The results showed that the total insulation (I_T) of the 11 protective boots ranged from 0.9 to 1.2 clo per boot and no differences in thermal insulation across the three air temperatures were found. Thermal insulation had a significant and strong relationship with the thickness of the boot layer (r = 0.905, P < 0.001), whereas there was no relationship between the thermal insulation and the boot mass. The thermal insulation of military boots was 81% explained by the thickness of the boot layer alone, and 94% by both the thickness of the boot layer and boot mass (P < 0.001). Thermal insulation based on the partial-zone method was 26% greater than the values using the entire zone method. We proposed design strategies for military boots according to zonal power consumption.

Microwave-convection (MWC) drying was compared with hot air-convection (HAC) drying to elucidate the differences in the drying characteristics of fabrics based on the moisture type and drying performance. For a single fabric, free water removal was more effective with MWC drying, whereas HAC drying was better suited for bound water removal. For stacked fabrics, the moisture leveling effect of MWC drying enabled a uniform drying of the fabric, evaporating both free water and bound water. The dimensional change of MWC was lower than that of HAC, and the toughness and fiber morphology of the fabrics dried using MWC were comparable to those of untreated fabrics. Furthermore, MWC drying exhibited a higher energy efficiency with increasing fabric loads. The lower drying performance of HAC drying was attributed to the convection of hot air and the limitation of heat conduction. The findings of this study are expected to help develop automatic drying algorithms for household dryers.

In the post-pandemic era, integrating e-commerce and deep learning technologies is critical for the fashion industry. Automatic classification of women’s pants presents challenges due to diverse styles and complex backgrounds. This study introduces an optimized Swin Transformer model enhanced by the Global Attention Mechanism (GAM) to improve classification accuracy and robustness. A novel dataset, FEMPANTS, was constructed, containing images of five main trouser styles. Data preprocessing and augmentation were applied to enhance the model's generalization. Experimental results demonstrate that the improved model achieves a classification accuracy of 99.12% and reduces classification loss by 34.6%. GAM enhances the model's ability to capture global and local features, ensuring superior performance in complex scenarios. The research results not only promote the automation process in the fashion industry but also provide references for other complex image classification problems. This study highlights advancements in fashion e-commerce, offering practical applications for inventory management, trend analysis, and personalized recommendations, while paving the way for future innovations in deep learning-based image recognition.

Fashion plays a critical role in self-expression and confidence-building, but adolescents with adolescent idiopathic scoliosis (AIS) have limited fashion choices when they wear a spinal brace that is bulky and rigid underneath their clothing. Spinal braces also have a significant impact on their self-and body-images, social life, and overall quality of life. This study proposes a morphing torso mannequin that accurately measures the interface pressure between the brace and skin, and evaluates the effectiveness of scoliosis braces, thus eliminating the need for ongoing X-rays and ensuring patient safety. The constructed mannequin replicates the AIS torso by using a validated finite element model and 3D-printing and molding techniques. A comparison of the in-brace effects on the morphing mannequin shows significant improvements in spinal alignment and reductions in Cobb’s angles (5.2° and 2.2°), which is consistent with clinical X-ray images. The large correlation coefficient (0.95) between the mannequin and clinical results confirms its ability to simulate the corrective effects of spinal bracing accurately. The proposed morphing mannequin provides valuable information for applying appropriate pressure to halt the progression of spinal curvature while maximizing the wear comfort for AIS patients. Additionally, the mannequin allows for the assessment of clothing fit, thus simulating the effects of wearing a brace and enabling fashion designers to create garments that accommodate the unique physical profile of AIS patients.

This study aimed to analyze those factors affecting the adhesion between substrate fabrics and 3D printing filaments by evaluating the influence of fabric surface characteristics, fiber composition, yarn type, and the fabric’s structure. The results showed that among nylon, PET, and cotton, nylon demonstrated the highest peel strength; however, overall, surface characteristics had a more significant impact on adhesion than fiber composition, and in terms of yarn type, fabrics made from staple fibers had higher peel strength than those made from filament yarns, which is probably due to the rougher surface of staple yarns enhancing adhesion. Regarding fabric structure, among woven, knitted, and nonwoven fabrics, nonwoven fabrics demonstrated the highest peel strength, confirming that the structure and density of the fabric play a crucial role in adhesion. It was also found conclusively that fabric surface roughness and porosity affect peel strength significantly, with rougher surfaces and higher porosity leading to increased adhesion. This is because rougher surfaces provide more area for adhesion, and higher porosity allows the molten 3D filament to penetrate more easily between the fibers, thereby improving adhesion. This study provides practical guidelines for improving the adhesion characteristics of 3D-printed composite fabrics.

Textile electrode is capable of measuring the myoelectric potentials of skeletal muscles such as electromyography (EMG), owing to their outstanding low weight, flexibility, breathability, and comfort properties. Nonetheless, textile surfaces often exhibit intermittent adhesion between the electrode surface and the skin, which can result in fluctuations in electrical resistivity due to the inherent characteristics of textiles. This study aimed to suggest the solutions to improve adhesive of textile electrode for the improvement of electrode performance with high quality signal by minimizing these intermittent contacts. For this, an adhesive intermediate, two different conductive materials, between the skin and the textile electrode was introduced to improve the instability skin contact, respectively. To assess the impact of various adhesive intermediates on knitted electrodes, two different types of adhesive intermediates were utilized: a conductive hydrogel-based adhesive intermediate and a conductive paste-based adhesive intermediate. Moreover, the durability of knitted electrodes with adhesive intermediate was evaluated by assessing the changes of signal quality during drying time for 180 min. As a results of sEMG measurement, it was confirmed that the sEMG signal was stably detecting by applying the adhesive intermediate. Both types of adhesive intermediate significantly increased the signal acquisition performance of knitted electrodes by more than threefold. After five washing cycle, the knitted electrodes with two types of adhesive intermediate maintained approximately 80% of their initial SNR values. Therefore, the use of the adhesive intermediate presented in this study not only improves the performance of the electrode but also ensures reusability.

The surface area of the working electrode plays a crucial role in determining the sensor’s performance, especially in enzymatic sensors. Increasing the surface area of the working electrode has a significant impact on the sensor’s functionality. This research focused on developing textile-based sensors using a multi-layer concept, employing the direct coating method. Two different sensors which are multilayer textile-based sensor (MTBS) and single-layer textile-based sensor (STBS) were prepared, while commercial screen-printed carbon electrode (SPCE) was also used as a comparison. The measurements were carried out using potassium ferricyanide solutions with concentrations of 0.01 M, 0.02 M, 0.03 M, 0.04 M, and 0.05 M at a voltage of 1 V, with a maximum duration up to the end of the measurement and a time interval of 0.5 s. According to the research findings, the fluid spreading speed of the SPCE is the lowest when compared to the spreading speeds of the MTBS and STBS. Specifically, the fluid spreading speed of the SPCE is 4.3 times slower than that of the STBS and 51 times slower than that of the MTBS. Utilizing a multi-layer concept with specific coatings can lead to better-performing sensors in terms of stability and sensitivity. The MTBS exhibits the greatest sensitivity, as indicated by its linear equation slope of 717.230 µA µM⁻¹ cm⁻².

This study investigated the antimicrobial efficacies of fabrics (100% cotton and 100% silk) dyed with an ethanol extract of Gardenia jasminoides (G. jasminoides). More specifically, these fabrics were dyed using a G. jasminoides extract with a dye bath ratio of 1:20 at 40–60 °C for 60 min, followed by post-mordanting. The concentrations of the aluminum sulfate, copper sulfate, and ferrous sulfate mordants were each set to 3% (o.w.f.). The samples were mordanted using a mordant bath ratio of 1:30 at 40 °C for 20 min. The iron mordant slightly increased the dye uptake (K/S) of the cotton fabric but did not increase the dye uptake (K/S) of the silk fabric. The antimicrobial efficacies of the dyed fabrics against methicillin-resistant Staphylococcus aureus (MRSA) ATCC 33591 were determined to be 99.8 and 87.8% for the cotton and silk fabrics, respectively. The inhibitory effects of the cotton and silk fabrics against MRSA were 30.5 × and 167.3 × the inoculum size, respectively, indicating the superior inhibitory effect of the dyed cotton fabric. These results suggest that the fabrics dyed with G. jasminoides extract may possess antibacterial activity against antimicrobial-resistant bacteria.