Fibers and Polymers最新文献

The study aims to analyze the mechanical, thermal, fatigue, water absorption, flammability and creep properties of aluminized glass, kenaf fiber, and silane-treated granite dust particle reinforced polyester composites. Silane treatment was conducted to enhance interfacial bonding and dispersion of granite dust particles. Composites were fabricated using anhydride polyester resin, curing agents, and reinforcements. Laminates were prepared via sandwich construction with alternating layers of fibers. Results revealed that the specimen G2, containing optimized filler content, demonstrated superior mechanical properties compared to G0 and G1. Specifically, G2 showed a tensile strength of 147 MPa, flexural strength of 201 MPa, ILSS of 30 MPa, compression strength of 161 MPa, Izod impact of 7.1 J, specific wear rate of 0.11 mm³/Nm, thermal conductivity of 0.19 W/mK, and flammability (propagation speed) of 7.32 mm/min. It also exhibited minimal water absorption of 0.011%, fatigue strengths ranging from 33,251 to 27,411 at different percentages of UTS, and creep values ranging from 0.0028 to 0.0018 at 5000 s to 15000 s. These results highlight G2’s enhanced load-bearing capacity, impact resistance, wear resistance, and thermal insulation. Although G3 displayed better values for wear resistance, flammability, water absorption, thermal conductivity, and creep, the excessive filler content did not improve mechanical properties. SEM analysis was conducted for investigating the microstructural changes in the specimens. G2’s optimized balance between filler content and mechanical performance suggests its potential for various engineering applications, offering improved strength, durability, and thermal stability. This makes the composite material to be tougher enough and could replace existing metallic materials in aerospace, automobile, space science, defence weapon manufacturing, household domestic appliances, and various biomedical applications, etc.

Global car manufacturers have been actively exploring strategies to incorporate eco-friendly fiber materials into automotive interiors, with a particular focus on replacing traditional PET (polyethylene terephthalate) fabrics with recycled PET. Light fastness stands out as one of the most crucial features for fabrics utilized in automobile interiors, especially when exposed to sunlight. This is because the interior temperature of a vehicle subjected to sunlight can escalate rapidly, accelerating dye fading due to degradation caused by UV (ultraviolet) radiation. In this study, we undertook the dyeing of recycled PET by adjusting the concentration of anthraquinone-based dispersion dye, renowned for its exceptional light fastness, alongside varying the concentration of a light fastness enhancer. Subsequently, leveraging the dyeing data obtained from the fabric, we developed an Artificial Neural Network (ANN), a machine learning model. This model facilitates the computation of dyeing properties both before and after light fastness tests, enabling the prediction of color differences and light fastness ratings. The generated model underwent training and optimization, achieving R² values exceeding 0.98 for all dependent variables. To verify the model’s accuracy, computations were conducted on data not included in the dataset. The outcomes indicated that the model could predict dyeing qualities with an average absolute error of approximately 6% compared to the actual values.

The use of natural products and clean methods for the production of nanoparticles and their application in the manufacture of multi-purpose textiles can significantly reduce the harmful environmental effects of hazardous materials. In response to environmental warnings, a clean synthesis of palladium nanoparticles (PdNPs) was carried out using materials extracted from saffron Stamen (SS) as a plant waste to produce antibacterial wool yarn. For this purpose, natural dyes were extracted from SS using an ultrasound technique to synthesize PdNPs in different ratios of SS extract solution (SSE) and palladium acetate (Pd) solution. PdNPs were identified by Fourier-transform infrared spectroscopy (FT-IR), UV–visible spectroscopy, and dynamic light scattering (DLS). The results showed that the diameter of PdNPs in the SSE: Pd ratio of 1:2 was 23.9 nm. In addition, dyed and antibacterial wool fibers were made with different proportions of Pd and SSE solutions. Field emission scanning electron microscopy (FESEM)–energy-dispersive spectroscopy (EDS), X-ray diffraction (XRD), and FT-IR data showed that PdNPs with an average particle size of 22.5 nm were deposited on the wool fibers. Also, XRD data confirms the deposition of palladium particles on the thread surface. Calorimetric data show a dyeing and color change from yellow to dark brown with excellent dye absorption on the fiber (K/S = 6.72). The dyed samples had moderate to good washing ratings (3–4) and excellent light fastness (6–7). Also, the presence of PdNPs significantly reduced the growth of bacteria on dyed wool fibers. Therefore, the percentage of bacteria reduction for SSE-dyed samples was 38%, which increased to more than 99% with the addition of Pd at an SSE:Pd ratio of 1:2. The results of this research showed that SSE can be used as an efficient, sustainable, and green candidate for the conventional chemical synthesis of PdNPs to produce colored and antibacterial wool yarns.

In this study, the molecules in wool fabric were modified to convert their disulfide bonds into sulfhydryl groups (–SH) by using tris (2-carboxyethyl) phosphine hydrochloride (TCEP) as a reducing agent. The XPS, SEM, and UV–visible absorption spectrum were utilized to track the modification process. MXene and UV-curable PUA resin were blended and printed on the modified wool substrates by screen printing way, followed by a sustainable UV irradiation to effectively fix the printed coating on them. The effects of TCEP on the wettability, electrical resistance of conductive coatings were investigated. Especially, the durability of the conductive coatings to mechanical operations, wet and temperature treatment were systematically evaluated by measuring the resistance change rate on unmodified and TCEP modified wool fabrics, respectively. The results indicated that TCEP modification of wool fabrics could significantly improve the durability and stability of conductive coatings. This was because the active –SH groups in modified wool could participate in the polymerization of PUA molecules into a film under the excitation of UV photo-initiators, greatly improving the integrity and toughness of its structure and ultimately resulting in better adhesion between conductive coatings and wool fabrics.

This study introduces "Woolitmus" a textile-based wearable sweat pH indicator developed using pyranine, also known as HPTS (8-Hydroxypyrene-1,3,6-trisulfonic acid) and naturally occurring wool as substrate. Sweat pH analysis is crucial for monitoring health conditions associated with pH imbalance. The sensor exhibits pH responsiveness under visible and UV light, offering the potential as a real-time sweat patch for pH monitoring. The interaction of pyranine with wool substrate is detailed, elucidating the mechanism behind the pH sensitivity backed up by photophysical characterizations. Stability and reversibility tests also confirm the sensor's robustness and performance. The reported sensor also can simultaneously collect and detect pH levels without the support of any additional accessories like electrodes, display, etc. It also offers sensitivity, real-time response, and non-invasive detection. But more importantly, it stands out for its biodegradability, reusability, zero e-waste, and biocompatibility of the substrate. The wool fabric-based pH sensor holds promising applications, including health monitoring and lifestyle management.

The dissolubility of electrospun chitosan nanofibers in aqueous environments is a matter of concern for the long-term water treatment application. In this study, chitosan was ionically crosslinked with a highly chemical stable polymer, sulfonated polyphenylsulfone (sPPSU), with aim of developing water stable electrospun nanofibers adsorbent for removal of an anionic dye, Congo red (CR). The morphology was characterized by electron microscopies, which showed high longitudinal uniformity nanofiber and fibrous orientation with no characteristic flaws on the surface of nanofibers. Additional studies for detecting changes in the surface wettability of the electrospun chitosan fibers by contact angle were performed, while TGA and DSC were used for determining the thermal stability and crosslinking phenomenon, respectively. To demonstrate the efficiency of the adsorbents, the dye removal rate is investigated as a function of pH, adsorbent dosage, and dye concentration. The optimal experimental conditions for achieving the best adsorptive behavior were 150 min for optimal time, 5 mg adsorbent dose, 10 ml dye solution volume, and 180 rpm shaker speed. The crosslinked chitosan nanofibers were regenerated over adsorption–desorption cycles to validate the favorable reusability. The obtained results revealed that the sPPSU crosslinked chitosan had excellent water-stability, and maximum anionic dye adsorption capacity (531.56 mg/g) according to the Langmuir model, and 371 mg/g was the actual adsorption ability. Additionally, the developed nanofibers showed an excellent reusability, exhibiting removal efficiency (~ 70%) after three consecutive adsorption–desorption cycles.

Poly(vinylidene fluoride) (PVDF) is widely used as a membrane material for applications such as filtration and water treatment due to its unique properties such as high mechanical strength, chemical resistance, ease of electrospinning, thermal stability, and high hydrophobicity. Using embedded organic and/or inorganic fillers, excellent separation efficiency and antifouling performance can be readily achieved. Among these fillers, metal–organic frameworks (MOFs) materials have drawn a lot of attention from researchers due to their variety and unique structures. These properties are further amplified when MOFs are at nanoscale. The synthesis of particles at this scale is challenging and only limited research has been published. With the help of electrospinning, nano-scaled ZIF-67 particles were grown on the surface of the PVDF fibers. In this work, the influence of solvents and ligand concentration on the morphology of the particles formed were evaluated. The morphology of the fibers was analyzed through SEM and the structural characterization was confirmed by XRD and FTIR, while the mass concentration of ZIF-67 was estimated by TGA. These materials are excellent candidates for applications such as textiles, filtration, and gas separation.

To address the low recognition accuracy and poor real-time performance of models in online fabric defect detection tasks, an efficient and compact fabric defect detection method, YOLOv7-tinier, is introduced in this paper. YOLOv7-tinier makes several key improvements to the YOLOv7-tiny model. First, it uses partial convolution to reconstruct the feature extraction module ELAN in the backbone network, reducing the number of parameters and extracting more diverse and hierarchical features and thus improving the detection accuracy and speed. Secondly, a new module called Dilated Spatial Pyramid Pooling Fast Cross Stage Partial Concat is proposed to replace the original Spatial Pyramid Pooling Cross Stage Partial Concat, further reducing the number of parameters and improving the computational efficiency. Finally, it introduces a convolution structure with attention mechanism SConv(Self-attentional convolution) to replace the ordinary convolution of the Neck part, and SBL and ELAN-S modules are constructed, which substantially enhances the network’s detection accuracy without significantly increasing the number of parameters. Extensive comparison and ablation experiments were conducted on the real fabric defect dataset. The experimental results show that under identical conditions, YOLOv7-tinier, our proposed model, achieved a 9.55% improvement in mean Average Precision (mAP) and a 10.81% reduction in parameters compared to the baseline YOLOv7 model, while maintaining a Frames Per Second (FPS) rate of 155.27 Hz. This model can meet both the accuracy and real-time requirements of fabric defect detection in textile manufacturing environments.

Hemp (Cannabis sativa L.) is an annual plant belonging to the Moraceae family. It is grown for its long and stronger fiber. Fabrics made from hemp fibers have obvious advantages over synthetic textiles. An effective attempt has been made to see the effect of scouring and bleaching on the physico-mechanical properties of the hemp fabric. In this study, sodium hydroxide was used for scouring and hydrogen peroxide was used for bleaching. After the scouring, it was found that the tensile strength of the fabric decreased, but elongation increased due to the removal of impurities and non-cellulosic components. Removal of non-cellulosic impurities enhanced the softness, compactness, and crease recovery angle of the fabric. After scouring, there was also a noticeable increase in the whiteness index. On the other hand, there was a significant increase in the whiteness index after bleaching, but the tensile strength and thickness decreased due to the acidic nature of hydrogen peroxide, which weakened and deteriorated the surface of the fabric, as confirmed by the result of Scanning electron microscopy. Furthermore, it was found that bleaching with a 5% concentration of H₂O₂ has an unfavorable impact on the physico-mechanical properties of the hemp fabric, whereas scouring with a 2% concentration of NaOH improves most of the physico-mechanical properties of the hemp fabric.

In this study, multiple-network hydrogels of polyacrylamide/gelatin/alginate were synthesized by photopolymerization method. In the study, the effect of the concentration of N,Nʹ-Methylenebisacrylamide, as a crosslinking agent, on hydrogels for use as wound dressings was investigated. For improving antibacterial, and wound-healing properties, salicylic acid was incorporated into the hydrogels. Results of this study showed that the novel multiple-network hydrogels exhibited adjustable mechanical properties, and desirable swelling capacities, making them good choices for wound dressing applications. Furthermore, the optimized formulation of the novel hydrogel, prepared with 0.025 gr of the crosslinking agent, revealed prominent characteristics including 2.93 ± 0.13 µm average pore size, 168.514% swelling percentage, 165 kPa tensile strength, 208 kPa Young’s modulus, and prolonged salicylic acid release. Finally, an antibacterial assay and cell scratch test of the optimized hydrogel showed a significant reduction in bacterial growth and complete closure of the wound within 24 h.