Fibers and Polymers最新文献

The application of hydrogen peroxide bleaching catalysts in cotton fabric bleaching can effectively reduce the temperature and time of the bleaching process, thereby reducing energy consumption. Conventional hydrogen peroxide bleaching catalysts such as manganese complex (Mn–Me₃TACN) with the ligand of 1,4,7-trimethyl-1,4,7-triazacyclononane introduced by Unilever can achieve satisfactory whiteness at low bleaching temperatures. However, Mn–Me₃TACN can result in severe chemical damage to fibers and the ligand 1,4,7-trimethyl-1,4,7-triazacyclononane is very expensive, which greatly inhibit the commercial application of the conventional bleaching catalysts. In this work, a novel and cost-effective manganese complex (Mn-DMPP) with the ligand of 1,4-dimethylpiperazine for cotton fabric bleaching is reported. The physicochemical properties of synthesized complex are characterized and the bleaching performance of Mn-DMPP is evaluated in terms of H₂O₂ decomposition and the whiteness index, tensile strength retention, and capillary effect of the bleached fabric. After bleaching at 70 °C for 45 min, the whiteness of the fabric bleached with Mn-DMPP is 76.55, and the tensile strength retention is 87.77%. Under the same testing conditions, the whiteness of the fabric bleached with Mn–Me₃TACN is 76.79, and the tensile strength retention is 82.3%. Compared with conventional catalyst Mn–Me₃TACN, the cotton fabric bleached with the Mn-DMPP at low temperature shows higher tensile strength retention at similar whiteness levels. Therefore, Mn-DMPP is an effective catalyst for the low-temperature bleaching of cotton fabric with hydrogen peroxide. It has competitive advantages in reducing bleaching production costs and protecting fibers from severe chemical damage.

Auxetic materials refer to materials with a negative Poisson’s ratio (NPR). They expand when stretched and become slimmer when compressed. Due to the special features of auxetic materials, they are used in a wide range of industrial applications including medical, fashion and clothing, composite, sports, and protective applications. This study aims to design and develop auxetic weft-knitted fabrics based on a horizontal zigzag structure with different structural patterns and investigate the influence of various parameters on their auxetic behavior. Auxetic fabrics with three patterns and three loop lengths were knitted using polyester yarn with different counts. Subsequently, the auxetic behavior of each fabric sample was examined. The results denote that the negative Poisson’s ratio decreases with increasing strain. The effect of the fabric knit pattern with different angles on the auxetic behavior is different in the course and wale directions. Fabrics with higher loop lengths have a higher initial negative Poisson’s ratio (NPR), while fabrics with smaller loop lengths exhibit auxetic behavior in a wider strain range. Furthermore, optimizing the fabric structure and dimensional parameters such as yarn count and loop length can enhance auxetic behavior. This research provides a useful study for optimizing auxetic fabric properties by changing structural parameters and developing innovative applications for materials with special mechanical properties.

Multifunctional polyester fabric was developed using graphitic carbon nitride (g-C₃N₄) and ZnO semiconductors and its self-cleaning properties were studied by varying the weight ratio of semiconductors and dye concentration. The fabrics were treated using two distinct methods pad-dry-cure and exhaustion. Fabric self-cleaning activity was assessed by monitoring the degradation of methylene blue stains under natural sunlight, quantified through changes in ΔRGB values. The coated samples were also evaluated for air permeability, wettability, flammability, bending length, and antibacterial properties. The study found that polyester fabrics treated with g-C₃N₄ via the pad-dry-cure method exhibited significantly higher self-cleaning activity (ΔRGB = 120.3) than those treated by the exhaustion method (ΔRGB = 68.8). Furthermore, fabrics coated with a ZnO/g-C₃N₄ hybrid demonstrated superior self-cleaning performance to those treated with either g-C₃N₄ or ZnO alone. Antibacterial results indicated the effectiveness of treated samples through the pad-dry-cure method for 100% antibacterial activity. However, samples treated with exhaustion showed higher air permeability, water absorption, and flexibility.

To prepare a new formaldehyde adsorption material with excellent comprehensive performance, high practical value, and easy industrial production, a regenerated cellulose fiber (RCF) non-woven fabric-supported ZIF-67 (ZIF-67@RCF) was prepared by in situ growth of ZIF-67 on the surface of fibers. The microscopic morphology, mechanical properties, specific surface area, and formaldehyde adsorption properties were analyzed, and the results showed that the ZIF-67 can rapidly and extensively grow on the surface of RCF under normal temperature and pressure with uniform distribution. The fiber surface can be completely covered within 25 min with a loading capacity of nearly 2 wt%. The important thing is this method does not damage the mechanical properties of RCF non-woven fabric; on the contrary, it improves the mechanical properties to some extent. In an enclosed testing space, formaldehyde was able to be removed quickly in the presence of ZIF-67@RCF and the removal rate could reach 98% in 10 min. We believe that this material could be used to prepare new air purification filter for formaldehyde removal.

Fabric defect detection is a prevalent issue in the textile industry. For fabrics with monotone coloration and simple patterns, the existing detection algorithms have been able to meet the detection requirements of industrial production. However, there is still a lack of effective detectors to detect fabrics defects with complex patterns and variable colors. This paper proposed an improved RT-DETR model called CPF-DETR, which improves the detection effect by a noise suppression module (NSM) and a novel encoder using dynamic snake convolution (DSC-Encoder). Firstly, RT-DETR as a complete end-to-end real-time detection model was used as our detection framework to avoid the effect of the lack of a priori knowledge of the anchor in industry detection. Secondly, we designed a noise suppression module to filter out noise from complex backgrounds. Furthermore, we introduced the dynamic snake convolution (DSC) into the encoder and designed a hybrid convolution module (HCM) which helps the encoder to enhancing its ability to acquire elongated structure detail information in complex pattern. Finally, we compared our CPF-DETR with many state-of-the-art models on a Complex Patterned Fabric Dataset (CPF) collected from the Aliyun Tianchi fabric defect dataset. The experimental results demonstrate that the accuracy of our detector is superior to existing models. Our detector achieved 69.1% AP outperforming the RT-DETR by 2.3% and yolv8m by 10.6%. The inference latency of 10.46ms is also able to meet the real-time detection requirements.

The field of fabric defect detection has undergone a transformative journey marked by the evolution of object detection models. From traditional approaches to advanced deep learning architectures, these models have addressed crucial challenges in the textile industry. YOLOv7-tiny model stands out as a remarkable solution, demonstrating unprecedented performance in fabric defect detection. Its enhanced architecture addresses key industry issues, including high-resolution images, small defect sizes, and imbalanced datasets. Therefore, the aim of this paper is to incorporate the YOLOv7 model with improvements to detect woven fabric defects in real time. Augmenting the Enhanced Residual Convolutional Network (ERCN) with extra Convolutional, batch normalization and leaky rectified linear unit (CBL) layers enhances hierarchical feature extraction, while the two-concatenation technique adds complexity for richer representations. Reducing CBL layers in Efficient layer aggregation networks-downgrade (ELAN-D) streamlines and optimizes, emphasizing a balanced approach in the YOLOv7-tiny model for targeted objectives. The improved YOLOv7-tiny model excels in achieving a delicate balance between accuracy and efficiency, vital for practical applications in the textile sector. This model’s accuracy, with a mAP of 84% at a 0.50 threshold and 40% at 0.50:0.95 showed exceptional in comparisons to other models. The model also boasts a high accuracy of 98% and operates at a commendable detection speed of 90 fps, meeting real-time demands in fabric production. Recognizing defects as small as 1 mm, the YOLOv7-tiny model emerges as a pivotal tool in automating fabric defect detection and optimizing textile quality management processes.

This study reports the thermal and optical properties of a transparent polyimide (PI) film system incorporating trifluoromethyl (CF₃-), ether (-O-), and sulfone (-SO₂–) functionalities into the polymer backbone by co-polymerizing 4,4'-(hexafluoroisopropylidene) diphthalic anhydride (6FDA) and bis(3-aminophenyl) sulfone (3DDS)-based transparent PI with additional diamine monomers such as bis[4-(4-aminophenoxy)phenyl] sulfone (p-BAPS) and bis[4-(3-aminophenoxy)phenyl] sulfone (m-BAPS). Our findings clearly demonstrate that the introduction of p-BAPS and m-BAPS monomers into the 6FDA/3DDS-based PI system significantly influences the thermal and optical properties of the resulting PI films. Specifically, the glass transition temperature (T_g) of the 6FDA/3DDS/p-BAPS-based PI film increased by approximately 10 °C compared to the pristine 6FDA/3DDS-based PI film, while the yellowness index (YI) decreased from 12.75 to 3.76. On the other hand, the T_g of the 6FDA/3DDS/m-BAPS-based PI film decreased by about 5 °C compared to the pure 6FDA/3DDS-based PI film, but nevertheless, the YI value decreased from 12.75 to − 1.85 and the optical properties were improved compared to the 6FDA/3DDS-based PI film. Based on these observations, it was reasonably concluded that the simultaneous introduction of flexible ether bonds and rigid, linear benzene rings would not only minimize the thermal degradation of the final PI film due to the introduction of comonomers but also improve its thermal and optical properties simultaneously. Thus, we believed that this study will provide valuable insights on the simultaneous enhancement of the thermal and optical properties of PI system through the combination of various monomers, such as introducing flexibility into the polymer backbone and reinforcing the rigid ring structure and substituent configurations.

This study aimed to improve the spinnability of poly(butylene adipate) polyester through the addition of naphthalene ring structures. First, poly(butylene adipate-co-buthylene-2,6-naphthalate) (PBABN) copolyesters of different ratios were produced through a one-pot polymerization of 1,4-butanediol, adipic acid, and 2,6-naphthalene dicarboxylate (NDC). All PBABN copolyesters maintained good thermal stabilities. As the naphthalene content was increased from 0 to 100 mol%, the glass transition temperature (T_g) of the PBABN copolyester increased from − 60 to 90 °C while its melting temperature (T_m) increased from 50 to 240 °C. When 30 mol% BN units were added, the elongation at break of the PBABN copolyester exceeded 2000%, indicating that the addition of a specific ratio of NDC could improve the elongation of the copolyester. Additionally, PBABN-50 material showed a higher Young’s modulus of 79 MPa, a yield strength of 19 MPa, and an elongation at break of 800%, which show its applicability to packaging materials, agricultural films, and fibers. Second, PBABN copolyesters with BN units above 50% were chosen for melt-spinning into fibers and were post-drawn with a ratio of 3.0 to enhance the fiber strength. The maximum stress values of the fibers with 50, 70, 90, and 100 mol% NDC were 1.93 ± 0.08, 2.82 ± 0.06, 3.89 ± 0.05, and 5.93 ± 0.12 g den^–1, respectively.

Graphical Abstract

This study aims to examine the deformation properties and crashworthiness performance of square tubes manufactured from 3D-printed polylactic acid (PLA), which incorporate window features. Four distinct parameters are analyzed, each assessed at three different levels. These parameters include: window end shape (rectangular, round, and triangle), window length (20, 25, and 30 mm), window width (3, 6, and 10 mm), and window number (2, 4, and 6). The structures underwent quasi-static axial compression loading to evaluate their performance under compressive loading. Throughout the testing process, comprehensive data were collected, including the load and energy absorbed during compression and the resulting displacement responses. Additionally, detailed records of the failure histories for each tube were maintained. The assessment of crashworthiness involved the measurement of several critical indicators: the initial peak crash load (({text{F}}_{text{ip}})), the total energy absorbed (U), the mean crash load (({text{F}}_{text{m}})), the specific absorbed energy (SEA), and the crash force efficiency (CFE). To determine the most effective configuration, a multi-attribute decision-making (MADM) approach was utilized. This analysis revealed that the Rd/L25/B6/N2 combination offered the best performance in terms of crashworthiness.