Search Results (68)

Given the environmental significance of the textile industry, especially the accumulation of nondegradable materials, there is extensive development of greener approaches to fabric waste management. Here, we investigated the biodegradation potential of three Streptomyces strains in model compost on polyamide (PA) and polyamide-elastane (PA-EA) as synthetic, and on cotton (CO) as natural textile materials. Weight change of the materials was followed, while Fourier-Transform Infrared Spectroscopy (FTIR) and Scanning Electron Microscopy (SEM) were used to analyze surface changes of the materials upon biodegradation. The bioluminescence-based toxicity test employing Aliivibrio fischeri confirmed the ecological safety of the tested textiles. After 12 months, the increase of 10 and 16% weight loss, of PA-EA and PA, respectively, was observed in compost augmented with Streptomyces sp. BPS43. Additionally, a 14% increase in cotton degradation was recorded after 2 months in compost augmented with Streptomyces sp. NP10. Genome exploration of the strains was carried out for potential plastic-degrading enzymes. It highlighted BPS43 as the most versatile strain with specific amidases that show sequence identity to UMG-SP-1, UMG-SP-2, and UMG-SP-3 (polyurethane degrading enzymes identified from compost metagenome). Our results showcase the behavior of Streptomyces sp. BPS43 in the degradation of PA and PA-EA textiles in composting conditions, with enzymatic potential that could be further characterized and optimized for increased synthetic textile degradation. Full article

Rising volumes of textile waste necessitate the development of more efficient recycling systems, with a primary focus on the optimization of sorting technologies. Near-infrared (NIR) spectroscopy is a state-of-the-art method for fiber identification; however, its accuracy in quantifying textile blends, particularly common polyester/cotton blend textiles, still requires refinement. This study explores the potential and limitations of NIR spectroscopy for quantifying cotton content in post-consumer textiles. A lab-scale NIR sorter and a handheld NIR spectrometer in complementary wavelength ranges were applied to a diverse range of post-consumer textile samples to test model accuracies. Results show that the commonly assumed 10% accuracy threshold in industrial sorting can be exceeded, especially when excluding textiles with <35% cotton content. Identifying and excluding the range of non-linearity significantly improved the model’s performance. The final models achieved an RMSEP of 6.6% and bias of −0.9% for the NIR sorter and an RMSEP of 3.1% and bias of −0.6% for the handheld NIR spectrometer. This study also assessed how textile characteristics—such as color, structure, product type, and alkaline treatment—affect spectral behavior and model accuracy, highlighting their importance for refining quantification when high-purity inputs are needed. By identifying current limitations and potential sources of errors, this study provides a foundation for improving NIR-based models. Full article

The textile industry is progressively shifting towards more sustainable solutions, particularly in the field of printing technologies. This study reports the development and evaluation of water-based pigment inks formulated with bio-based pigments derived from intermediates produced via bacterial fermentation. Two pigments—indigo (blue) and quinacridone (red)—were incorporated into ink formulations and applied on cotton and polyester fabrics through valve-jet inkjet printing (ChromoJet). The physical properties of the inks were analyzed to ensure compatibility with the equipment, and printed fabrics were assessed as to their color fastness to washing, rubbing, artificial weathering, and artificial light. The results highlight the good performance of the bio-based inks, with excellent light and weathering fastness and satisfactory wash and rub resistance. The effect of different pre-treatments, including a biopolymer and a synthetic binder, was also investigated. Notably, the biopolymer pre-treatment enhanced pigment fixation on cotton, while the synthetic binder improved wash fastness on polyester. These findings support the integration of biotechnologically sourced pigments into eco-friendly textile digital printing workflows. Full article

The widespread phase-out of long-chain per- and poly-fluoroalkyl substances (PFASs) has created an urgent need for durable, fluorine-free water-repellent finishes that match the performance of legacy chemistries while minimising environmental impact. Here, the performance of an eco-friendly hybrid organic–inorganic treatment obtained by the in situ hydrolysis–condensation of triethoxy(octyl)silane (OS) in an amino-terminated polydimethylsiloxane (APT-PDMS) aqueous dispersion was investigated. The sol was applied to plain-weave cotton and polyester by a pad-dry-cure process and benchmarked against a commercial fluorinated finish. Morphology and chemistry were characterised by SEM–EDS, ATR-FTIR, and Raman spectroscopy; wettability was assessed by static contact angle, ISO 4920 spray ratings, and AATCC 193 water/alcohol repellence; and durability, handle, and breathability were evaluated through repeated laundering, bending stiffness, and water-vapour transmission rate measurements. The silica/PDMS coating formed a uniform, strongly adherent nanostructured layer conferring static contact angles of 130° on cotton and 145° on polyester. After five ISO 105-C10 wash cycles, the treated fabrics still displayed a spray rating of 5/5 and AATCC 193 grade 7, outperforming or equalling the fluorinated control, while causing ≤5% loss of water-vapour permeability and only a marginal increase in bending stiffness. These results demonstrate that the proposed one-step, water-borne sol–gel process affords a sustainable, industrially scalable route to high-performance, durable, water-repellent finishes for both natural and synthetic textiles, offering a viable alternative to PFAS-based chemistry for outdoor apparel and technical applications. Full article

Advanced deep learning has assisted in various operations in different industries. In the textile industry, the professional must be trained and experienced in fabric classification. Fabrics such as Abaca are difficult to classify as the same base material is intertwined with a different material. The versatile nature of Abaca is used in various products including paper bills, ropes, handwoven handicrafts, and fabric. Abaca fabric is an unsought product of fabric due to its rough texture. Blended Abaca fabrics are traditionally mixed with cotton, silk, and polyester. Due to the combination of the characteristics of the materials, the fabric classification is prone to human error. Therefore, we created a device capable of classifying blends of Abaca fabric using YOLOv8 architecture. We used a Raspberry Pi 4B with camera module v3 to capture images for classification. The dataset consisted of four blends, specifically Abaca, Cotton Abaca, Polyester Abaca, and Silk Abaca. A total 500 images were used to test the model’s performance, and the performance accuracy was 94.6%. Full article

Industrial residues are sources of functional biopolymers with interesting properties for textile applications. This study aims to evaluate the impact of enzymatic pre-treatment on oil yield and phenolic compounds’ content in an aqueous extraction process, as well as the functional properties incorporated into textiles. This research investigated the influence of residue granulometry, biomass percentage, and the application of enzymatic pre-treatment with different enzymes (cellulase, pectinase, xylanase) individually or in combination. Chestnut hedgehog (CH), tobacco plant stems (TPSs), vine shoot trimmings (VSTs), and beer spent grain (BSG) were explored. For textile functionalization, the extracted oils were incorporated into a bio-based formulation and applied on cotton fabric through pad-dry-cure. For CH, the pre-treatment with cellulase and xylanase achieved an oil yield of 149 and 148 mg oil/mL extract, respectively. With the combination of both enzymes, the richest oil in phenolic compounds was extracted: 1967.73 ± 16.86 mg GAE/g biomass. CH and TPS oils presented an antioxidant activity above 60%, and the functionalized textiles also showed the highest antioxidant potential and a UPF of 30. The textiles presented water repellence and washing fastness. This study demonstrates a sustainable oil extraction method and its potential application in the development of functional textiles. Full article

Synthetic and natural fibers are widely used in the textile industry. Natural fibers include cellulose-based materials like cotton, and regenerated fibers like viscose as well as protein-based fibers such as silk and wool. Synthetic fibers, on the other hand, include PET and polyamides (like nylon). Due to significant differences in their chemistry, distinct dyeing processes are required, each generating specific waste. For example, cellulose fibers exhibit chemical inertness toward dyes, necessitating chemical auxiliaries that contribute to wastewater contamination, whereas synthetic fibers are a major source of non-biodegradable microplastic emissions. Addressing the environmental impact of fiber processing requires a deep molecular-level understanding to enable informed decision-making. This manuscript emphasizes potential solutions, particularly through the biodegradation of textile materials and related chemical waste, aligning with the United Nations Sustainable Development Goal 6, which promotes clean water and sanitation. For instance, cost-effective methods using enzymes or microbes can aid in processing the fibers and their associated dyeing solutions while also addressing textile wastewater, which contains high concentrations of unreacted dyes, salts, and other highly water-soluble pollutants. This paper covers different aspects of fiber chemistry, dyeing, degradation mechanisms, and the chemical waste produced by the textile industry, while highlighting microbial-based strategies for waste mitigation. The integration of microbes not only offers a solution for managing large volumes of textile waste but also paves the way for sustainable technologies. Full article

The YOLO series is widely recognized for its efficiency in the real-time detection of objects within images and videos. Accurately identifying and classifying fabric types in the textile industry is vital to ensuring quality, managing supply, and increasing customer satisfaction. We developed a method for fabric type classification and object detection using the YOLOv5 architecture. The model was trained on a diverse dataset containing images of different fabrics, including cotton, hanbok, dyed cotton yarn, and a plain cotton blend. We conducted a dataset preparation process, including data collection, annotation, and training procedures for data augmentation to improve model generalization. The model’s performance was evaluated using precision, recall, and F1-score. The developed model detected and classified fabrics with an accuracy of 81.08%. YOLOv5s allowed a faster performance than other models. The model can be used for automated quality control, inventory tracking, and retail analytics. The deep learning-based object detection method with YOLOv5 addresses challenges related to fabric classification, improving the abilities and productivity of manufacturing and operations. Full article

The growing environmental impact of textile waste, fueled by the rapid rise in global fiber production, underscores the urgent need for sustainable end-of-life solutions. This review explores cutting-edge pathways for textile waste management, spotlighting innovations that reduce reliance on incineration and landfilling while driving material circularity. It highlights advancements in collection, sorting, and pretreatment technologies, as well as both established and emerging recycling methods. Smart collection systems utilizing tags and sensors show great promise in streamlining logistics by automating pick-up routes and transactions. For sorting, automated technologies like near-infrared and hyperspectral imaging lead the way in accurate and scalable fiber separation. Automated disassembly techniques are effective at removing problematic elements, though other pretreatments, such as color and finish removal, still need to be customized for specific waste streams. Mechanical fiber recycling is ideal for textiles with strong mechanical properties but has limitations, particularly with blended fabrics, and cannot be repeated endlessly. Polymer recycling—through melting or dissolving waste polymers—produces higher-quality recycled materials but comes with high energy and solvent demands. Chemical recycling, especially solvolysis and pyrolysis, excels at breaking down synthetic polymers like polyester, with the potential to yield virgin-quality monomers. Meanwhile, biological methods, though still in their infancy, show promise for recycling natural fibers like cotton and wool. When other methods are not viable, gasification can be used to convert waste into synthesis gas. The review concludes that the future of sustainable textile recycling hinges on integrating automated sorting systems and advancing solvent-based and chemical recycling technologies. These innovations, supported by eco-design principles, progressive policies, and industry collaboration, are essential to building a resilient, circular textile economy. Full article

This study primarily focuses on the analysis of volatile organic compounds using GC–MS, with ICP-MS employed as a complementary method to quantify trace metal content. Headspace GC–MS was conducted to detect alkylphenol ethoxylates (APEOs), formaldehyde, aromatic amines derived from azo dyes, perfluorinated carboxylic acids, chlorophenols (PCPs), tetrachlorophenols (TPCs), and phthalates in textile samples of different origin and composition. Principal component analysis was used to detect patterns in the volatilome according to the origin and the textile composition. In addition, seven metals (Cr, Ni, Cu, Mo, Cd, Hg, and Pb) were quantified in a subset of samples. The study revealed distinct chemical profiles in textiles based on their origin, with GC–MS identifying key volatile organic compounds and ICP-MS quantifying heavy metals in a subset of samples. Principal component analysis highlighted cotton content as a critical factor in differentiating textile profiles. While most samples adhered to regulatory standards, some exceeded thresholds for metals like copper and nickel, underscoring the need for enhanced quality control in manufacturing processes. By integrating advanced analytical methods, this study provides insights into sustainable and safe textile production, offering valuable benchmarks for regulatory compliance and industry best practices. The outcomes contribute to improving product safety, promoting responsible manufacturing, and supporting regulatory bodies in the enforcement of environmental and safety standards, aligning with the growing demand for sustainability in the textile sector. Full article

The growing interest in developing antibacterial textiles using natural functional agents is largely driven by their sustainable and eco-friendly attributes. Lignin, a highly available biopolymer with a polyphenolic structure, has drawn attention due to its potential as a bioactive antibacterial agent. However, its inherent heterogeneity poses challenges, particularly regarding its antibacterial efficacy. In this study, unmodified kraft lignin sourced directly from the paper industry was applied to cotton and polyester fabrics, using a knife-coating technique with varying concentrations (0%, 5%, 10%, 20%, and 30% w/v), to assess its potential as an antibacterial coating. The lignin-coated fabrics demonstrated hydrophobic properties, with water contact angles reaching up to 110.3° and 112.6°, for polyester and cotton fabrics, respectively, alongside significantly reduced air permeability and water vapor permeability indexes, regardless of lignin concentration. Antibacterial evaluations also revealed that lignin-based coatings, with at least 10% w/v concentration, allowed cotton fabrics with a bacterial reduction surpassing 96%, according to ASTM E2149-2013, particularly for Gram-positive S. aureus, highlighting the potential of lignin as an antibacterial agent. Despite their limited resistance to domestic washing, the lignin-coated fabrics demonstrated exceptional stability under hot-pressing conditions. Therefore, this stability, combined with the hydrophobic and antibacterial properties observed, particularly on coated cotton fabrics, highlights the potential application of lignin-based coatings for the development of antibacterial and water-repellent textiles, with these coatings being particularly suited for single-use applications or scenarios where washing resistance is not a requirement. This approach offers a sustainable and efficient method for producing functional textiles while enabling value-added utilization of lignin, showcasing its potential as an eco-friendly solution in textile functionalization. Full article

Cotton, the most widely produced natural fiber, is integral to the textile industry and sustains the livelihoods of millions worldwide. However, its quality is frequently compromised by contamination, particularly from honeydew, a substance secreted by insects that leads to the formation of sticky fibers, thereby impeding textile processing. This study investigates ultraviolet (UV) hyperspectral imaging (230–380 nm) combined with multivariate data analysis to detect and quantify honeydew contaminations in real cotton samples. Reference cotton samples were sprayed multiple times with honey solutions to replicate the natural composition of honeydew. Comparisons were made with an alternative method where samples were soaked in sugar solutions of varying concentrations. Principal component analysis (PCA) and quadratic discriminant analysis (QDA) effectively differentiated and classified samples based on honey spraying times. Additionally, partial least squares regression (PLS-R) was utilized to predict the honeydew content for each pixel in hyperspectral images, achieving a cross-validation coefficient of determination R² = 0.75 and root mean square error of RMSE = 0.8 for the honey model. By employing a realistic spraying method that closely mimics natural contamination, this study refines sample preparation techniques for improved evaluation of honeydew levels. In conclusion, the integration of hyperspectral imaging with multivariate analysis represents a robust, non-destructive, and rapid approach for real-time detection of honeydew contamination in cotton, offering significant potential for industrial applications. Full article

This article provides information on the processing of chromium-containing waste from the Aktobe ferroalloy compounds plant using chemical reagents followed by high-temperature heat treatment for the synthesis of a composite chromite pigment used in the textile industry. This technology was developed for the first time for the purpose of recycling industrial waste and rational use of natural resources. The obtained pigments were analyzed by the X-ray phase of a D878-PC75-17.0 incident beam monochromator and the phase composition of the composite chromite pigment was studied. The thermogravimetric analysis of the composite chromite pigments was performed using a TGA/DSC 1HT/319 analyzer to determine the change in mass with time and temperature. According to the TGA results, the mass loss was determined to be 0.18% of the total mass. The elemental composition of the composite chromite pigment was determined using a JEOL JSM-6490 LV SEM device and the content of chromium oxide (Cr₂O₃) was determined, which reached up to 50%. The thermodynamic patterns of the processes occurring during the production of chromite pigments were studied using the integrated Chemistry software pack HSC-6. The results of testing printed and processed cotton and composite fabrics by the proposed method showed that the color fastness to washing and wet and dry friction is 4 points and the wear resistance assessment is 4860 and 6485 cycles, respectively. Composite chromite pigment based on technogenic wastes is recommended for use in various coloring compositions, including those used for printing on cotton and composite fabrics. Full article

Cotton is a crucial crop in the global textile industry, with major production regions including China, India, and the United States. While smart agricultural mechanization technologies, such as automated irrigation and precision pesticide systems, have improved crop management, weeds remain a significant challenge. These weeds not only compete with cotton for nutrients but can also serve as hosts for diseases, affecting both cotton yield and quality. Existing weed detection models perform poorly in the complex environment of cotton fields, where the visual features of weeds and crops are similar and often overlap, resulting in low detection accuracy. Furthermore, real-time deployment on edge devices is difficult. To address these issues, this study proposes an improved lightweight weed detection model, YOLO-WL, based on the YOLOv8 architecture. The model leverages EfficientNet to reconstruct the backbone, reducing model complexity and enhancing detection speed. To compensate for any performance loss due to backbone simplification, CA (cross-attention) is introduced into the backbone, improving feature sensitivity. Finally, AFPN (Adaptive Feature Pyramid Network) and EMA (efficient multi-scale attention) mechanisms are integrated into the neck to further strengthen feature extraction and improve weed detection accuracy. At the same time, the model maintains a lightweight design suitable for deployment on edge devices. Experiments on the CottonWeedDet12 dataset show that the YOLO-WL model achieved an mAP of 92.30%, reduced the detection time per image by 75% to 1.9 ms, and decreased the number of parameters by 30.3%. After TensorRT optimization, the video inference time was reduced from 23.134 ms to 2.443 ms per frame, enabling real-time detection in practical agricultural environments. Full article

Nowadays, cotton boll detection techniques are becoming essential for weaving and textile industries based on the production of cotton. There are limited techniques developed to segment, detect, and count cotton bolls precisely. This analysis identified several limitations and issues with these techniques, including their complex structure, low performance, time complexity, poor quality data, and so on. A proposed technique was developed to overcome these issues and enhance the performance of the detection and counting of cotton bolls. Initially, data were gathered from the dataset, and a pre-processing stage was performed to enhance image quality. An adaptive Gaussian–Wiener filter (AGWF) was utilized to remove noise from the acquired images. Then, an improved Harris Hawks arithmetic optimization algorithm (IH²AOA) was used for segmentation. Finally, an anchor-free compact central attention cotton boll detection network (A-frC²AcbdN) was utilized for cotton boll detection and counting. The proposed technique utilized an annotated dataset extracted from weakly supervised cotton boll detection and counting, aiming to enhance the accuracy and efficiency in identifying and quantifying cotton bolls in the agricultural domain. The accuracy of the proposed technique was 94%, which is higher than that of other related techniques. Similarly, the precision, recall, F1-score, and specificity of the proposed technique were 93.8%, 92.99%, 93.48%, and 92.99%, respectively. Full article