Abstract The contamination of the aquatic environment is becoming a serious problem. Dyes are considered as micropollutants and visible in aquatic environment at very low concentrations as 1 mg L−1. These are utilized in many application areas like textile, paper, printing and tannery industries etc. The dye used in textile industries contaminate aquatic habitat and show potential toxicity towards aquatic organisms, which may enter the food chain. The present review discusses the impact of textile dyes on water bodies, aquatic microalgae and macrophytes. It also discusses the different classes of dye which are classifying according to their solubility in water. These dyes are acidic, basic, direct, disperse, vat, sulfur and pigments. These dyes do not tightly bind to the fabric; its discharge as an effluent in aquatic environment could vary from 2% for basic dyes to as high as 50% for reactive dyes. Due to the unawareness and continuous discharge of textile dyes without prior treatment into the environment and their persistence constitutively increasing the risk of the aquatic flora and decrease the quality of water like eutrophication, odor, color and turbidity and the long-term hazard like accumulation of carcinogenic products, Persistence and production of by-products. It also causes carcinogenicity and mutagenicity.
Naoto Tomita, Suguru Sato, Toshihiro Takeshita
et al.
In this study, we developed a UV-tape-assisted laser patterning (UT-Laser) technique that enables the simple transfer-based formation of wiring with line widths below 200 $μ$m onto textile substrates. With the rapid advancement of wearable devices capable of acquiring various types of physiological and environmental information, research on electronic textiles (e-textiles)-in which electronic components are integrated into fabrics and clothing-has progressed considerably. However, integrating high-performance, rigid electronic components onto textiles remains challenging: the diameter of textile fibers limits the formation of fine wiring, making reliable mounting of such components difficult. To address these challenges, we devised the UT-Laser technique, in which thin foil or film materials are laser vector-cut on UV tape, and the adhesive strength is controlled through UV exposure. The unnecessary portions are selectively and collectively peeled away to form fine wiring, which is subsequently transferred onto the textile substrate. This approach enables facile fabrication of fine wiring with line widths below 200 $μ$m on textiles. Furthermore, by forming fine wiring from a flexible copper clad laminate and transferring it onto heat-resistant glass cloth, electronic components can be soldered directly, allowing the fabrication of e-textile devices capable of withstanding more than 10,000 bending cycles. The prototype e-textile device fabricated using the proposed method integrates a microcontroller, USB connector, battery holder, flash memory, inertial measurement unit, and environmental sensors, and successfully acquires data related to stair climbing, respiration, and changes in body temperature during sleep.
To address issues such as unreasonable band selection and feature redundancy in waste textile detection, this study proposes a multi-attribute group decision-making framework based on cubic q-rung orthopair fuzzy sets for optimizing infrared bands. Firstly, a novel cubic q-rung orthopair fuzzy power Maclaurin symmetric mean (Cq-ROFPMSM) operator is developed by integrating the PA and MSM operators to capture correlations among spectral attributes and mitigate the influence of outliers, thereby enabling more effective fusion of uncertain spectral information. To objectively determine attribute weights, a novel hybrid weighting MEREC-CRITIC method was proposed, which can reflect both the mutual influence among attributes and their overall importance. With five typical waste textiles as decision objects, the information content, separability, and correlation of the bands are selected as evaluation attributes. Finally, the CODAS method is employed to rank five band division schemes, and the results indicate that the proposed model identifies an optimal band range of 1800 ~ 2600 nm. Compared with the baseline methods such as KNN, DT and CNN, the proposed method improves image quality by 35% and classification accuracy by 25% in waste textile detection. This study contributes a theoretically robust and practically applicable decision framework for spectral band optimization in waste textile recycling.
Science, Textile bleaching, dyeing, printing, etc.
Several noteworthy scenarios emerged in the global textile and fashion supply chains during and after the COVID-19 pandemic. The destabilizing influences of a global pandemic and a geographically localized conflict are being acutely noticed in the worldwide fashion and textile supply chains. This work examines the impact of the COVID-19 pandemic, the Russo-Ukraine conflict, Israel-Palestine conflict, and Indo-Pak conflict on supply chains within the textile and fashion industry. This research employed a content analysis method to identify relevant articles and news from sources such as Google Scholar, the Summon database of North Carolina State University, and the scholarly news portal NexisUni. The selected papers, news articles, and reports provide a comprehensive overview of the fashion, textile, and apparel supply chain disruptions caused by the pandemic and the war in Ukraine, accompanied by discussions from common supply chain perspectives. Disruptions due to COVID-19 include international brands and retailers canceling orders, closures of stores and factories in developing countries, layoffs, and furloughs of workers in both retail stores and supplier factories, the increased prominence of online and e-commerce businesses, the growing importance of automation and digitalization in the fashion supply chain, considerations of sustainability, and the need for a resilient supply chain system to facilitate post-pandemic recovery. In the case of the Russo-Ukraine war, Israel-Palestine war, and Indo-Pak war, the second-order effects of the conflict have had a more significant impact on the textile supply chain than the direct military operations themselves. In addition to these topics, the study delves into the potential strategies for restoring and strengthening the fashion supply chain
Automated sorting is crucial for improving the efficiency and scalability of textile recycling, but accurately identifying material composition and detecting contaminants from sensor data remains challenging. This paper investigates the use of standard RGB imagery, a cost-effective sensing modality, for key pre-processing tasks in an automated system. We present computer vision components designed for a conveyor belt setup to perform (a) classification of four common textile types and (b) segmentation of non-textile features such as buttons and zippers. For classification, several pre-trained architectures were evaluated using transfer learning and cross-validation, with EfficientNetB0 achieving the best performance on a held-out test set with 81.25\% accuracy. For feature segmentation, a zero-shot approach combining the Grounding DINO open-vocabulary detector with the Segment Anything Model (SAM) was employed, demonstrating excellent performance with a mIoU of 0.90 for the generated masks against ground truth. This study demonstrates the feasibility of using RGB images coupled with modern deep learning techniques, including transfer learning for classification and foundation models for zero-shot segmentation, to enable essential analysis steps for automated textile recycling pipelines.
This review focuses on the transformative applications of deep learning and artificial intelligence in textile dyeing, printing, and finishing. In textile dyeing, the topics span color prediction, color-based classification, dyeing recipe prediction, dyeing pattern recognition, and the nuanced domain of color fabric defect detection. In textile printing, applications of artificial intelligence and machine learning center around pattern detection in printed fabrics, the generation of novel patterns, and the critical task of detecting defects in printed textiles. In textile finishing the prediction of fabric thermosetting parameters is discussed. Artificial neural networks, diverse convolutional neural network variations like AlexNet, traditional machine learning approaches including support vector regression, principal component analysis, XGBoost, and generative artificial intelligence such as generative adversarial networks, as well as genetic algorithms all find application in this multifaceted exploration. At its core, the interest to use these methodologies is because of the need to minimize repetitive and time-consuming manual tasks, curtail prototyping costs, and promote process automation. The review unravels a plethora of innovative architectures and frameworks, each tailored to address specific challenges. However, a persistent hurdle looms – the scarcity of data, which remains a significant impediment. While unveiling a collection of research findings, the review also spotlights the inherent challenges in implementing artificial intelligence solutions in the textile dyeing and printing domain.
Tussah silk is one of the most widely used wild silks. It is usually dyed with acid dyes, despite the shortcoming of poor wet fastness. Reactive dyeing is a good solution to this problem. In our work, sulfatoethylsulfone (SES), sulfatoethylsulfone/monochlorotriazine (SES/MCT), monochlorotriazine (MCT), and bis(monochlorotriazine) (Bis(MCT)) dyes were used to dye tussah silk. All of these dyes showed lower exhaustion and fixation on tussah silk than on mulberry silk under alkaline conditions. Among them, SES dyes were more applicable, with a fixation of 70–85% (at 4%owf dye) at 90 °C when using sodium bicarbonate as an alkali. SES dyes also showed a rapid fixation speed. The dyeing of tussah silk required lower sodium bicarbonate dosage, the use of more neutral electrolytes, and a higher dye quantity to achieve deep effects compared to mulberry silk. Dyed tussah silk displayed lower apparent color depth and brilliance than dyed mulberry silk. The neutral boiling dyeing of tussah silk with SES dyes exhibited higher exhaustion, higher fixation (82–92% at 4%owf dye), and a slower fixation speed compared with alkaline dyeing. Furthermore, in this dyeing method, SES dyes showed higher and more efficient fixation on tussah silk than on mulberry silk. All dyed tussah silk had excellent color fastness to soaping.
Aligning large language models (LLMs) behaviour with human intent is critical for future AI. An important yet often overlooked aspect of this alignment is the perceptual alignment. Perceptual modalities like touch are more multifaceted and nuanced compared to other sensory modalities such as vision. This work investigates how well LLMs align with human touch experiences using the "textile hand" task. We created a "Guess What Textile" interaction in which participants were given two textile samples -- a target and a reference -- to handle. Without seeing them, participants described the differences between them to the LLM. Using these descriptions, the LLM attempted to identify the target textile by assessing similarity within its high-dimensional embedding space. Our results suggest that a degree of perceptual alignment exists, however varies significantly among different textile samples. For example, LLM predictions are well aligned for silk satin, but not for cotton denim. Moreover, participants didn't perceive their textile experiences closely matched by the LLM predictions. This is only the first exploration into perceptual alignment around touch, exemplified through textile hand. We discuss possible sources of this alignment variance, and how better human-AI perceptual alignment can benefit future everyday tasks.
Textile pilling assessment is critical for textile quality control. We collect thousands of 3D point cloud images in the actual test environment of textiles and organize and label them as TextileNet8 dataset. To the best of our knowledge, it is the first publicly available eight-categories 3D point cloud dataset in the field of textile pilling assessment. Based on PointGPT, the GPT-like big model of point cloud analysis, we incorporate the global features of the input point cloud extracted from the non-parametric network into it, thus proposing the PointGPT+NN model. Using TextileNet8 as a benchmark, the experimental results show that the proposed PointGPT+NN model achieves an overall accuracy (OA) of 91.8% and a mean per-class accuracy (mAcc) of 92.2%. Test results on other publicly available datasets also validate the competitive performance of the proposed PointGPT+NN model. The proposed TextileNet8 dataset will be publicly available.
Mechanical metamaterials -- structures with unusual properties that emerge from their internal architecture -- that are designed to undergo large deformations typically exploit large internal rotations, and therefore, necessitate the incorporation of flexible hinges. In the mechanism limit, these metamaterials consist of rigid bodies connected by ideal hinges that deform at zero energy cost. However, fabrication of structures in this limit has remained elusive. Here, we demonstrate that the fabrication and integration of textile hinges provides a scalable platform for creating large structured metamaterials with mechanism-like behaviors. Further, leveraging recently introduced kinematic optimization tools, we demonstrate that textile hinges enable extreme shape-morphing responses, paving the way for the development of the next generation of mechanism-based metamaterials.
Prototyping electronic textile (e-textile) involves embedding electronic components into fabrics to develop smart clothing with specific functionalities. However, this process is still challenging since the complicated wiring setup is required during experimental phases. This paper presents plug-n-play e-knit, a large-scale, repositionable e-textile for providing trial-and-error prototyping platforms across the textile. Plug-n-play e-knit leverages industrial digital knitting machines loaded with conductive thread to automatically embed a communication and power supply network into garments, in addition to using soft magnet connectors to rearrange electronic components while preserving the stretchability of the garment. These combinations enable users to quickly establish e-textile sensor networks, and moreover test the performance and optimal placement of the electric devices on the textile. We demonstrated that our textiles leveraging custom I2C protocols could achieve the motion-resilient motion-tracking sensor network over a 2700 $cm^2$ garment area.
Hand-wearable robots, specifically exoskeletons, are designed to aid hands in daily activities, playing a crucial role in post-stroke rehabilitation and assisting the elderly. Our contribution to this field is a textile robotic glove with integrated actuators. These actuators, powered by pneumatic pressure, guide the user's hand to a desired position. Crafted from textile materials, our soft robotic glove prioritizes safety, lightweight construction, and user comfort. Utilizing the ruffles technique, integrated actuators guarantee high performance in blocking force and bending effectiveness. Additionally, we present a participant study confirming the effectiveness of our robotic device.
Aldo Joao Cárdenas-Oscanoa, Jean Lawrence Tene Tayo, Caoxing Huang
et al.
In recent decades, there has been a growing concern about the excessive consumption on petroleum-based sources. Scientists are now focused on increasing the utilization of natural and renewable sources instead of nonrenewable ones to produce safety and environmentally friendly products. Their aim is to maintain and enhance product performance while also keeping production costs in check. Within this framework, natural-fiber insulation boards emerge as a trending topic and consequently, also the use of natural adhesives to supply them and reach an entirely friendly-environment product. Natural-fiber insulation material performance is typically evaluated by its mechanical and physical properties. Among them are bending, compression, tensile strength, density, water absorption, and thermal conductivity. Throughout the cited literature, a diversity of vegetal-origin fibers, especially wood-fibers and natural adhesive sources like lignin, tannins, and proteins for insulation materials has been found, which are constantly improved in order to reach a superior production scale. This work provides a summary of research that focuses on natural fiber insulation products as well as natural adhesives, pointing to Polylactic Acid (PLA).
Science, Textile bleaching, dyeing, printing, etc.
The scope of our study was to investigate the changes in electrospun polylactic acid (PLA) fiber mats’ morphological, mechanical, and thermal properties in vitro. We electrospun two sets of PLA fiber mats with different average diameters, E6 (747 nm) and E10 (1263 nm). The degradation study of PLA electrospun fibers was carried out in phosphate-buffered saline solution at 37 °C to simulate conditions within the human system. The results reveal the thicker fibers (E10) degraded more rapidly than the E6 sample due to their different morphology. E10 showed a 29% reduction in diameter and a 41% weight loss, while E6 exhibited an 18% reduction in diameter and a 27.5% weight loss. E6’s Young’s modulus increased by 3.55 times, while E10’s rose by 2.23 times after 28 days of degradation, and the fibers became more rigid. E6 showed a more pronounced decrease in crystallinity compared with E10. Changes in electrospun fiber diameters and crystallinity greatly influence the degradation mechanism of PLA.
Chemicals: Manufacture, use, etc., Textile bleaching, dyeing, printing, etc.
Abstract Smart technology has become an increasingly prominent feature in the fashion apparel industry. However, small retailers still face challenges while trying to adopt such innovative smart technologies to increase consumer interaction and sales. By applying the Technology, Organization, and Environment (TOE) framework, the aim of this study is to understand small independent fashion retail owners’ and employees’ thoughts on innovative retail technologies and their reactions to the currently available new technology. A qualitative research method of in-depth interviews with 11 participants working in fashion companies was used. The themes that emerged in this study represent criteria to be met prior to adopting new technology for small fashion retailers, including transparency, consistency, and integration of technology. While there were concerns regarding the cost and timing of adopting smart technology, they all expressed unanimous agreement that these advancements would become the next major trend in fashion retail, enhancing consumer connectivity. In particular, the smart technology they plan to adopt must possess the capability for reciprocity between consumers and the company. This will motivate a resurgence of innovative technologies in the less advanced fragment of small independent fashion retailers. Future research can focus on analyzing how the implementation of new smart technologies affects these types of businesses and their customer satisfaction.
Textile bleaching, dyeing, printing, etc., Social Sciences
Water is used significantly in the textile industry's various processing operations. The success of textile wet processing strongly depends on a consistent and clean supply of good-quality water. Hence, it is imperative to bestow the greatest attention towards the quality of water. Any problem in textile processing is usually associated with the water used. It is therefore essential to first test the sample of water to obviate any difficulties that the textile processing unit may face. Usually, water is considered to be unsuitable for textile processing if it contains more than 250ppm parts of hardness. Hardness in water causes wastage of dyes, chemicals and soaps, dullness of shades, uneven and patchy shades in dyeing and printing, poor fastness in shade in processed fabric, corrosion of the boiler and vessels. Keeping this in mind, the present work was carried in textile chemistry laboratory of Banasthali Vidyapith. The main objective was to see the effect of hardness of water on wet textile processing. The procedure adopted was divided into three phases. In first phase, fabric and raw materials were collected and water was prepared in textile chemistry laboratory by the researcher herself. The second phase included procedure adopted for carrying out wet textile processing (desizing, scouring, bleaching, and dyeing) with three different water samples (hard, moderately hard and soft). In the last phase, the treated samples were analyzed on the basis of percentage weight loss, whiteness index and color fastness test, percentage dye absorption and wettability test etc. It was observed that weight loss after desizing process (enzymatic desizing) was found to be more with soft water as compared to the hard and moderately hard water. In the scouring process, it was found that with moderately hard water, weight loss percentage was more as compared to soft and hard water. In the bleaching process, it was found that sample was more white when treated with moderately hard water compared to hard and soft water. In dyeing process, it was found that intensity of shades in sample treated with moderately hard water was more as compared to hard and soft water. It can also be said that some degree of hardness is required for textile wet processing.
The article presents data on the development of technology for the preparation of cotton fabrics for subsequent coloring processes and final finishing. Improving the quality of products in the process of preliminary preparation of fabrics is associated with the development of highly efficient technologies using textile auxiliaries, new chemical and physical methods for intensifying ongoing processes. Technologies have been developed for the preparation of cotton fabrics aimed at obtaining the required level of physical and mechanical characteristics of materials. The optimal parameters for the preparation of technological solutions, concentrations of chemicals, the ratio of components in the solution, temperature and duration of the process were selected. The qualitative parameters of the treated tissue, capillarity, degree of whiteness, and discontinuity characteristics were studied. A periodic and continuous method of preparing cotton fabrics is proposed, ensuring the removal of non-fibrous impurities from harsh fabrics, which will improve the quality of the treated fabric by increasing capillarity and whiteness, reduce energy consumption and duration of the process. The obtained research allows us to build modern and promising, economically justified technological processes for the preparation of textile materials made from natural, chemical fibers and their mixtures.
This study presents a novel approach for touch sensing using semi-elastic textile surfaces that does not require the placement of additional sensors in the sensing area, instead relying on sensors located on the border of the textile. The proposed approach is demonstrated through experiments involving an elastic Jersey fabric and a variety of machine-learning models. The performance of one particular border-based sensor design is evaluated in depth. By using visual markers, the best-performing visual sensor arrangement predicts a single touch point with a mean squared error of 1.36 mm on an area of 125mm by 125mm. We built a textile only prototype that is able to classify touch at three indent levels (0, 15, and 20 mm) with an accuracy of 82.85%. Our results suggest that this approach has potential applications in wearable technology and smart textiles, making it a promising avenue for further exploration in these fields.