This study investigates the physical, mechanical, and handle properties of woven fabrics produced using various luxury animal fibres, including 100% cashmere, superfine wool, wool/silk (70/30), and cashmere/silk (70/30) blends. All fabrics were woven under identical construction conditions, with only the weft yarn composition varying. Comprehensive testing, covering breaking strength, tear resistance, seam slippage, dimensional stability, elongation, air permeability, and bending rigidity, was conducted before and after finishing processes. Results showed that 100% cashmere fabrics exhibited the highest breaking strength, while wool/silk blends offered comparable performance with significant cost advantages. Coarser wool yarns (21.5 μm) provided superior tear strength, whereas silk blends enhanced elongation and resilience. Wool/silk fabrics also demonstrated the best seam slippage resistance and the highest air permeability. Cashmere and silk-containing fabrics, though softer and more drapable, showed greater dimensional shrinkage after finishing. Statistical analysis revealed that fabric properties were significantly influenced by weft yarn composition (p<0.05), with finishing treatments affecting elongation, permeability, and rigidity. Notably, wool/silk (70/30) fabrics emerged as the most balanced option, combining mechanical performance, tactile comfort, and economic feasibility. These findings highlight the potential of superfine wool and silk blends as viable alternatives to cashmere in premium textile applications

This study investigates the antifungal properties of warp-knitted cotton fabrics treated with metal-based compounds containing sodium tetrahydroxocuprate (Cu), silver diamine (Ag), and sodium zincate (Zn), synthesised via novel, efficient methods designed to reduce processing complexity and cost. Treatments were applied at concentrations of 0.5% and 1%, and antifungal efficacy was assessed against Candida albicans over 28 days. Surface morphology and chemical composition analyses were conducted using scanning electron microscopy (SEM), energy-dispersive spectroscopy (EDS), and Fourier-transform infrared spectroscopy (FTIR). Results revealed uniform particle distribution on fabric surfaces. It was found that Cu-based particles tended to accumulate more densely and demonstrated stronger adhesion on the yarn surfaces. In contrast, in comparison, this effect became gradually weaker in the samples containing Ag and Zn particles. Antifungal testing demonstrated that Cu-treated fabrics exhibited the highest reduction in fungal load, achieving a 4 log10 (10,000-fold) decrease at 1% concentration by day 7, with sustained activity through day 28. Ag treatments resulted in up to a 3.04 log10 reduction, while Zn treatments showed reductions up to 3.22 log10 at 1%. The 1% metal complex concentration consistently outperformed 0.5% across all metals. Findings highlight Cu compounds as particularly effective for healthcare textiles due to rapid and robust antifungal activity, whereas Ag and Zn compounds offer stable, long-term protection.

With the increasingly intensified market competition, Chinese small and medium-sized green textile enterprises (SMGTEs) are now facing more supply chain challenges, such as sharp demand fluctuations, rising costs and even supply chain disruptions. As one of the most important abilities to deal with uncertainty and risk, supply chain resilience is of great significance for the survival and development of Chinese SMGTEs. Therefore, this study first constructed a theoretical framework of the influencing factors of supply chain resilience from a dynamic capability perspective for Chinese SMGTEs based on in-depth interviews. The measurement model was then designed, followed by a questionnaire survey and factor analysis. This study also used statistical models to empirically verify the effect of dynamic capability factors on supply chain resilience for Chinese SMGTEs. The research results will provide practical guidance for Chinese SMGTEs to sustainably improve their supply chain resilience and market competitiveness.

The article addresses a highly relevant topic: the application of artificial intelligence (AI) to the design of Minoan-inspired clothing by transforming fashion illustrations into photorealistic visualisations of physical, wearable garments. Advanced AI systems have been used to develop innovative and practical fashion design solutions that capture the timeless elegance of Minoan costumes while giving them contemporary flair. The study includes an application-based analysis of five affordable AI systems that can transform fashion drawings into photorealistic images. A comparative analysis highlights the observed differences in colours and shapes between the original fashion drawings and the AI-generated models. The effectiveness of these AI systems was validated through a survey and principal component analysis. The results obtained have practical implications in areas such as fashion design, custom clothing production, sustainable fashion, marketing and the training of professionals in this field.

Branding strategies and customer involvement have become central to Indian businesses as sustainability gains prominence across both offline and online businesses. Due to rising environmental concerns, companies are focusing on sustainable practices, energy-efficient solutions, and eco-friendly products to meet consumer demands and regulatory standards. Purchasing the products based on green innovative marketing strategies has attracted people from various nations, too. However, purchasing decisions vary from one individual to another based on the driving factors like persona, psychological, economic, payment mode, social, quality, trust, cost, reputation, reviews and offers. In this research, the association between branding strategies as an independent factor using green innovation and international marketing strategies against the dependent factor, customer involvement in the textile industry, is examined. The moderating factor ‘disposable income’ is adopted here, which gives this research its uniqueness, significance and novelty. The research adopts SEM analysis for examining the variables and the Hayes Process for moderating factor analysis. The targets are people who are interested in fashion clothing. The sample size used is n=589. The findings showed that there exists an association between green innovation in marketing (GIM) and purchase decision involvement (PDI) and international marketing (IM) and PDI. Similarly, the moderating factor, disposable income (DI), moderates the association between GIM and PDI; whereas it doesn’t moderate the IM and PDI. Thus, the research concluded that the disposable income as a moderating factor certainly impacts the purchase decision of the customers and international marketing strategies in the fashion clothing in textile industry.

The textile printing and dyeing industry, with huge chemical demand, has a negative impact on the ecosystem. Chemical footprint quantifies the toxic impacts of chemical pollutants by assessing their behaviour in the environment. In this paper, four methods were used to calculate and evaluate the chemical footprint of a polyester dress printing and dyeing process. The chemical footprint of the printing and dyeing process of a polyester dress, calculated with USEtox, Assessment of Mean Impact, Score System, and Strategy Tool, was 1585.51 PAF×m3 ×day, 14089.04 l, 331, and 75, respectively. Scouring, colouring, pretreatment, and printing were identified as the major procedures contributing, with the antifoaming agents and the chelating disperse agents as the major auxiliaries contributing. The results of the Strategy Tool are limited in their representativeness of environmental load. Compared to other methods, AMI ensures that the evaluation results are scientific while maintaining user-friendliness.

The need for natural clothing is increasing day by day. To meet this demand, the apparel industry is developing new systems to enhance production and raw material usage. Using healthy products is essential for a healthy life, which increases the need for natural raw materials. Cotton is the ideal natural raw material for a renewable and sustainable production line. Despite the growing production, it cannot fully meet the demand. Therefore, new systems are being developed to improve the quality of cotton production. The foundation of the textile industry is yarn, and yarn production lines consist of systematically operated machines. These production systems include carded, combed, and open-end methods. In combed production, high-quality and long fibres are used to produce yarns with counts such as Ne 30 or Ne 50. In combed yarn production, fibre length and ratio can be adjusted through machine settings. Lap feeding cylinder gaps in combed yarn machines are critical for this adjustment. In this study, experimental results were obtained using 4 different yarn counts produced from the same blend and 5 different combed feeding jaw settings. These results were optimised using artificial neural networks. In the analysis, yarn count and combing cylinder gap were used as input data, while the physical properties of the yarn were used as output data.

The global textile industry faces a critical inflexion point as circular economy mandates intensify and waste volumes soar beyond 100 million tonnes annually. Central to realising circularity is the efficiency and fidelity of textile waste sorting, a longstanding bottleneck dominated by manual, low-throughput, and error-prone methods. This paper investigates the deployment of an AI-enabled robotic sorting system integrating hyperspectral imaging (HSI) and deep learning algorithms within the context of India’s fragmented textile recycling ecosystem. We demonstrate that spectral imaging combined with convolutional neural networks (CNNs) achieves over 95% classification accuracy across heterogeneous, post-consumer Indian textile waste streams, including multi-fibre blends that typically confound manual sorters. Drawing from industrial benchmarks such as Sweden’s SipTex and U.S.-based Refiberd, we design a prototype that integrates conveyor automation, real-time classification, and robotic actuation. Comparative analysis reveals that the AI system achieves throughput rates exceeding 1,000 garments per hour, representing a 20× gain over manual processes while reducing misclassification rates by more than 60%. A techno-economic model suggests payback periods under four years when scaled to medium-sized facilities, with significant reductions in labour dependency and waste-to-landfill ratios. Our findings have strong implications for policy and industry: AI sorting systems not only align with India’s National Textile Policy and MITRA initiatives but also represent an enabling infrastructure for chemical recycling, extended producer responsibility, and traceable material flows. By bridging technological innovation with operational scalability, this study advances the industrial feasibility of circular textiles in the Global South.

The yarn spinning method and the utilised raw material play a significant role in determining the comfort properties of fabrics. Spinning methods, such as conventional ring, mechanical compact, and Siro spinning, influence the yarn’s structure, uniformity, and surface characteristics, which in turn affect fabric properties like moisture management and thermal comfort. This study explores the moisture management and thermal comfort properties of knitted fabrics produced from different blended yarns spun on three distinct spinning techniques: Conventional ring, mechanical compact, and Siro spinning. For analysing how different spinning methods and yarn types influence some comfort properties, Moisture Management Test (MMT), Alambeta Tests and air permeability tests were performed in the context of this research. For the statistical analyses, a Two-way ANOVA test was performed in order to investigate the effect of yarn spinning method and yarn type on moisture management, thermal comfort and air permeability properties of knitted samples. The findings revealed that spinning methods and fibre blends significantly impact the properties of the fabric. The research aims to provide insights into the relationship between yarn structure and fabric behaviour, offering valuable guidance for textile development and innovation.

Conductive knitted fabrics can function as humidity sensors, detecting the presence of liquids on their surface through changes in electrical resistance. This property can be leveraged for automatic hemostasis systems, where the detection of blood at a wound site triggers real-time intervention. In this study, conductive yarns including Shieldex (Statex: 60- 440 W/m), AgSiS (Lib-40: 5 W/m), and stainless steel (60 W/m) were integrated into knitted fabrics using a Shima Seiki machine. The fabrics were characterised for mechanical strength, abrasion resistance (1,000 and 5,000 cycles), washing durability (1 and 5 cycles), and resistance to acidic and alkaline perspiration. Electrical resistance was measured under exposure to four aqueous media simulating physiological and wound conditions: deionised water (pH 6, 244 mS/cm), acidic perspiration (pH 5.5, 10.73 mS/cm), alkaline perspiration (pH 8, 11.35 mS/cm), and 20% saline solution (pH 5.0, 9.5 mS/cm). Morphological and compositional analyses were conducted using SEM, EDX, and FTIR spectroscopy. The results demonstrated that all fabrics exhibited measurable and repeatable resistance variations, with the strongest response observed for the 20% saline solution and Lib-40 conductive yarn, highlighting their potential as humidity sensors for real-time detection of bleeding events in automatic hemostasis systems.