Search Results (158)

Drawing on Sen’s capabilities approach and digital empowerment frameworks, this study investigates digital literacy as a mediating factor in the conversion of structural resources into empowerment outcomes for indigenous women artisans of native cotton in northern Peru. A cross-sectional explanatory study involving 100 craftswomen used structural equation modeling (PLS-SEM) to examine the impact of technological infrastructure, sociodemographic factors, and sociocultural knowledge on economic, personal, and social empowerment, with digital literacy as the necessary mediating mechanism. A 45-item questionnaire assessed predictor variables, the four mediator dimensions (cognitive, technical, social and communicative competencies) and the three domains of empowerment as dependent variables. PLS-SEM analysis in SmartPLS 4.0 showed that the model fit well (SRMR = 0.072, CFI = 0.931) and that the structural factors accounted for 80.4% of the variance in digital literacy. The mediator had a large effect on all areas of empowerment but had the largest effect on economic empowerment (β = 0.846, R² = 0.709) compared to personal and social empowerment (β = 0.618, β = 0.628, R² ≈ 0.37). The indirect effects validated the mediating role of digital literacy, demonstrating its function as an essential conversion mechanism that transforms infrastructural, sociodemographic, and knowledge resources into tangible empowerment gains. The results provide empirical support for skills-based frameworks in digital inclusion initiatives, advancing SDGs 5, 8, and 9 by illustrating how digital skills empower vulnerable artisanal communities to transform traditional knowledge and access to technology into multifaceted empowerment outcomes. Full article

Cleaning historical silk textiles is a particularly sensitive operation that requires precise control to prevent mechanical or chemical damage. In this study, we investigate using flexible PVA–borax-based gels to remove soot from silk, i.e., polyvinyl alcohol–borax (PVA-B) gels and polyvinyl alcohol–borax–agarose double network gels (PVA-B/AG DN) loaded with different cleaning agents—namely, 30% ethanol and 1% Ecosurf EH-6—in addition to plain gels loaded with water. These gel formulations were tested on simplified model systems (SMS) and were applied using two methods: placing and tamping. The cleaning results were compared with a traditional contact-cleaning approach; micro-vacuuming followed by sponging. Visual inspection, 3D opto-digital microscopy, colorimetry, and machine-learning-assisted (ML) soot counting were exploited for the assessment of cleaning efficacy. Rheological characterization provided information about the flexibility and handling properties of the different gel formulations. Among the tested systems, the DN gel containing only water, applied by tamping, was easy to handle and demonstrated the highest soot-removal effectiveness without leaving residues, as confirmed by micro-Fourier Transform Infrared (micro-FTIR) analysis. Scanning electron microscope (SEM) micrographs proved the structural integrity of the treated silk fibers. Overall, this work allows us to conclude that PVA–borax-based gels offer an effective, adaptable, and low-risk cleaning strategy for historical silk fabrics. Full article

Texture mapping of weft-knitted fabrics plays a crucial role in virtual try-on and digital textile design due to its computational efficiency and real-time performance. However, traditional texture mapping techniques typically adapt pre-generated textures to deformed surfaces through geometric transformations. These methods overlook the complex variations in yarn length, thickness, and loop morphology during stretching, often resulting in visual distortions. To overcome these limitations, we propose Knit-Pix2Pix, a dedicated framework for generating realistic weft-knitted fabric textures directly from knitted unit mesh maps. These maps provide grid-based representations where each cell corresponds to a physical loop region, capturing its deformation state. Knit-Pix2Pix is an integrated architecture that combines a multi-scale feature extraction module, a grid-guided attention mechanism, and a multi-scale discriminator. Together, these components address the multi-scale and deformation-aware requirements of this task. To validate our approach, we constructed a dataset of over 2000 pairs of fabric stretching images and corresponding knitted unit mesh maps, with further testing using spring-mass fabric simulation. Experiments show that, compared with traditional texture mapping methods, SSIM increased by 21.8%, PSNR by 20.9%, and LPIPS decreased by 24.3%. This integrated approach provides a practical solution for meeting the requirements of digital textile design. Full article

CO₂ laser processing has emerged as an efficient dry-finishing technique capable of inducing controlled chemical and morphological transformations in cotton and denim textiles. The strong mid-infrared absorption of cellulose enables localised photothermal heating, leading to selective dye decomposition, surface oxidation, and micro-scale ablation while largely preserving the bulk fabric structure. These laser-driven mechanisms modify colour, surface chemistry, and topography in a predictable, parameter-dependent manner. Low-fluence conditions predominantly produce uniform fading through fragmentation and oxidation of indigo dye; in comparison, moderate thermal loads promote the formation of carbonyl and carboxyl groups that increase surface energy and enhance wettability. Higher fluence regimes generate micro-textured regions with increased roughness and anchoring capacity, enabling improved adhesion of dyes, coatings, and nanoparticles. Compared with conventional wet processes, CO₂ laser treatment eliminates chemical effluents, strongly reduces water consumption and supports digitally controlled, Industry 4.0-compatible manufacturing workflows. Despite its advantages, challenges remain in standardising processing parameters, quantifying oxidation depth, modelling thermal behaviour, and assessing the long-term stability of functionalised surfaces under real usage conditions. In this review, we consolidate current knowledge on the mechanistic pathways, processing windows, and functional potential of CO₂ laser-modified cotton substrates. By integrating findings from recent studies and identifying critical research gaps, the review supports the development of predictable, scalable, and sustainable laser-based cotton textile processing technologies. Full article

The problem of textile industry waste has become increasingly relevant. Recycling clothing industry waste to build acoustic panels is one of the most popular and relatively inexpensive ways to use clothing industry waste. We see a lack of information on the acoustic properties of panels made from waste from the clothing industry. The aim of this research is to determine the acoustic properties of a wide range of clothing industry waste recycled into acoustic panels. The acoustic panels were made from clothing industry waste, a different composition of textile and paper residues generated during digital printing processes. We see that panels made from square-cut scraps knitted and woven fabrics, and from yarns and fibers have relatively good acoustic properties. The panel made only of paper had good acoustic properties, the production of panels from paper and textile resulted in similar acoustic properties. Analyzing the acoustic properties of the double specimen, it was found that testing the double-layered panels, the insertion loss is better; by tripling the samples, it was found that although the acoustic properties improved, they were only marginal. Cellulose fiber boards were characterized by significantly higher air resistance. The air resistance of the boards made from fabric scraps was lower. Full article

The rapid evolution of innovative materials and their 4D printing on fabrics allows textiles to change shape or properties when exposed to external stimuli. This work reviews the fundamentals of 4D printing, briefly revisiting additive manufacturing technology and materials, as both are extensively described in various articles and reviews. It also outlines the advancements in smart textiles and their functionality as multifunctional fabrics. The review focuses primarily on reviewing the technical foundations and emerging applications of 4D-printed smart polymers and their integration onto passive textiles for smart applications. Finally, a critical review is presented, emphasizing the numerous individual developments undertaken not only in academia but also by young students, independent engineers, and entrepreneurs who showcase their progress and various challenges through social media. Easy access to knowledge, digital communication, and an interest in creating new materials and structures with a relatively low budget will allow the advancement and development of 4D printing processing strategies for functional materials, promoting the creation of intelligent and adaptive textile systems. Full article

The textile sector provides essential goods, yet it remains environmentally and socially intensive, driven by high water use, pesticide dependent monocropping, chemical pollution during processing, and growing waste streams. This review examines credible pathways to sustainability by integrating emerging plant-based fibres from hemp, abaca, stinging nettle, and pineapple leaf fibre. These underutilised crops combine favourable agronomic profiles with competitive mechanical performance and are gaining momentum as the demand for demonstrably sustainable textiles increases. However, conventional fibre identification methods, including microscopy and spectroscopy, often lose reliability after wet processing and in blended fabrics, creating opportunities for mislabelling, greenwashing, and weak certification. We synthesise how advanced molecular approaches, including DNA fingerprinting, species-specific assays, and metagenomic tools, can support the authentication of fibre identity and provenance and enable linkage to Digital Product Passports. We also critically assess environmental Life Cycle Assessment (LCA) and social assessment frameworks, including S-LCA and SO-LCA, as complementary methodologies to quantify climate burden, water use, labour conditions, and supply chain risks. We argue that aligning fibre innovation with molecular traceability and harmonised life cycle evidence is essential to replace generic sustainability claims with verifiable metrics, strengthen policy and certification, and accelerate transparent, circular, and socially responsible textile value chains. Key research priorities include validated marker panels and reference libraries for non-cotton fibres, expanded region-specific LCA inventories and end-of-life scenarios, scalable fibre-to-fibre recycling routes, and practical operationalisation of SO-LCA across diverse enterprises. Full article

This study analyzes how sustainability research in the textile and apparel industry is structured and argues that technological innovation—while essential for sustainable transformation—cannot generate meaningful impact when pursued in isolation. Its effectiveness depends on alignment with environmental assessment, ethical and institutional mechanisms, and circular strategies. A review of 133 publications (2020–2024) examining titles, keywords, abstracts, and conclusions identified these four thematic axes as the core framework shaping current research. Findings show that technological innovation is the most extensively addressed dimension, yet its industrial and policy influence remains limited when not connected to standardized assessment tools, governance systems, or consumer use-phase behaviors. When the four dimensions operate collectively, technological advances achieve stronger empirical validation, institutional coherence, and circular-system integration. By addressing a key gap in prior literature—which has typically examined these dimensions separately rather than as an integrated system—this study clarifies how their coordinated interaction conditions sustainability transition pathways. The integrated framework provides a theoretical basis for understanding constraints and mediators within sustainability transitions and suggests that future research and policy should adopt system-level strategies that intentionally strengthen linkages across the four dimensions to accelerate sustainable transformation. Full article

Textile materials are widely used in diverse applications, yet their anisotropic structure and large deformations present major challenges in mechanical characterization. Conventional uniaxial tensile tests can quantify bulk properties but offer limited insight into local strain distributions. In this work, it was shown that a 2D Digital Image Correlation (DIC) technique captures full-field strain data in three types of woven cotton fabrics with distinct weave patterns and densities, each tested in warp and weft orientations. In controlled tensile experiments conducted per EN ISO 13934-1, DIC revealed that strain in the loading direction (EpsY) was highly orientation-dependent (p < 0.001), whereas strain perpendicular to loading (EpsX) was unaffected by orientation (p = 0.193). These findings contrast with traditional tensile data, which indicate significant orientation effects on maximum force and elongation (both p < 0.001). Compared to point-based techniques, 2D DIC provided richer information on anisotropic deformation, including the ability to detect local strain concentrations before failure. The strong interaction between fabric type and orientation indicates that each fabric exhibits distinct strain response when loaded along warp and weft directions, underscoring the importance of evaluating both orientations when designing or selecting textiles for multidirectional loading. By combining standard tensile testing with full-field optical strain measurements, a more comprehensive understanding of textile behavior emerges, enabling improved material selection, enhanced product performance, and broader applications in engineering and textile fields. Full article

This study explores the enhancement of Beech wood (Fagus sylvatica L.) surfaces through the laser engraving of motifs inspired by Romanian textile heritage, combining cultural preservation with modern surface design techniques. A digitization and computer-aided design (CAD)-based workflow was employed to accurately transfer traditional motifs onto wood substrates. Engraving was performed using a nitrogen laser at ten different power settings ranging from 10 W to 150 W, followed by color analysis of the engraved areas. The resulting surfaces were evaluated using the International Commission on Illumination (CIELab) system to identify optimal engraving conditions. Based on colorimetric analysis, three laser power settings were selected for final motif reproduction: 30 W, 45 W, and 105 W. The process enabled the accurate rendering of a traditional three-color motif, achieving both visual fidelity and aesthetic appeal. Results demonstrate that color-based laser engraving allows precise, durable, and culturally significant ornamentation of wooden surfaces. The conclusions highlight the potential of this technique to add artistic and commercial value to wood products while preserving and promoting cultural identity. Full article

Fabric drape characterization is vital for textile performance and aesthetics, but the conventional Cusick method is labor-intensive and incompatible with digital workflows. This study assesses a smartphone-enabled digital image processing (DIP) method for fabric drape coefficient (DC) measurement, providing an accessible, low-cost alternative to the Cusick method. Draped specimens of light, medium, and heavy fabrics were imaged at three diameters (24, 30, and 36 cm) using a smartphone positioned at three vertical distances (22, 32, and 42 cm). DCs were determined through pixel-based analysis in Adobe Photoshop^®, ImageJ^®, and MATLAB^®. Statistical comparison against the Cusick method employed F-tests for variance, two-sample t-tests for mean differences, and skewness analysis. No statistically significant differences were found between smartphone DIP (with either the iPhone or Samsung device) and Cusick measurements (p > 0.05). Neither imaging height nor software platform significantly influenced outcomes, though a 22 cm height consistently provided the most stable conditions. ImageJ^® was identified as an effective open-source option for reliable analysis. The findings establish a reliable, cost-effective, and portable method for drape evaluation, reducing technical and economic barriers while aligning with Industry 4.0 digitalization. This approach enables broader adoption of reliable textile characterization across research, industry, and domains. Full article

Growing environmental awareness is resulting in new initiatives aimed at improving quality of life and minimizing the negative impact of manufactured goods on the environment. The European Union’s strategy to introduce a Digital Product Passport fits perfectly into this trend. According to current assumptions, the DPP will be based on QR codes or NFC technology, but the use of solutions operating in higher-frequency bands is worth considering. One such solution could be a UHF RFID tag. One of the sectors where the DPP will need to be used is the textile industry, and since the authors are conducting research on textronic RFID tags, they decided to test new solutions in this area, which could ultimately serve as a ready-made solution for the future. It was decided to use commonly available conductive fabrics, which can be successfully used to manufacture antennas on typical production lines in textile factories without the involvement of specialized RFID engineers. Since the effectiveness of the tag depends on the parameters of the antenna used, it is crucial to consider the impact of different fabrics on those parameters. As part of the article, the authors prepared model antenna samples made of various conductive fabrics, and then analyzed (through simulation and experimental studies) the effect of the fabrics used on the impedance of the model antenna. Obtained results confirm the thesis about the influence of different conductive fabrics on antenna parameters, especially in the case of the real part of the impedance. The final product (tag) works equally effectively regardless of the fabric used, but the impact of changes in its parameters is noticeable (read range values dispersion). Full article

In the era of Industry 5.0, the digital revolution stands as the paramount tool for achieving efficiency and elevating the standards of quality and quantity. This study delves deeply into the invaluable applications of digital twins within real production settings, highlighting their transformative potential across a multitude of industries. Focusing particularly on textiles, machinery, and electronics manufacturing, the authors illustrate how digital twins enhance productivity, anticipate challenges, bolster the food supply chain, refine healthcare services, and propel sustainability initiatives within each sector. Through concrete examples, we demonstrate how digital twins can markedly decrease waste, energy consumption, and production downtime, all while elevating product quality and enabling virtualization. By virtually simulating physical systems, numerous operational issues can be mitigated, underscoring the pivotal role of digital twins in fostering hyper-personalization, sustainability, and resilience the foundational tenets of Industry 5.0. Nevertheless, this evaluation acknowledges the inherent challenges associated with the widespread adoption of digital twins, including concerns regarding data infrastructure, cybersecurity, and workforce adaptation. By presenting a balanced assessment of both the advantages and disadvantages, this review aims to guide future research and development endeavors, paving the way for the successful integration of this revolutionary technology as we journey toward Industry 5.0. Full article

This study develops and validates a weft knitted Mini-Jacquard in Peruvian Pima cotton as a print-free coloration strategy by integrating CAD-based pattern simulation with prototype manufacturing. A three-color design (red, blue, white) was programmed on a flat knitting machine using a 10 × 14 rapport. Color-wise yarn consumption was computed directly from the digital pattern, and the physical sample was characterized through combustion testing and optical micrographs. The prototype exhibited a yarn count of ~20/1 Ne, S-twist (~11.18 TPI), and 100% cellulosic composition. The blue yarn showed the highest consumption (≈73.81%), followed by white (≈19.65%) and red (≈6.55%), consistent with the digital rapport’s color distribution. The CAD stage ensured pattern fidelity and supported raw-material planning; the knitted sample showed a soft hand, dimensional stability, and sharp motif definition upon visual assessment. A sustainability and comparative analysis with chemical printing was conducted, revealing that the Mini-Jacquard achieved the highest design accuracy and tactile comfort, outperforming screen printing and heat transfer in geometric fidelity, chromatic homogeneity, and texture. The Mini-Jacquard optimized operational times (320 min/m²) compared to transfer printing (332 min/m²) and screen printing (740 min/m²), reducing process stages and complexity. Although Jacquard production involves higher energy costs ($34.8) and material expenses ($11.6), it provides greater structural value and durability, positioning it for high-end applications. Moreover, the Mini-Jacquard could reduce water consumption by approximately 90% and thermal energy use by 70%, eliminating chemical residues and extending fabric lifespan, thus offering high sustainability and circular potential. A transparent scenario-based analysis indicates substantial reductions in water and thermal-energy use when omitting printing/fixation/washing stages, along with the elimination of printing-stage effluents. Overall, design-integrated coloration via Mini-Jacquard is technically feasible and potentially eco-efficient for Pima-cotton value chains, with applications in apparel, accessories, and functional textiles. Full article

Supply chains are a primary contributor to global greenhouse gas (GHG) emissions, rendering their decarbonization an essential dimension of sustainable development. Artificial intelligence (AI) provides a transformative pathway by facilitating proactive emission avoidance through operational efficiency, transparency, and resilience, in contrast to post-emission mitigation approaches such as carbon capture. This study explores the potential of AI to support indirect carbon dioxide removal (CDR) via supply chain decarbonization, adopting a comparative case study methodology. Empirical evidence is drawn from Tunisian agri-food, textile, and port logistics sectors, based on multi-source datasets spanning 6–12 months and covering fleet sizes ranging from 40 to 250,000 units. Methodological robustness was ensured through the use of pre-intervention baselines, statistical imputation for missing data (<5%), and validation against 20% out-of-sample test sets. Results indicate that AI-enabled interventions achieved annual avoided emissions between 500 and 1500 tCO₂ and reduced fuel consumption by 12–15%, with sensitivity analyses incorporating ±8–12% error margins. Among the approaches tested, hybrid models integrating operational and strategic layers demonstrated the most pronounced impact, aligning immediate efficiency gains with long-term systemic decarbonization. Furthermore, AI facilitates renewable energy integration, digital twin applications, and compliance with international sustainability frameworks, notably the Paris Agreement and the United Nations Sustainable Development Goals. Nevertheless, challenges related to data quality, computational demands, limited expertise, and organizational resistance constrain scalability. The findings underscore AI’s dual role as a technological enabler and systemic driver of supply chain decarbonization, advancing its positioning within global environmental sustainability transitions. Full article