Search Results (522)

Silver nanoparticles have gained significant attention due to their remarkable antimicrobial properties, making them ideal candidates for incorporation into various coatings. These coatings exhibit antimicrobial activity through multiple mechanisms, including the release of silver ions, the generation of reactive oxygen species, and the disruption of microbial cell membranes and intracellular metabolic pathways. The integration of silver nanoparticles into coating matrices through physical embedding, chemical bonding, or surface grafting not only influences the controlled release of antimicrobial agents but also affects the mechanical stability and longevity of the coatings. Several factors, including nanoparticle size, shape, surface chemistry, and functionalization, influence the antimicrobial efficiency of these nanoparticle-based coatings. As a result, silver nanoparticle coatings have been widely applied in medical devices, textiles, antifouling surfaces, and food packaging. This review discusses the advances in using silver nanoparticles in antimicrobial coatings, focusing on the mechanisms of action, fabrication methods, and diverse applications. The review also highlights the influence of nanoparticle characteristics on antimicrobial performance, providing insights into the future directions for smart coatings. Future research is expected to focus on optimizing the fabrication techniques, enhancing the stability of silver nanoparticle coatings, and exploring innovative applications in emerging fields. Full article

Currently, the sedentary nature of office work has led to a steady increase in the prevalence of spinal disorders, including lower back pain, back pain, and neck pain. Medical research has shown that monitoring and improving sitting posture is an important measure to prevent spinal discomfort. The emergence and development of wearable technology have enabled more people to effectively monitor their health. In this study, we propose and design a textile sensor-based sitting posture correction smart garment to realize dynamic sitting reminders aimed at meeting the needs of sedentary office workers. The garment achieves real-time sitting posture recognition through integrated machine learning algorithms, with a recognition accuracy exceeding 95% using a random forest classifier. Additionally, we developed haptic vibration feedback and visual GUI feedback modes to provide sitting posture intervention and dynamic sitting reminders. To evaluate the system’s effectiveness and usability, we conducted comparative experiments analyzing sitting posture behavior before and after wearing the smart garment, along with a user satisfaction survey. The results demonstrate that the smart garment effectively helps office workers adjust their sitting posture and reduces the risk of spinal discomfort associated with prolonged sedentary work. Full article

The surface profile and structural alignment of fibers and yarns in fabrics are critical factors affecting the electrical properties of conductive textile surfaces. This study aimed to investigate the impact of fabric surface roughness and the geometrical parameters of woven fabrics on their electrical resistance properties. Surface roughness was assessed using the MicroSpy^® Profile profilometer FRT (Fries Research & Technology) Metrology™, while electrical resistance was evaluated using the Van der Pauw method. The findings indicate that rougher fabric surfaces exhibit higher electrical resistance due to surface irregularities and lower yarn compactness. In contrast, smoother fabrics improve conductivity by enhancing surface uniformity and yarn contact. Fabric density, particularly weft density, governs the structural alignment of yarns. A 35% increase in weft density (W19–W27) resulted in a 13–15% reduction in resistance, confirming that denser fabrics facilitate current flow. Higher weft density also increases directional resistance differences, enhancing anisotropic behavior. Angular distribution analysis showed lower resistance and greater anisotropy at perpendicular orientations (0° and 180°, the weft direction; 90° and 270°, the warp direction), while diagonal directions (45°, 135°, 225°, and 315°) exhibited higher resistance. Surface roughness further hindered current flow, whereas increased weft density and surface mass reduced resistance and improved the directional dependencies of the electrical resistances. This analysis was conducted based on research using woven fabrics produced from silver-plated polyamide yarns (Shieldex^® 117/17 HCB). These insights support the optimization of these conductive fabrics for smart textiles, wearable sensors, and e-textiles. Fabric variants W19 and W21, with lower resistance variability and better isotropic behavior under the S electrode arrangement, could be proposed as suitable materials for integration into compact sensing systems like heart rate or bio-signal monitors. Full article

Automation in textile manufacturing plays a pivotal role in enhancing production efficiency, precision, and innovation. This study investigates the integration of intelligent technologies in the knitting sector, focusing on industrial flat knitting machines from a leading manufacturer and the use of the advanced software platform M1plus V7.5. The software’s capabilities for the digital design and simulation of complex patterned and structural knits are explored through the development and production of five experimental knitted designs. Each sample is evaluated in terms of its structural characteristics and dimensional behaviour after washing. The results highlight the potential of software-driven optimisation to improve product accuracy, reduce shrinkage variability, and support smart manufacturing practices in the textile industry. Full article

This study presents the fabrication and characterization of magnetically active textiles using cotton fibers impregnated with suspensions of pumpkin seed oil, carbonyl iron microparticles, and propolis microparticles. The textiles were utilized to manufacture planar capacitors, enabling an investigation of the effects of static magnetic fields and the introduced microparticles on the components of complex dielectric permittivity. The results reveal that the dielectric properties of the fabricated textiles are highly sensitive to the applied magnetic field intensity, the frequency of the alternating electric field, and the composition of the impregnating suspension. The experimental findings suggest that the dielectric loss and permittivity can be finely tuned by adjusting the magnetic flux density and the proportion of propolis microparticles. The multifunctional nature of these magnetically responsive textiles, combined with the bioactive properties of the incorporated natural components, opens promising pathways for applications in smart textiles, biomedical devices, and sensor technologies. Full article

This study introduces an experimental method for evaluating the reflectivity of flexible textile materials under controlled lighting conditions. The proposed methodology employs a light booth and four standard illuminants (D65, TL84, Incandescent light A, and Department store light CWF), as well as a fixed-position lux meter to assess the reflective properties of textile samples with different knitted macrostructures. Each sample is measured against a black background, and reflectance is quantified as a ratio between the light intensity measured with and without the sample in place. The approach is especially relevant for the textile industry, as it provides valuable insights into the development of sustainable reflective materials for protective clothing and wearable technologies. By offering a repeatable, low-cost measurement technique, this method advances textile metrology, contributing to the optimization of material selection based on reflectivity needs and ensuring reliability across different lighting environments. This research supports the creation of more efficient, sustainable, and adaptive textiles. Full article

Cardiac arrhythmias are a growing global health concern, and the need for accessible, continuous monitoring has driven rapid advancements in wearable technologies. This review explores the evolution, capabilities, and clinical impact of modern wearables for arrhythmia detection, including smartwatches, smart rings, ECG patches, and smart textiles. In light of the recent surge in commercially available wearables across all categories, this review offers a detailed comparative analysis of leading devices, evaluating cost, regulatory approval, model specifications, and system compatibility. Smartwatches and patches, in particular, show a strong performance in atrial fibrillation detection, with patches outperforming Holter monitors in long-term monitoring and diagnostic yield. This review highlights a paradigm shift toward patient-initiated diagnostics but also discusses challenges such as false positives, regulatory gaps, and healthcare integration. Overall, wearable devices hold significant promise for reshaping arrhythmia management through early detection and remote monitoring. Full article

The integration of machine learning (ML) has begun to reshape the development of advanced polymeric materials used in technical textiles. Polymeric materials, with their versatile properties, are central to the performance of technical textiles across industries such as healthcare, aerospace, automotive, and construction. By utilizing ML and AI, researchers are now able to design and optimize polymers for specific applications more efficiently, predict their behavior under extreme conditions, and develop smart, responsive textiles that enhance functionality. This review highlights the transformative potential of ML in polymer-based textiles, enabling advancements in waste sorting (with classification accuracy of up to 100% for pure fibers), material design (predicting stiffness properties within 10% error), defect prediction (enabling proactive interventions in fabric production), and smart wearable systems (achieving response times as low as 192 ms for physiological monitoring). The integration of AI technologies drives sustainable innovation and enhances the functionality of textile products. Through case studies and examples, this review provides guidance for future research in the development of polymer-based technical textiles using AI and ML technologies. Full article

This study aims to examine how digital supply chain (DSC) dimensions—including digital performance measurement, information technology, digital suppliers, manufacturing, logistics and inventory, and digital client management—influence supply chain agility, and how agility subsequently impacts innovation performance within Moroccan industries. A cross-sectional quantitative research design was employed, collecting data via structured questionnaires from 634 supply chain professionals across sectors such as agri-food, textile, automotive, and aerospace in Morocco. Data were analyzed using structural equation modeling (SEM) with SmartPLS 4, evaluating direct and mediating relationships among variables. Results reveal that digital performance measurement, information technology, and logistics significantly enhance supply chain agility, which in turn strongly boosts innovation performance. Surprisingly, digital manufacturing negatively impacted agility, while digital suppliers and digital clients showed no significant direct effect. Theoretically, the study provides empirical evidence linking DSC dimensions to agility-mediated innovation performance, enriching dynamic capabilities and resource-based views. Practically, it advises managers to prioritize digital performance monitoring and IT integration to foster agility-driven innovation. This paper disaggregates digital supply chain dimensions, clarifying their distinct impacts on agility and innovation, thus addressing a research gap in digital transformation literature. Full article

Smart textiles are an evolving field, but challenges in durability, washing, interfacing, and sustainability persist. Widespread adoption requires robust, lightweight, fully integrated fiber-based conductors. This paper proposes using ultralong carbon nanotube (UCNT) yarns with a width-to-length ratio of several orders of magnitude larger than typical carbon nanotube fibers. These yarns enable the manufacturing of stable, workable structures, composed of a network of twisted fibers (tows), which are suitable for fabric integration. Our research includes the creation of textile prototype demonstrators integrated with coated and non-coated UCNT yarns, tested under military-grade standards for both mechanical durability and electric functionality. The demonstrators were evaluated for their electrical and mechanical properties under washability, abrasion, and weathering. Notably, polymer-coated UCNT yarns demonstrated improved mechanical durability and electrical performance, showing promising results. However, washing tests revealed the presence of UCNT nanofibers in the residue, raising concerns due to their classification as hazards by the World Health Organization. This paper examines the sources of fiber release and discusses necessary improvements to coating formulations and testing protocols to mitigate fiber loss and enhance their practical viability. These findings underscore both the potential and limitations of UCNT yarns in military textile applications. Full article

Nowadays, the emphasis on sustainable performance highlights the necessity for resilience and innovation in tackling environmental and economic concerns within supply chain operations. Therefore, this study investigates the impact of six supply chain management practices (SCMPs) on organizational performance (OP) and environmental sustainability performance (ESP), along with the moderating role of supply chain dynamism. This research was conducted within medium and large export manufacturing firms in Jordan’s Garment, Textile, and Leather (GTL) sector, a pivotal export industry critical to the country’s economy. Data were gathered from 204 managers, employing an online self-administered questionnaire, using a quantitative research approach. The hypotheses were examined via structural equation modeling (SEM) through the SmartPLS software4. The findings reveal that ESP was significantly influenced by strategic supplier partnership and postponement. Additionally, the level of information sharing and internal lean practices were found to have a dual impact on both OP and ESP. Supply chain dynamism acted as a significant moderator only in the relationship between postponement and both OP and ESP. This study fills a significant gap in the GTL context in developing economies for export manufacturing firms that contribute to the current literature. What makes it original is its consideration of supply chain dynamism as a moderating variable and its context in an important sector for Jordan’s economy. In conclusion, the results present valuable implications for practitioners on developing custom SCMPs for sustainable and operational performance objectives in the dynamic supply chain context. Future studies should adopt probability sampling methods to improve the generalizability of the findings. Further, the findings should be confirmed by conducting a study on other exporting sectors or geographical areas to gain additional perspectives on the relationships between SCMPs, OP, and ESP. Full article

This study proposes a Residual Neural Network (ResNet) Convolutional Neural Network (CNN) model for predicting the resistance of colorized conductive fabrics (white, red, green, and blue) fabricated through the Single-Walled Carbon Nanotube dip-coating process using a non-contact image analysis approach. The Analysis of Variance (ANOVA) resulted in a p-value of 2.48426 × 10⁻⁸, confirming a statistically significant relationship between the brightness and resistance of conductive fabrics. Histogram equalization preprocessing was applied to enhance the efficiency of model training. The ResNet model achieved an RMSE of 0.0622 and a coefficient of determination of 0.941585, demonstrating approximately a 58% improvement in performance compared to the baseline CNN. The non-contact resistance evaluation method proposed in this study opens new possibilities for the development of wearable electronic devices and smart textiles, offering a foundational approach for real-time process monitoring and automated quality control in manufacturing. Full article

Graphene-based textile pressure sensors are emerging as promising candidates for wearable sensing applications due to their high sensitivity, mechanical flexibility, and low energy consumption. This study investigates the design, fabrication, and electromechanical behaviour of graphene-coated nonwoven textile-based piezoresistive pressure sensors, focusing on the impact of different electrode materials and fabrication techniques. Three distinct sensor fabrication methods—drop casting, electrospinning, and electro-spraying—were employed to impregnate graphene onto nonwoven textile substrates, with silver-coated textile electrodes integrated to enhance conductivity. The fabricated sensors were characterised for their morphology (SEM), chemical composition (FTIR), and electromechanical response under cyclic compressive loading. The results indicate that the drop-cast sensors exhibited the lowest initial resistance (~0.15 kΩ) and highest sensitivity (10.5 kPa⁻¹) due to their higher graphene content and superior electrical connectivity. Electro-spun and electro-sprayed sensors demonstrated increased porosity and greater resistance fluctuations, highlighting the role of fabrication methods in sensor performance. Additionally, the silver-coated knitted electrodes provided the most stable electrical response, while spun-bonded and powder-bonded nonwoven electrodes exhibited higher hysteresis and resistance drift. These findings offer valuable insights into the optimisation of graphene-based textile pressure sensors for wearable health monitoring and smart textile applications, paving the way for scalable, low-power sensing solutions. Full article

The aim of the research is to investigate the impact of laser operation parameters on the LIG (laser-induced graphene) process. It focuses on evaluating the feasibility of using the induced conductive layers to create antenna circuits that are dedicated to radio-frequency identification (RFID) technology. Given the specific design of textile RFIDtex transponders, applying the LIG technique to fabricate antenna modules on a flexible substrate (e.g., Kapton) opens new possibilities for integrating RFID labels with modern materials and products. The paper analyses the efficiency of energy and data transmission in the proposed innovative UHF RFID_LIG tags. The signal strength, read range, and effectiveness are estimated in the experimental setup, providing key insights into the performance of the devices. Based on the obtained results, it can be concluded that changes in laser cutting parameters, the size of the induced graphene layer, and the method of fixing the Kapton substrate significantly affect the quality of the cutting/engraving components and the conductivity of burned paths. However, these changes do not directly affect the correct operation of the RFID_LIG transponders, owing to the fact that these structures are resistant to external impacts. Nevertheless, an increased range of data readout from the RFID_LIG tags can be achieved by using graphene paths with higher conductivity. The obtained results confirm the validity of the proposed concept and provide a foundation for further research on adapting the LIG method to automated logistics, ultimately leading to the development of more versatile and innovative solutions for identification processes. Full article

The textile industry is among the most environmentally impactful sectors, underscoring the urgency of transitioning to circular economy models. This study examines the development of a digital-driven circular ecosystem for the Italian textile sector, leveraging insights from a comprehensive survey on sustainable practices. By analyzing material flows and identifying inefficiencies, the research proposes an integrated framework to enhance sustainability across the supply chain. Key indicators are established to monitor environmental, economic, and social impacts, offering a practical tool for decision-making. The findings reveal how scenario-based approaches and targeted strategies can empower Italian textile companies to adopt circular practices effectively. This work lays the groundwork for a robust ecological transition, fostering both environmental sustainability and competitive advantage in the global market. Full article