Search Results (1,639)

Poly(trimethylene terephthalate) (PTT) is a thermoplastic polyester with a unique structure due to having three methylene groups in the glycol unit. PTT competes with poly(ethylene terephthalate) (PET) and poly(butylene terephthalate) (PBT) in carpets, textiles, and thermoplastic materials, primarily due to the development of economically efficient synthesis methods. PTT is widely utilized in textiles, carpets, and engineering plastics because of its advantageous properties, including quick-drying capabilities and wrinkle resistance. However, its low melting point, resistance to chemicals, and brittleness compared to PET, have limited its applications. To address some of these limitations for targeted applications, PTT nanocomposites incorporating clay, carbon nanotube, silica, and ZnO have been developed. The distribution of nanoparticles within the PTT matrix remains a significant challenge for its potential applications. Several techniques, including sol–gel blending, melt blending, in situ polymerization, and in situ forming methods have been developed to obtain better dispersion. This review discusses advancements in the synthesis of various PTT nanocomposites and the effects of nanoparticles on the isothermal and nonisothermal crystallization of PTT. Full article

The problem of spreading harmful infections through contaminated surfaces has become more acute during the recent coronavirus pandemic. The design of self-cleaning materials, which can continuously decompose biological contaminants, is an urgent task for environmental protection and human health care. In this study, the surface of blended cotton/polyester fabric was functionalized with N-doped TiO₂ (TiO₂-N) nanoparticles using titanium(IV) isopropoxide as a binder to form durable photoactive coating and additionally decorated with Cu species to promote its self-cleaning properties. The photocatalytic ability of the material with photoactive coating was investigated in oxidation of acetone vapor, degradation of deoxyribonucleic acid (DNA) fragments of various lengths, and inactivation of PA136 bacteriophage virus and Candida albicans fungi under visible light and ultraviolet A (UVA) radiation. The kinetic aspects of inactivation and degradation processes were studied using the methods of infrared (IR) spectroscopy, polymerase chain reaction (PCR), double-layer plaque assay, and ten-fold dilution. The results of experiments showed that the textile fabric modified with TiO₂-N photocatalyst exhibited photoinduced self-cleaning properties and provided efficient degradation of all studied contaminants under exposure to both UVA and visible light. Additional modification of the material with Cu species substantially improved its self-cleaning properties, even in the absence of light. Full article

High-performance waterproof and breathable nanofiber membranes (WBNMs) are in great demand for various advanced applications. However, the fabrication of such membranes often relies on fluorinated materials or involves complex preparation processes, limiting their practical use. In this study, we present an innovative approach by utilizing silicon-containing polyurethane (SiPU) as a single-component, fluorine-free raw material to prepare high-performance WBNMs via a simple one-step electrospinning process. The electrospinning technique enables the formation of SiPU nanofibrous membranes with a small maximum pore size (d_max) and high porosity, while the intrinsic hydrophobicity of SiPU imparts excellent water-repellent characteristics to the membranes. As a result, the single-component SiPU WBNM exhibits superior waterproofness and breathability, with a hydrostatic pressure of 52 kPa and a water vapor transmission rate (WVTR) of 5798 g m⁻² d⁻¹. Moreover, the optimized SiPU-14 WBNM demonstrates outstanding mechanical properties, including a tensile strength of 6.15 MPa and an elongation at break of 98.80%. These findings indicate that the single-component SiPU-14 WBNMs not only achieve excellent waterproof and breathable performance but also possess robust mechanical strength, thereby enhancing the comfort and expanding the potential applications of protective textiles, such as outdoor apparel and car seats. Full article

The identification of unmarked graves is important in archaeology, forensics, and cemetery management, but invasive methods are often restricted due to ethical or cultural concerns. This necessitates the use of non-invasive geophysical techniques. Our study demonstrates the potential of induced polarization (IP) imaging as a non-invasive remote sensing technique specifically suited for detecting and characterizing unmarked graves. IP leverages changes in the electrical properties of soil and pore water, influenced by the accumulation of organic matter from decomposition processes. Measurements were conducted at an inactive cemetery using non-invasive textile electrodes to map a documented grave from the early 1990s, with a survey design optimized for high spatial resolution. The results reveal a distinct polarizable anomaly at a 0.75–1.0 m depth with phase shifts exceeding 12 mrad, attributed to organic carbon from wooden burial boxes, and a plume-shaped conductive anomaly indicating the migration of dissolved organic matter. While electrical conductivity alone yielded diffuse grave boundaries, the polarization response sharply delineated the grave, aligning with photographic documentation. These findings underscore the value of IP imaging as a non-invasive, data-driven approach for the accurate localization and characterization of graves. The methodology presented here offers a promising new tool for archaeological prospection and forensic search operations, expanding the geophysical toolkit available for remote sensing in culturally and legally sensitive contexts. Full article

This study presents an advanced framework for fabric texture classification by leveraging macro- and micro-texture extraction techniques integrated with deep learning architectures. Co-occurrence histograms, local binary patterns (LBPs), and albedo-dependent feature maps were employed to comprehensively capture the surface properties of fabrics. A late fusion approach was applied using four state-of-the-art convolutional neural networks (CNNs): InceptionV3, ResNet50_V2, DenseNet, and VGG-19. Excellent results were obtained, with the ResNet50_V2 achieving a precision of 0.929, recall of 0.914, and F1 score of 0.913. Notably, the integration of multimodal inputs allowed the models to effectively distinguish challenging fabric types, such as cotton–polyester and satin–silk pairs, which exhibit overlapping texture characteristics. This research not only enhances the accuracy of textile classification but also provides a robust methodology for material analysis, with significant implications for industrial applications in fashion, quality control, and robotics. Full article

The textile industry’s reliance on synthetic dyes contributes significantly to pollution, highlighting the need for sustainable alternatives like biopigments. This study investigates the production and application of the biopigment prodigiosin, which was produced by Pseudomonas putida with a yield of 1.85 g/L. Prodigiosin was prepared under acidic, neutral, and alkaline conditions, resulting in varying protonation states that influenced its affinity for cotton and polyester fibers. Three surfactants (anionic, cationic, non-ionic) were tested, with non-ionic Tween 80 yielding a promising color strength (above 4) and fastness results with neutral prodigiosin at 1.3 g/L. Cotton and polyester demonstrated good washing (color difference up to 14 for cotton, 5 for polyester) and light fastness (up to 15 for cotton, 16 for polyester). Cellulose acetate, used in the conventional printing process as a thickener, produced superior color properties compared to commercial thickeners. Neutral prodigiosin achieved higher color strength, and cotton fabrics displayed halochromic properties, distinguishing them from polyester, which showed excellent fastness. Prodigiosin-printed samples also exhibited strong antimicrobial activity against Pseudomonas aeruginosa and retained halochromic properties over 10 pH cycles. These findings suggest prodigiosin as a sustainable dye alternative and pH sensor, with potential applications in biomedical materials, such as antimicrobial and pH-responsive wound dressings. Full article

Wool has distinctive biological, physical, and chemical properties that contribute to its value both for the sheep and in global fibre and textile markets. Its fibres are primarily composed of proteins, principally keratin and keratin-associated proteins (KAPs). To better comprehend the genes that underpin key wool traits, this study examined the keratin-associated protein 36-1 gene (KRTAP36-1) in Chinese Tan lambs. We identified three previously reported alleles of the gene (named A, B and C) that were present in the lambs studied, with genotype frequencies as follows: 2.0% (n = 5; AA), 6.9% (n = 17; AB), 13.8% (n = 34; AC), 8.9% (n = 22; BB), 33.4% (n = 82; BC) and 35.0% (n = 86; CC). The frequencies of the individual alleles in the Chinese Tan lambs were 12.4%, 29.1% and 58.5% for alleles A, B and C, respectively. The three alleles were in Hardy–Weinberg Equilibrium. In an association analysis, it was revealed that allele C was associated with variation in the mean fibre curvature of the fine wool of the Chinese Tan lambs, but this association was not observed in their heterotypic hair fibres. This finding suggests that KRTAP36-1 might be differentially expressed in the wool follicles that produce the two fibre types, and that along with other KRTAP genes, it may be involved in determining fibre curvature and the distinctive curly coat of the lambs. Full article

The abrasion resistance of fabrics is one of the basic properties determining the utility performance and durability. The abrasion resistance of textile materials is measured using the Martindale device according to appropriate standards. The sample breakage method is the most commonly used of the three methods. The method is based on organoleptic assessment of fabric breakage. The method is time-consuming, and results may be subject to error resulting from the subjective nature of the assessment. The aim of the presented work was to check the possibility of the application of contactless 3D surface geometry measurement using an optical profilometer in an assessment of changes in fabrics’ surface due to the abrasion process. The obtained results confirmed that some parameters of the geometric structure of fabric surfaces, such as the highest height of the roughness profile Rz, the height of the highest pick of the roughness profile Rp, the depth of the lowest valley of the roughness profile Rv, the depth of the total height of the roughness profile Rt, and the kurtosis Rku, can be used to assess the abrasion resistance of fabrics. It is also stated that using the non-contact optical measurement of fabric surface geometry allows for an assessment of the directionality of surface texture. For this purpose, the autocorrelation function and angle distribution function can be applied. Full article

Rubber–textile conveyor belts are an important element of large-scale transport systems, which in many cases are subjected to excessive dynamic loads. Assessing the impact resistance of them is essential for ensuring their reliability and longevity. The article focuses on the use of machine learning methods as one of the approaches to the analysis and prediction of the impact resistance of rubber–textile conveyor belts. Based on the data obtained from the design properties of conveyor belts and experimental testing conditions, four models were created (regression model, decision tree regression model, random forest model, ANN model), which are used to analyze and predict the impact force of the force acting on the conveyor belt during material impact. Each model was trained on training data and validated on test data. The performance of each model was evaluated using standard metrics and model indicators. The results of the model analysis show that the most powerful model, ANN, explains up to 99.6% of the data variability. The second-best model is the random forest model and then the regression model. The least suitable choice for predicting the impact force is the regression tree. Full article

This study investigates the influence of weaving process parameters on the structural homogeneity of woven fabrics, with a focus on the structural autoregulation phenomenon. Two experimental fabric groups of 30 each, plain and twill weaves, were produced using varied loom settings: shed closure timing, lease rod position, backrest roller position, warp pre-tension, and yarn twist direction. Structural uniformity was assessed using a proprietary method and the MagFABRIC 2.1. image analysis system, which quantify intra-repeat, inter-repeat, and global inhomogeneity. This method uses the size, shape, and location of inter-thread pores as well as warp and weft pitches. The results indicate that autoregulation can reduce local structural disturbances, including warp yarn grouping. In plain weaves, loom parameters and humidity significantly contributed to structural autoregulation. In contrast, twill weaves demonstrated dominant internal feedback mechanisms, significantly influenced by yarn twist direction. Regression models at F = 10 revealed nonlinear interactions, confirming autoregulation and experimentally supporting Nosek’s quasi-dynamic theory for these types of fabrics. The results of these studies have practical relevance in high-performance textiles such as filtration, barrier fabrics, and composite reinforcements, where local structural deviations critically affect the functional properties of fabrics. Full article

Moringa oleifera (MO), a nutritionally and pharmacologically potent species, is emerging as a sustainable candidate for applications across bioenergy, agriculture, textiles, pharmaceuticals, and biomedicine. This review explores recent advances in MO-based biotechnologies, highlighting novel extraction methods, green nanotechnology, and clinical trial findings. Although MO’s resilience offers promise for climate-smart agriculture and public health, challenges remain in standardizing cultivation and verifying therapeutic claims. This work underscores MO’s translational potential and the need for integrative, interdisciplinary research. MO is used in advanced materials, like electrospun fibers and biopolymers, showing filtration, antibacterial, anti-inflammatory, and antioxidant properties—important for the biomedical industry and environmental remediation. In textiles, it serves as an eco-friendly alternative for wastewater treatment and yarn sizing. Biotechnological advancements, such as genome sequencing and in vitro culture, enhance traits and metabolite production. MO supports green biotechnology through sustainable agriculture, nanomaterials, and biocomposites. MO shows potential for disease management, immune support, metabolic health, and dental care, but requires further clinical trials for validation. Its resilience is suitable for land restoration and food security in arid areas. AI and deep learning enhance Moringa breeding, allowing for faster, cost-effective development of improved varieties. MO’s diverse applications establish it as a key element for sustainable development in arid regions. Full article

The excellent moisture transport properties of yarns play a crucial role in improving the liquid moisture transfer behavior within textiles and maintaining their thermal-wet comfort. However, the current research on the moisture management performance of fabrics made from yarns with excellent liquid transport properties primarily compares the wicking results, without considering the varying requirements of testing conditions due to differences in human sweating rates during daily activities. Moreover, the understanding of moisture transport mechanisms in yarns within fabrics under different testing conditions remains insufficient. In this study, two types of twisted combination yarns, composed of hydrophobic profiled polyester filaments and hydrophilic spun yarns to form a hydrophobic-hydrophilic gradient along the axial direction of the yarn, were developed and compared with profiled polyester filaments to understand the liquid migration behaviors in the knitted fabrics formed by these yarns. Results showed that hydrophobic profiled polyester filament yarn demonstrated superior liquid transport performance with infinite saturated liquid supply (vertical wicking test). In contrast, the twisted combination yarns exhibited better moisture diffusion properties under limited liquid droplet supply conditions (droplet diffusion test and moisture management test). These contradictory findings indicated that the amount of liquid moisture supply in testing conditions significantly affected the moisture transport performance of yarns within fabrics. It was revealed that the liquid moisture in the twisted combination yarns migrated through capillary wicking for moisture transfer. Under an infinite saturated liquid supply condition, the higher the content of hydrophilic fibers in the spun yarns, the greater the amount of moisture transferred, demonstrating an excellent liquid transport performance. Under the limited liquid droplet supply conditions, both the volume of liquid water and the moisture absorption capacity of the yarn jointly influence internal moisture migration within the yarn. It provided a theoretical reference for testing the internal moisture wicking performance of fabrics under different states of human sweating. Full article

The escalating global demand for fresh water, driven by urbanization and industrial growth, underscores the need for sustainable water management, particularly in the water-intensive construction sector. Although prior studies have primarily concentrated on treated wastewater, the practical viability of utilizing untreated wastewater has not been thoroughly investigated—especially in developing nations where treatment expenses frequently impede actual implementation, even for non-structural uses. While prior research has focused on treated wastewater, the potential of untreated or partially treated wastewater from diverse industrial sources remains underexplored. This study investigates the feasibility of incorporating wastewater from textile, sugar mill, service station, sewage, and fertilizer industries into concrete paver block production. The novelty lies in a dual approach, combining experimental analysis with XGBoost-based machine learning (ML) models to predict the impact of key physicochemical parameters—such as Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), and Hardness—on mechanical properties like compressive strength (CS), water absorption (WA), ultrasonic pulse velocity (UPV), and dynamic modulus of elasticity (DME). The ML models showed high predictive accuracy for CS (R² = 0.92) and UPV (R² = 0.97 direct, 0.99 indirect), aligning closely with experimental data. Notably, concrete pavers produced with textile (CP-TXW) and sugar mill wastewater (CP-SUW) attained 28-day compressive strengths of 47.95 MPa and exceeding 48 MPa, respectively, conforming to ASTM C936 standards and demonstrating the potential to substitute fresh water for non-structural applications. These findings demonstrate the viability of using untreated wastewater in concrete production with minimal treatment, offering a cost-effective, sustainable solution that reduces fresh water dependency while supporting environmentally responsible construction practices aligned with SDG 6 (Clean Water and Sanitation) and SDG 12 (Responsible Consumption and Production). Additionally, the model serves as a practical screening tool for identifying and prioritizing viable wastewater sources in concrete production, complementing mandatory laboratory testing in industrial applications. 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

Cellulose nanofiber (CNF) treatments can enhance the structure and performance of regenerated cellulose fibers. This study investigates the effects of CNF treatment on the mechanical properties, water absorption behavior, and humidity dependence of regenerated cellulose fibers. Tensile testing demonstrated that CNF-treated fibers exhibit improved elasticity and reduced swelling in aqueous environments. Scanning electron microscopy revealed the adsorption of CNF components onto the fiber surfaces. Microbeam X-ray diffraction indicated structural differences between untreated and CNF-treated fibers, with the latter containing cellulose I crystals. Small-angle X-ray scattering revealed alterations in the internal fibrillar structure due to CNF treatment. FT-IR spectroscopy highlighted humidity-dependent variations in molecular vibrations, with peak intensities increasing under higher humidity conditions. Additionally, CNF treatment inhibited water absorption in high-humidity conditions, contributing to reduced expansion rates and increased elastic modulus during water absorption. Overall, CNF treatment enhanced both the mechanical strength and water resistance of regenerated cellulose fibers, making them suitable for advanced textile applications. This study provides valuable insights into the role of CNF-treated fibers in improving the durability and functional performance of regenerated cellulose-based textile. Full article