Search Results (564)

Developing sustainable textile finishes that enhance moisture management and breathability remains a significant challenge in designing high-performance apparel. In this study, we propose an eco-friendly coating strategy utilizing an aqueous dispersion of poly(3-hydroxybutyrate)-diol (PHB.E.0), a member of the polyhydroxyalkanoate (PHA) family. This coating was applied to woven polyester (PES) and cotton (CO) fabrics using a low-impact spray-coating technique, aiming to improve functional properties while maintaining environmental sustainability. This solvent-free process significantly reduces chemical usage and energy demand, aligning with sustainable manufacturing goals. Successful deposition of the coating was confirmed by scanning electron microscopy (SEM), attenuated total reflectance Fourier-transform infrared spectroscopy (ATR-FTIR), elemental (C/O) analysis, and thermogravimetric analysis (TGA), which also revealed substrate-dependent thermal behaviour. Wettability, water absorption, and permeability tests showed that the coated fabrics retained their hydrophilic character. PHB.E.0 coatings led to a significant reduction in air permeability, particularly after hot pressing at 180 °C, from ≈670 to ≈171 L·m⁻² s⁻¹ for PES and from ≈50 to ≈30 L·m⁻²·s⁻¹ for CO, without compromising water vapor permeability. All coated samples maintained high breathability, essential for wearer comfort. These results demonstrate that PHB.E.0 coatings enhance wind resistance while preserving moisture vapor transport, offering a sustainable and effective solution for functional sportswear. Full article

Azo dyes are widely used in the textile industry due to their vibrant colors and chemical stability; however, wastewater containing these dyes poses significant environmental and health risks due to their toxic, persistent, and potentially carcinogenic properties. In this study, the treatment of wastewater containing trypan blue dye was investigated using the electrooxidation process with boron-doped diamond electrodes, and the efficiency of the process was modeled through the Extreme Gradient Boosting (XGBoost) algorithm. In the experimental phase, the effects of key operational parameters, including current density, pH, electrolysis time, and supporting electrolyte concentration, on TB dye removal efficiency were systematically evaluated. Based on the experimental data obtained, a machine learning-based XGBoost prediction model was developed, and hyperparameter optimization was performed to enhance its predictive performance. The model achieved high accuracy (R² = 0.996 for training and 0.954 for testing) and yielded low error metrics (RMSE and MAE), confirming its reliability in predicting removal efficiency. This study presents an integrated and data-driven approach for improving the efficiency and sustainability of electrooxidation processes and offers an environmentally friendly and effective method for the treatment of azo dye-contaminated wastewater. Full article

Ethnographic collections often incorporate composite objects consisting of various materials, including wood, textiles and metals. These objects are vulnerable to deterioration when iron fastenings corrode under humid environments, and their removal is therefore essential for the long-term preservation of artifacts. This study investigates the efficacy of the chelating agents Desferrioxamine B (DFO-B) and ethylenediaminetetraacetic acid (EDTA), applied in different gel formulations, in cleaning wooden and textile mock-ups stained with iron corrosion products. Three gel types were explored: xanthan gum, agarose and Nanorestore extra-dry gel with medium water retention (nano-MWR). The results indicated that xanthan gum exhibited the highest cleaning effectiveness but posed risks of residue deposition and surface damage due to the required clearance process. Agarose and nano-MWR gels proved to be less effective but showed potential for achieving high chelator efficacy with repeated applications. Agarose enhanced the chelators’ efficacy on textiles, while nano-MWR gel performed better on even wooden surfaces. No chemical damage was detected for either substrate across gel applications. The study concludes that a single gel formulation does not achieve equivalent cleaning efficacy on the two substrates of composite objects with a defined number of applications. Agarose in a semi-rigid state enhances the efficacy of textile treatment and may achieve comparable results on wood after repeated applications. Alternatively, a combined approach using agarose for textiles and nano-MWR gel for wood may optimize chelator performance on composite artifacts. Full article

The increased consumption of apparel has resulted in a corresponding increase in the volume of waste textiles, with polyester–cotton blended textiles accounting for as much as 80% of the total. However, extant recycling methodologies are beset by challenges, including high cost and difficulty in separation. Mycelium has been shown to possess the ability to degrade complex components in culture substrates. The present study explores the feasibility of using polyester–cotton yarn as a substrate for mycelium composite materials, thus offering an innovative approach to the treatment of waste blended textiles. Five mycelium composite materials with varying polyester–cotton ratios were prepared and tested for mechanical strength, moisture resistance, and biodegradability. ANOVA analysis confirmed that all properties of the mycelium composites were significantly influenced by the polyester–cotton matrix ratio, with partial eta-squared (ηp²) exceeding 84% across all properties. The most significant effect was observed in compressive strength (ηp² > 99%). Experiments identified a 65:35 polyester–cotton ratio as yielding optimal comprehensive properties: namely, a compressive strength of 0.221 MPa and flexural strength of 0.791 MPa, coupled with excellent moisture resistance and biodegradability. This provides data support for the development of textile-based mycelium composite products. In light of the aforementioned performance studies and material characteristics, the development of three series of experiential home products was undertaken. Design evaluations were conducted to explore the potential application of mycelium composites, which could have significant implications for promoting sustainable development in the textile and apparel industry and advancing innovative designs for mycelium composite materials. Full article

Noise has detrimental effects on mental and physical health and quality of life, especially for those living in apartment buildings. Therefore, sound insulation materials are pivotal for reducing unwanted noise as well as enhancing acoustic comfort. This study offers a hybrid approach for analyzing 3D woven textile sound insulation material effectiveness, especially in residential buildings, by simulating airborne sound insulation and testing manufactured slab samples with 3D woven textile mortars in a laboratory using a tapping machine. At the same time, the JCA model and the transfer matrix method are employed to calibrate sound absorption coefficients (SAC) and simulate its airborne sound insulation effect in buildings in Seoul, South Korea. Results indicate that the maximum mean sound pressure level (SPL) of the 3D woven textile was reduced up to 9 dB in the octave band frequencies. The thickness improvement of 3D woven textiles enhances the mid- and high-frequency sound absorption effect, most pronounced in 3D woven textiles made of double-layer (DSRM) material, which demonstrated an air sound insulation efficiency around 28.5% greater than that of traditional materials. The maximum drop in impact sound pressure level (SPL) at 2 kHz is 13 dB. The study also proposes a strategy to optimize sound insulation performance, which is used as an effective solution for noise control in buildings. These findings lay the groundwork for research on the application of 3D woven textiles for sound insulation in residential buildings and offer prospects for sustainable textile composites in architectural building applications. Full article

In response to the increasing demand for environmentally friendly and cost-effective adsorbents in wastewater treatment, this study reports the green synthesis, characterization, and application of a magnetic epichlorohydrin Rosa canina (m-ECH-RC) nanocomposite for removing Safranin O (SO), a commonly used cationic dye in textile effluents. The synthesized material was characterized using Brunauer–Emmett–Teller (BET), Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), X-ray diffraction (XRD), and zeta potential analyses to reveal its surface morphology, pore structure, functional groups, crystallinity, and colloidal stability. Adsorption performance was systematically tested under various conditions, including pH, adsorbent dose, contact time, ionic strength, and initial dye concentration. Kinetic analyses revealed that the adsorption process of Safranin O dye mainly obeys pseudo-second-order kinetics, but intraparticle and film diffusion also contribute to the process. As a result of the Isotherm analysis, it was found that the adsorption process conformed to the Langmuir model. Testing on real textile wastewater samples demonstrated a removal efficiency of 75.09% under optimized conditions. Reusability experiments further revealed that the material maintained high adsorption–desorption performance for up to five cycles, emphasizing its potential for practical use. These findings suggest that m-ECH-RC is a viable and sustainable adsorbent for treating dye-laden industrial effluents. Full article

The discharge of synthetic dyes into aquatic ecosystems stands as a pointed environmental concern, with serious consequences affecting not only biodiversity and water quality but also human health. To address this challenge, this study introduces a natural Tunisian chert, a silica-rich sedimentary rock, as a promising, sustainable, and low-cost adsorbent for treating textile dye-polluted wastewater. For the first time, the adsorption capabilities of a Tunisian chert were systematically evaluated for both cationic (Methylene Blue; MB and Cationic Yellow 28; CY28) and anionic dyes (Eriochrome Black T; EBT). To assess the impacts of key operational parameters, such as pH (2–12), contact time (0–240 min), adsorbent dosage (0.02–0.25 g), and initial dye concentration (50–500 mg/L), batch mode adsorption trials were performed. The Langmuir isotherm model most accurately fits the adsorption data, yielding a maximum adsorption capacity of 138.88 mg/g for MB, 69.93 mg/g for CY28, and 119.04 mg/g for EBT, outperforming multiple conventional adsorbents. Kinetic modeling revealed that adsorption adhered to a pseudo-second-order model, with rapid equilibrium within 45–60 min, highlighting the efficiency of the Tunisian chert. Optimal dye removal was obtained at pH = 8 for cationic dyes and pH = 4 for EBT, driven by electrostatic interactions and surface charge dynamics. The current research work reveals that Tunisian chert is a low-cost and efficient adsorbent with a high potential serving for large-scale industrial applications in wastewater treatment. Using a locally abundant natural resource, this work provides a maintainable and economical approach for dye removal from polluted wastewater. Full article

The Necrópolis de Wari Kayan, at the Paracas site in the coastal desert of south–central Peru, is a large archeologically excavated mortuary complex with fine textile preservation, dated approximately to 2000 BP. This study investigates loose yarns associated with textiles from Wari Kayan tomb 12 (bundle 382), collected by the late Dr. Anne Paul in 1985 at what is now the Museo Nacional de Arqueología Antropología e Historia del Perú (MNAAHP). Sequencing multiple state-of-the-art analyses, including direct analysis in real time mass spectrometry (DART-MS), high performance liquid chromatography (HPLC) with diode array detection, and accelerator mass spectrometry, on the same small sample, seeks to “squeeze out every drop” of information. Six mantles from the outer layer include different sets of color hues and values, representing either different time periods or different producer groups. Plasma oxidation at low temperature (<100 °C) prepared carbon dioxide for AMS radiocarbon analysis. Fibers remaining after oxidation were combusted for light-stable isotope analysis. The sequential analysis results in fiber and dye composition, radiocarbon age, and stable isotope fractionation values may suggest fiber origin, continuing and updating a project started over 40 years ago. Full article

The increasing environmental concerns surrounding plastic waste have intensified recycling efforts, particularly in the textile industry, where poly(ethylene terephthalate) (PET) is widely used for sustainable material production. The growing use of recycled PET (rPET) in textiles has prompted the need for reliable analytical methods to detect and quantify rPET content. This study differentiates between virgin and recycled PET by simulating mechanical recycling through five reprocessing cycles of three distinct PET grades, assessing changes in crystalline structure, intrinsic viscosity, molecular weight, and specific degradation markers. Differential Scanning Calorimetry revealed bimodal melting behaviour in reprocessed samples, while intrinsic viscosity and Gel Permeation Chromatography indicated molecular degradation. Notably, the release of dimethyl terephthalate (DMT) and dimethyl isophthalate (DMiP) was consistently observed as a function of degradation. These markers were identified and quantified using High-Performance Liquid Chromatography (HPLC) and Gas Chromatography (GC), with GC offering higher sensitivity and lower matrix interference. This study demonstrates that DMT and DMiP are robust chemical indicators of PET degradation and recycled content. This analytical approach, combining thermal, rheological, and chromatographic techniques, provides a scientifically sound and potentially cost-effective basis for traceability systems, certification protocols, and regulatory compliance in sustainable textile production. Full article

Pectins are high-value plant cell-wall polysaccharides with extensive applications in the food, pharmaceutical, textile, paper, and environmental sectors. Traditional extraction and processing methodologies rely heavily on harsh acids, high temperatures, and non-renewable solvents, generating substantial environmental and economic costs. This review consolidates recent advances across the entire Bacillus–pectinase value chain, from green pectin extraction and upstream substrate characterization, through process and statistical optimization of enzyme production, to industrial biocatalysis applications. We propose a practical roadmap for developing high-efficiency, low-environmental-footprint enzyme systems that support circular bioeconomy objectives. Critical evaluation of optimization strategies, including submerged versus solid-state fermentation, response surface methodology, artificial neural networks, and design of experiments, is supported by comparative data on strain performance, fermentation parameters, and industrial titers. Sector-specific case studies demonstrate the efficacy of Bacillus pectinases in fruit-juice clarification, textile bio-scouring, paper bio-bleaching, bio-based detergents, coffee and tea processing, oil extraction, animal feed enhancement, wastewater treatment, and plant-virus purification. Remaining challenges, including enzyme stability in complex matrices, techno-economic scale-up, and structure-guided protein engineering, are identified. Future directions are charted toward CRISPR-driven enzyme design and fully integrated circular-economy bioprocessing platforms. Full article

The use phase of clothing contributes significantly to the overall environmental impacts due to clothing care practices. Decreasing environmental impact while maintaining washing performance in the use phase can be an effective strategy for sustainability and circularity in the textile value chain. However, existing studies on the environmental impacts of use phase usually consider limited washing conditions and neglect their impacts on washing efficiency. This study proposes a research framework that integrates the Response Surface Methodology (RSM) and Life Cycle Assessment (LCA) methodology to optimize washing parameters for better washing efficiency with less environmental impact in the clothing use phase. A series of laundry experiments were conducted to simulate household laundry, and an environmental impact assessment was conducted based on the experimental data. The optimized washing parameters were explored under eight impact categories and in terms of washing efficiency, and comparative analyses were conducted between three different washing scenarios. The results indicated that input load is the most significant factor influencing both washing efficiency and environmental impact, but with a negative correlation. The optimized washing conditions provided effective trade-offs, demonstrating notable environmental benefits through the scenario study. In the daily washing scenario with an expectation for a middle level of washing efficiency, using the optimized washing conditions can reduce the environmental impact by 80% on average compared to the high-washing-efficiency-oriented washing process and 60% on average compared to the low-environmental-impact-oriented washing process. However, for high washing efficiency demand, optimized washing conditions are less competitive due to increased washing time and detergent use. The results emphasized the importance of choosing appropriate washing parameters according to the demand for washing efficiency. Consistent environmental improvements can be achieved by changing consumer washing habits. Full article

This paper proposes a hybrid predictive maintenance framework that combines the discriminative power of XGBoost with the interpretability of a Bayesian Network automatically learned from sensor data. Targeted at textile manufacturing equipment operating under Industry 4.0 conditions, the system addresses the trade-off between early fault detection and decision transparency. Sensor data, including vibration, temperature, and electric current, were collected from a multi-needle quilting machine using a custom IoT-based platform. A degradation-aware labeling scheme was implemented using historical maintenance logs to assign semantic labels to sensor readings. A Bayesian Network structure was learned from this data via a Hill Climbing algorithm optimized with the Bayesian Information Criterion, capturing interpretable causal dependencies. In parallel, an XGBoost model was trained to improve classification accuracy for incipient faults. Experimental results demonstrate that XGBoost achieved an F1-score of 0.967 on the high-degradation class, outperforming the Bayesian model in raw accuracy. However, the Bayesian Network provided transparent probabilistic reasoning and root cause explanation capabilities—essential for operator trust and human-in-the-loop diagnostics. The integration of both models yields a robust and interpretable solution for predictive maintenance, enabling early alerts, visual diagnostics, and scalable deployment. The proposed architecture is validated in a real production line and demonstrates the practical value of hybrid AI systems in bridging performance and interpretability for predictive maintenance in Industry 4.0 environments. Full article

A swellable magnetic polymer with high removal capacity was produced. The copolymer consisting of acrylamide and 2,4,6-triallyloxy-1,3,5-triazine was synthesized via the radical polymerization method. Previously prepared magnetic Fe₃O₄ particles with the co-precipitation method were added during the synthesis, and then the obtained composite was hydrolyzed. The composite became a swellable hydrogel after hydrolysis. The synthesized magnetic composite hydrogel polymer was used for Malachite Green (MG) and Acid Violet 19 (AV19) binary textile dye mixture removal. A derivative method was developed to calculate the individual concentration of dyes in mixture solutions. The accuracy and precision of the developed method were examined by calculating the recovery percentage (R%) and relative standard deviation (RSD%). The highest removal percentages (~99% for MG and ~100% for AV19) were achieved at the dye mixture’s natural pH (pH 4). Antibacterial tests were examined against Gram-negative and Gram-positive bacteria, and the synthesized composite hydrogel polymer showed higher activity. The FTIR, XRD, SEM, and EDS analyses were also performed to characterize the synthesized materials. Full article

The wastewater discharged from the aquaculture and textile industries often contains toxic organic dyes, such as methylene blue (MB) and malachite green (MG), which pose significant risk to public health and ecosystem stability due to their high chemical stability, bioaccumulation potential and resistance to degradation. To address these challenges, the development of an integrated system capable of both efficient degradation and highly sensitive detection of organic dyes is essential for ecological restoration and early pollution monitoring. Herein, bifunctional Fe₃O₄-Cu₂O-Ag-GO (FCA 2-GO) nanocomposites (NCs) were developed by depositing Cu₂O, Ag nanocrystals and graphene oxide (GO) onto the surfaces of Fe₃O₄ nanocrystals. This multifunctional material acted as both a photocatalyst and a surface-enhanced Raman scattering (SERS) platform, enabling simultaneous degradation and ultrasensitive detection of organic dyes. Under simulated sunlight irradiation, FCA 2-GO NCs achieved over 98% degradation of both MB and MG within 60 min, driven by the synergistic action of reactive oxygen species (·O₂⁻ and ·OH). The degradation kinetics followed pseudo-first-order behavior, with rate constants of 0.0381 min⁻¹ (MB) and 0.0310 min⁻¹ (MG). Additionally, the FCA 2-GO NCs exhibited exceptional SERS performance, achieving detection limits as low as 10⁻¹² M for both dyes, attributed to electromagnetic–chemical dual-enhancement mechanisms. Practical applicability was demonstrated in soil matrices, showcasing robust linear correlations (R² > 0.95) between SERS signal intensity and dye concentration. This work provides a dual-functional platform that combines efficient environmental remediation with trace-level pollutant monitoring, offering a promising strategy for sustainable wastewater treatment and environmental safety. Full article

Agricultural and food by-products offer valuable opportunities for circular and bio-based innovation across sectors. In the textile industry, replacing fossil-based coatings with sustainable alternatives is increasingly urgent. This study evaluates the performance of a textile coating based on coffee silverskin (CS)—an abundant by-product of coffee roasting—applied to four natural fibre substrates: cotton, lyocell, wool, and silk. A formulation combining 60% CS sludge (8% solids), treated by wet ball milling, with an aliphatic polyester-polyurethane dispersion was applied via knife coating. Standardised tests assessed mechanical resistance, air permeability, colour fastness, moisture management, and water repellency, including contact angle and drop absorption analyses. Results revealed that all substrates were compatible with the CS-based coating, which reduced air permeability and increased hydrophobicity. Notably, silk showed the most significant functional enhancement, transitioning from hydrophilic to waterproof with increased durability—indicating strong potential for technical applications such as outerwear and performance textiles. Given the renewable origin of both the substrate and coating, this study highlights the feasibility of valorising agri-food waste in high-performance, bio-based textile systems. These findings demonstrate the potential of CS as a bio-based coating for technical textiles, supporting the development of high-performance and sustainable materials within the textile industry. Full article