Search Results (462)

Recycling cotton–PET textile blends using alkaline solutions has gained increasing attention, with studies showing promising treatment pathways with diverse process setups. However, these separation processes use various input materials and focus on a small number of treatment parameter values which render the comparison of results over a large parameter range difficult. This study presents the feasibility of recovering cotton or PET at fabric level from cotton–PET blends across a wide range of temperatures (from −30 °C to 95 °C) and alkaline concentrations (from 0 to 40% (w/w)). The focus of this study is centered on the share of separation and recoverable fiber mass after hydrolyzing one component using alkaline hydrolysis or alkaline pre-treatment followed by enzymatic hydrolysis. A comparison of purity and material loss of the recovered polymers for all parameter sets is given. Experiments were performed on two distinct textiles while process parameters were selected in a straightforward manner, excluding catalysts, co-solvents and defibration. The results map temperature and alkaline concentration areas where these cotton–PET separation processes are feasible regarding recoverable fiber mass. Based on these results, separation efficiency could be optimized to design economic and environmentally friendlier process conditions. Full article

This article investigates how EU and Member State legislation contributes to advancing circularity in the textile value chain, a priority sector due to its significant environmental impacts and economic relevance. The research aims to address the lack of comprehensive analysis of national legislation supporting textile circularity and to assess its alignment with the evolving EU framework. An inventory and critical analysis of legislative measures adopted at EU and Member State levels, covering all phases of the textile value chain, has been developed, based on review of the literature, screening of European Environment Agency and European Commission reports, and targeted web search. Results show that recent reforms of EU legislation, such as the Ecodesign for Sustainable Products Regulation and the revised Waste Framework Directive, have marked a shift toward a more systemic, lifecycle-oriented regulatory framework promoting textile circularity. Moreover, approximately 130 national policy initiatives and legislative measures exceeding EU requirements have been identified, with legislation focusing especially on the consumption and end-of-life stages and relevant innovation in countries such as France, Belgium, and the Netherlands. However, regulatory gaps remain, particularly regarding consumption, prevention of textile waste, secondary raw materials market, and recycling capacity. The findings also highlight the importance of stronger policy coherence between EU and national legislation. Full article

Efficient textile recycling depends on accurate identification of fibre types and compositions to support high-value material recovery and automated sorting. Existing commercial systems based on near-infrared (NIR) spectroscopy offer robust performance, but their model architectures and development methods are proprietary, and they often struggle to detect materials when carbon-black (graphite-based) dyes suppress the spectral signatures. This paper presents a hyperspectral imaging approach for textile fibre identification, combined with an artificial intelligence model capable of detecting cotton, polyester, elastane, and regions affected by carbon-black dye. Sixty-five textile samples were laboratory-verified to determine constituent materials and compositions, with 52 used in model development and testing. A semi-automatic algorithm detected textile boundaries and sampled 100 spectral patches per image. For materials exhibiting two distinct spectral signatures, typically due to carbon-black dye regions, 100 samples were collected for each signature, producing a database of 6500 spectra. A convolutional neural network model was trained using these signatures to predict fibre composition and identify any regions with carbon-black dye. The system achieved mean absolute errors below 2.1% for cotton, polyester, and elastane. A spatial clustering step groups pixels with similar spectra prior to detection, enabling region-wise material identification and allowing the model to classify clusters likely affected by carbon-black dye. This approach demonstrates high precision in fibre identification and reliable detection of carbon-black regions, highlighting its suitability for real-world textile analysis workflows. Full article

High-value recycling of waste cotton-containing fabrics is crucial for the sustainable development of the textile industry. In this study, cotton pulps with different degrees of polymerization (DP = 512–789) from waste polyester/cotton fabrics are systematically evaluated for viscose fiber production. The insolubles in the spinning solution and the effects of DP on its rheological behavior are examined. Based on the mechanical properties of the prepared viscose fibers, the spinning parameters (draw ratio, coagulation bath temperature, and H₂SO₄ concentration) are optimized. The results show that recycled pulps can produce spinning solutions without insolubles, indicating good spinnability and viscoelastic behavior similar to commercial wood pulp. Higher DP increases apparent viscosity and high-frequency elasticity. Under the optimal spinning conditions (draw ratio 1:1.13, coagulation bath temperature 40 °C, and H₂SO₄ concentration 8%), the viscose fibers prepared from recycled cotton pulp with DP = 789 achieve a dry tenacity of 2.31 cN/dtex, which is 37.5% higher than that of wood pulp-based viscose fibers, and exhibit higher elongation at break. This study provides a basis for quality control and process improvement in producing high-tenacity viscose fibers from recycled cotton pulp, paving the way for high-value recycling of waste cotton-containing fabrics. Full article

Surface stain contamination poses a critical barrier to the automated, high-precision fiber identification required for industrial-scale waste textile recycling. In this study, a dataset comprising 120 physical specimens (yielding 1200 regions of interest, ROIs) across 12 contamination categories was constructed by contaminating cotton, polyester, and poly-cotton blend textiles with carbon black, protein, and oil stains. The spectral interference effects of stains—including baseline drift and spectral overlapping induced by physical shielding and chemical absorption—were systematically analyzed. To identify the optimal classification pipeline, three mathematical preprocessing methods (First Derivative, FD; Standard Normal Variate, SNV; and Multiplicative Scatter Correction, MSC) were evaluated alongside Support Vector Machine (SVM) and One-Dimensional Convolutional Neural Network (1D-CNN) models. Results show that among the SVM-based pipelines, the FD-SVM model effectively resolves overlapping absorption peaks, achieved an average accuracy of 98.17% ± 1.33%, but remains highly dependent on mathematical preprocessing. In contrast, the 1D-CNN model employing a progressive stacking architecture of multi-scale convolutional kernels attains a highly robust mean accuracy of 99.58% ± 0.56% under a strict specimen-level 10-fold cross-validation. It achieves this by directly utilizing radiometrically calibrated raw spectra, thereby effectively bypassing manual spectral feature engineering. These findings demonstrate that Hyperspectral Imaging coupled with end-to-end deep learning provides a feasible and industrially deployable solution for simultaneous stain detection and fiber identification in waste textile sorting. Full article

The increasing complexity of textile waste, particularly blended fibers, represents a major challenge for conventional recycling approaches. This study proposes a selective valorization strategy for mixed textile waste streams by applying tailored chemical recycling routes to individual fiber type. Preliminary tests identified suitable methodologies for each fiber type: dissolution–precipitation for acrylic (poly(acrylonitrile)—PAN), acidolysis for nylon, glycolysis for polyester (PeS) and acetylation for cotton. Structural characterization confirmed that the incorporation of recycled products did not significantly change the chemical structure or crystallinity of the resulting materials. Furthermore, thermal analysis revealed comparable or slightly improved thermal stability in most recycled systems. Additionally, mechanical performance was observed to vary depending on the polymer type. Recycled acrylic and cellulose acetate showed reduced ductility, while nylon exhibited increased stiffness due to possible recrystallization effects. In contrast, PeS displayed enhanced elongation at break, suggesting increased chain mobility or plasticization effects. Overall, the results demonstrate that selective chemical valorization is a promising route for the efficient recycling of complex textile waste, enabling the recovery of high-quality materials with retained functional properties. Full article

The textile and clothing industry has historically exerted a significant negative impact on the environment. Excessive water consumption, chemical pollution, and soil degradation are just a few of the pressing environmental concerns linked to this sector. Addressing these issues has become a priority not only for regulatory bodies, at the National and European levels, but also for the industry itself. More recently, growing attention has turned to reducing the huge volume of waste generated by consumers’ unbridled purchase of clothing. In this context, the Circular Economy (CE) and the Digital Product Passport (DPP) have emerged as complementary approaches for improving product circularity, transparency, and traceability. However, in the textile and clothing sector, their effective implementation also depends on consumer participation in practices such as prolonged use, repair, reuse, and responsible end-of-life management. This article presents EcoProve, a gamified platform designed to encourage consumer engagement with CE practices through smart wardrobe management. The platform allows users to register garments, track usage, record maintenance and repair actions, and document sharing, donation, remaking, and recycling activities. These functionalities aim both to promote more sustainable clothing-related behaviours and to support the structured recording of use phase data relevant to DPP-oriented lifecycle information. This study reports the development and pilot validation of the platform with end users. The results suggest positive effects on environmental awareness, perceived understanding of sustainable textile-related practices, and initial self-reported changes in habits associated with clothing use and disposal. The findings support the potential of gamified digital platforms to foster consumer participation in CE systems in the textile and clothing sector while also indicating the need for broader and longer-term evaluations. Full article

This study examines how catalysts and operating conditions enhance the pyrolysis of textile wastes, supporting their use as a viable feedstock for waste-to-energy recycling. Pyrolysis of three common textile wastes—cotton, polyester, and nylon—was studied using thermogravimetric analysis (TGA). Experiments were conducted at heating rates of 5, 10, 15, and 20 °C/min, both with and without catalysts, including K₂CO₃, ZnO, KOH, CaO, and natural zeolite. The results showed that cotton decomposes at significantly lower temperatures than polyester and nylon, with a peak decomposition rate at 361.7 °C compared to 437.9 °C for polyester and 459.8 °C for nylon. Reaction kinetics were analyzed using three established models: Kissinger–Akahira–Sunose (KAS), Flynn–Wall–Ozawa (FWO), and Kissinger. Among the materials studied, polyester exhibited the lowest activation energy (184.8 kJ/mol), while cotton and nylon showed higher values (241.1 and 236.2 kJ/mol, respectively). Catalyst performance varied by material. Potassium carbonate was particularly effective for cotton, increasing the weight loss rate and reaction rate constant. ZnO significantly reduced the activation energy for nylon. Although catalysts generally enhanced reaction rates, many also increased activation energy. This increase in activation energy and collision frequency suggests that catalytic pyrolysis becomes more temperature-sensitive while achieving higher reaction turnover frequencies. Full article

One of the challenges to sustainability is the management of textile waste. Effective technologies for recycling this type of waste are still lacking. Currently, textile waste is most often landfilled or incinerated. Pyrolysis offers a more advantageous solution, as it enables partial recovery of raw materials along with energy recovery from the remaining mass, thereby aligning with the circular economy. The kinetics of volatile matter release play an important role in the pyrolysis and combustion of solid substances. This paper presents research related to this issue. The study concerns three waste textile materials (cotton, silk, and polyamide) and the following process parameters: temperatures from 400 to 800 °C and time from 0 to 900 s. The results of the study are characteristics, i.e., functions describing the kinetics of volatile matter release as a function of process parameters. The general forms of these functions were determined taking into account fundamental physical and chemical laws. In addition to process parameters, the functions include coefficients whose values were determined on the basis of experimental measurements. The characteristics were determined for isothermal processes as well as for generalized processes, additionally accounting for temperature variability, which represents an original contribution of this study. Kinetic coefficients were derived from the obtained characteristics. The studies revealed mass fractions of volatile matter exceeding even 90%. The obtained characteristics may serve as tools for improving the sustainable management of textile waste by enabling more rational control of thermal processes. Full article

Polyethylene terephthalate (PET) is among the most used plastics in domestic and industrial applications, particularly packaging, food containers and textiles. However, its recalcitrance to decomposition and biodegradation mostly results in landfilling and accumulation of PET waste in the environment if not processed. Chemical recycling of PET via selective depolymerization into its monomers may represent a pivotal step in the development of a truly circular economy of PET, which is still limited by economic and environmental sustainability issues. In this work, the depolymerization of PET is reported using ZnO as an insoluble catalyst, and ethanol as both a lytic agent and green solvent. A detailed investigation of reaction parameters, including reaction temperature, time and catalyst loading, showed that complete conversion of PET to diethyl terephthalate (DET) can be achieved with 92.5% selectivity at 180 °C and 48 h, with the potential for full DET selectivity at longer reaction times. The solid catalyst could be recovered and reused by simple centrifugation, with no loss of conversion or selectivity over three consecutive reuses. Full article

Blended polyester (PET)-based textiles comprise a significant portion of post-consumer waste, posing substantial challenges to circular economy initiatives while contributing to microfiber (MF) pollution. Despite the considerable recycling potential of PET textiles, no commercially viable technologies currently exist that can efficiently separate and recycle blended PET-based textile waste on an industrial scale. This review provides a comprehensive analysis of recycling strategies for post-consumer blended PET-based textiles and their subsequent valorization pathways. Mechanical, chemical, and biological recycling processes are mostly not yet market-ready, although chemical approaches are considered particularly promising. The findings highlight a critical need for advanced sorting technologies, enhanced material traceability, and robust MF mitigation strategies to foster circularity and contribute to the United Nations Sustainable Development Goals (SDGs). The results further indicate that mechanical recycling of blended PET textiles leads to significant MF release due to fiber fragmentation, whereas chemical recycling offers the potential for improved material recovery, but remains limited by high energy demand and solvent-related challenges. While closed-loop approaches support true circularity by maintaining textile-to-textile material flows, open-loop pathways repurpose textile waste for high-value non-textile applications. Full article

Municipal Solid Waste (MSW) is a common problem in all cities worldwide; it is expected to increase to 3400 billion tons by 2050. In Mexico, an average of 108,146 tons of MSW are generated daily. Artificial Intelligence (AI) is a computer tool that allows the development of systems that facilitate the recycling process. However, most AI programs focus on classifying paper, plastic, glass and metal; therefore, wood and textile waste have received little attention. Using texture techniques such as Local Binary Pattern (LBP), Gray-Level Co-occurrence Matrix (GLCM), Histogram of Oriented Gradients (HOG), Canny/Sobel edge detection, Fractal Dimension (FD), feature values were extracted and integrated from 4396 images belonging to wood and textile categories. Using the Random Forest Importance method, the most significant features were selected to train three Machine Learning (ML) algorithms. Multilayer Perceptron (MLP) achieved the best performance in accuracy with 96.70%, followed by Random Forest (RF) at 95.45% and Support Vector Machine (SVM) with 95.22%. The implementation of these comparisons will serve as a basis for the development of new technological tools with low computational cost that carry out a proper waste separation. Full article

Increasing amounts of textile waste require rapid implementation of novel recycling technologies. Biocatalytic degradation via enzymatic hydrolysis can be used to separate blends, which are otherwise inaccessible. However, the complex nature of the substrate and narrow operating window of the reaction necessitates process optimization but also complicates computational approaches. The reaction is performed in aqueous suspension at ambient pressure and temperatures well below boiling. Due to the gentle process conditions, preliminary assessment of ideal stirrer geometries can be performed in water under ambient conditions, using stirrers produced from commodity plastics using material extrusion-based 3D-printing at both bench (2 L) and semi-pilot (30 L) scale. Eight geometries were assessed using suspension activity (via cloud height), mixing energy consumption, and mixing time assessment via tracer addition at the bench scale. Four of these geometries were chosen for scale-up in a 30 L conical vessel. While large, especially close-clearance mixing equipment performed well at both sizes, an increase in performance of the pitched-blade turbine was observed at 30 L. This highlights the necessity of experimental scaleup procedure as well as optimized stirrer geometries for enzymatic hydrolysis. Full article

N-methylmorpholine N-oxide (NMMO monohydrate) is widely used for cellulose fibre production, as in the Lyocell process. However, fibre spinning from denim wastes remains significantly more complex due to its higher viscosity, the presence of indigo dye, and NMMO’s temperature sensitivity. These factors together create serious challenges for denim dissolution and fibre regeneration. This study presents a comprehensive rheological and structural characterisation of regenerated cellulose fibres derived from waste denim dissolved in NMMO. Oscillatory and steady-state rheological tests were conducted across concentrations (4–8 wt%) and temperatures (60–90 °C) to determine optimal spinning conditions. A 6% denim/NMMO solution at 80 °C displayed the most favourable rheological balance within the investigated concentration window (4–8 wt%), moderate complex viscosity, well-defined viscoelastic transitions, and a Tan δ value (~0.94) consistent with stable jet formation in air-gap spinning. Steady shear tests confirmed strong shear-thinning behaviour and mechanical predictability, essential for spinneret extrusion. Thermal ramp experiments validated 80 °C as the upper safe limit, balancing flow processability with structural integrity while avoiding solidification or NMMO degradation. The identified rheological parameters fall within ranges reported for spinnable cellulose dopes in air-gap spinning systems, suggesting strong potential for fibre formation under controlled conditions. These findings establish a robust rheological framework for denim-derived cellulose in NMMO and provide a foundation for future investigations into controlled fibre spinning and process scale-up in sustainable textile recycling. Full article

The textile industry consumes a significant quantity of water and produces effluent containing water-soluble dyes and heavy metals such as Lead (Pb), Cadmium (Cd), Chromium (Cr), Copper (Cu), and Zinc (Zn), among others. Heavy metal contamination of water bodies and their impact on aquatic life, as well as on human health, is of prime importance. This review examined the potential of phytoremediation, a low-cost and eco-friendly process for removing contaminants from textile effluent. This review also investigated the impact of heavy metal toxicity on aquatic plants used for biogas production post phytoremediation application. This review evaluated textile effluent characteristics, efficiency evaluation of phytoremediation of textile wastewater, metal uptake mechanisms of aquatic plants, and anaerobic digestion processes with emphasis on Water hyacinth (Eichhornia crassipes), Duckweed (Lemna minor), and Water lettuce (Pistia stratiotes). The findings indicated that these aquatic plants possess immense potential for removing heavy metals and other impurities by employing phytoextraction and rhizofiltration methods. Their rapid growth rate makes them preferred candidates for anaerobic digestion. However, accumulation of heavy metals in plant tissues inhibits microbial activities during anaerobic digestion, resulting in fluctuations in biogas and methane production. Findings also showed that these aquatic plants are efficient in the removal of heavy metals in water while yielding considerable biomass that can be used to produce bioenergy through anaerobic digestion. However, the sequestration of heavy metals in plant biomass may affect the rate of methane generation efficiency. The findings of this review suggest that phytoremediation has promising potential for the recycling of textile wastewater and, when coupled with biogas production, contributes towards a circular bioeconomy, an approach that integrates closed-loop resource utilization with renewable biological systems to minimize waste. Full article