A major obstacle to textile recycling is the presence of dyes, which limits the reuse of fibers in high-value applications. Despite previous studies on, cotton decolorization, the systematic development of an optimal formulation that preserves fabric integrity remains lacking. This study addresses this gap by investigating a decolorization method for mixed-dyed cotton textiles that enables successful redyeing while preserving fabric quality. Reactive and vat-dyed cotton fabrics were treated with sequential reductive and oxidative processes, in a full factorial design. The impact of input parameters on tensile strength was evaluated through statistical analysis using analysis of variance at a significance level of α = 0.05. The developed recipe was subsequently validated on post-consumer cotton textiles. Stripping efficiency was assessed using K/S values, and fabric quality was evaluated through tensile strength, pilling, and fuzzing appearance. Temperature showed the strongest influence on dye removal. Fabric strength was significantly affected by temperature and oxidizing agent, and by interactions of temperature with reducing agent and oxidation time. The optimized process achieved 98–99.5% color removal and retained 95% of the fabric’s tenacity. A stripping efficiency of >90% for post-consumer cotton validates the method’s applicability in real-world circular systems.

The growing volume of textile waste, driven by fast fashion and shortened garment lifespans, underscores the urgent need for scalable, automated sorting solutions. Progress is hindered by the absence of realistic, public datasets reflecting the variability of industrial textile sorting. We address this gap with TexSortML, a dataset of 751 post-consumer garments across 11 categories, captured under three controlled difficulty stages that simulate increasing handling disorder on a conveyor belt. On this dataset, we compare Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and CLIP-based zero-shot classifiers under five-fold cross-validation. A ViT fine-tuned directly on our data is the most accurate and most stable model at every stage, losing only 9.3 percentage points between the most and least controlled condition, against 16.6–16.9 points for the CNN baselines. The variant additionally pre-trained on external second-hand clothing data matches it only under controlled presentation and degrades sharply once garments are rotated or crumpled. CLIP-based zero-shot classifiers remain far behind and degrade the fastest. Rather than proposing a new architecture, this work contributes the dataset and a reproducible benchmark, and shows that extra training data can hurt when it is cleaner than the deployment condition. The dataset is publicly available on Zenodo.

Reducing the ecological footprint of aviation is a key objective in the development of future aircraft. This is particularly relevant in the emerging field of Urban Air Mobility, which demands sustainable yet industrially feasible solutions due to expected high production rates. As part of the cooperative research project KONKAV, innovative materials and manufacturing methods are being explored to meet these demands. One such approach is the partial consolidation of nonwovens made from recycled carbon fibers, aimed at producing multifunctional, recyclable components for Urban Air Mobility cabin linings for high bending stiffness requirements. This study presents the experimental characterization of various nonwoven architectures, focusing on how different levels of consolidation affect their specific mechanical properties. The partially consolidated structure enables tailored stiffness profiles, making it possible to optimize structural performance while integrating functions such as thermal insulation and acoustic damping directly into the lining. An analytical material model has been developed by analyzing the experimental results. The findings demonstrate that partially consolidated nonwovens can achieve a competitive stiffness-to-weight ratio, with advantages over conventional glass-fiber-reinforced composites in terms of eco-efficiency and circularity. The proposed construction method offers potential for cost-effective, lightweight solutions that support closed-loop material use in aviation interiors.