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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.

Three indirect techniques—tensile, compression, and air permeability measurements—have been used to investigate the fiber network organization inside a highly porous nonwoven. They are standard material property measurements that require relatively simple equipment and are hence an interesting alternative to more costly direct analysis methods such as image analysis or X-ray tomography. The tensile measurement provides information on in-plane anisotropy of the nonwoven, through the use of an experimental parameter obtained from the tensile moduli measured in two perpendicular directions. The air permeability and compression analyses are based on existing models that describe the 2D or 3D isotropy. It is also possible to obtain two characteristic lengths that describe the fiber network: the hydrodynamic diameter and the mean distance between fiber junctions. Another important parameter is the fraction of fibers oriented in the thickness direction that could be determined from the permeability data. Our study shows that the observations and results from all three independent techniques are very well correlated for the range of nonwovens studied, and thus provide coherent description and insight of their internal structure. The nonwovens were found to exhibit in-plane anisotropy, while fitting 3D isotropic models in compression and permeability behavior.

In this work, different woven fabrics with varying raw materials, fiber types and weave constructions were used for studying plasma treatment under different atmospheric conditions. Surface modification was characterized using wetting and capillarity surface analysis methods. Moreover, a fuzzy sensitivity variation criterion was used to select the most relevant parameters for woven fabrics from experimental data measured on the fabrics and during the plasma process. In fact, the results obtained using this learning data-based fuzzy sensitivity variation criterion could effectively validate those obtained from the physical and chemical knowledge on plasma treatment. According to the results, air permeability, fiber count, weave construction and summit density were identified as the most relevant parameters, in addition to electrical power, treatment speed and fiber nature. This finding indicated that these parameters had an influence on the plasma treatment results.

Design for longevity is known as an eco-design opportunity and could help to reduce the environmental footprint of energy-free items. However, extending the lifespan of products is not always desirable and the focus should be on achieving an optimal lifespan. Operationally, recommendations for design for longevity usually refer to durability, repairability, upgradability or emotional attachment. The use of high-quality and robust material is frequently stated, although it is not obvious what high-quality material is. Based on a quality by design approach, this study aims to propose a methodology to design for optimal longevity with a consumer-oriented approach. To do so, it includes data collection of product quality and manufacturing processes and then embeds consumers’ knowledge. These are combined into data analysis to help to highlight relationships and the most appropriate quality contributors. This methodology relies on three-steps: first, a single quality score which includes consumers’ knowledge; secondly, a multi-scale reverse-engineering process; and finally a data analysis using principal component analysis. The originality of such a proposal is that it enables the consumers’ knowledge to be considered in the identification of appropriated quality contributors. The proposed methodology is implemented in the fashion sector as it is said to be the second most polluting one. Moreover, given the huge variety of materials and production processes available in textiles, the selection of the most suitable recommendations to support a longer lifespan is very complex. The presented case study involves 29 T-shirts and reveals the mechanical-related strengths to be the main quality contributors.

The decolorization of a cotton fabric dyed with a reactive dye (C.I. Reactive Black 5) was studied using an optimized ozone-assisted process at pilot scale. Box–Behnken design was used to evaluate the effects of three parameters on the decolorization of the dyed textile, namely, pH of the treatment (3–7), ozone concentration (5–85 g/m3 of ozone), and treatment time (10–50 min). The fitted mathematical model allowed us to plot response surfaces as well as isoresponse curves and to determine optimal decolorization conditions. In this study, we have proposed a pilot-scale machine which utilizes ozone for the color stripping of the dyed cotton. This pilot-scale application opens up the route for application of ozone at an industrial scale for achieving sustainability in the textile industry.

In air filtration, nonwoven materials are known to be pertinent structures for fine filtration and moderate pressure drop. In order to develop a filter that combines good permeability and high efficiency, it is important to identify the relevant structural parameters of the nonwoven. The main criteria studied in this paper are fiber fineness, solid volume fraction and basis length (total length of fiber in unit area of nonwoven). The effect of combining different fiber diameters in order to reach the best compromise is also investigated. Our results show that the use of binary blends of different fiber diameters improves overall filtration behavior, in comparison to nonwoven filters with equivalent unimodal diameter distribution. A theoretical filtration model is used to predict filtration behavior for different structural characteristics and these predictions are compared to experimental results. However, this comparison demonstrates the limits of existing models in the case of fiber blends.

In the present work the natural madder dye ( Rubia tinctorum L.) was applied to the simultaneous dyeing and functionalization of polyester (PET) fabric. In the first part of the study the color performance and the durability were revealed for exhaustion dyed fabric. The dyed fabric was then characterized with respect to ultraviolet (UV) protection ability and antibacterial activity against Staphylococcus aureus ( S. aureus ) and Escherichia coli ( E. coli ). CIELab color coordinates, namely the positive a * and b * values, confirmed a yellow/orange color of the dyed fabric. From durability tests, the color showed a moderate to good light fastness and good to excellent fastness to washing and rubbing. The madder dye improved both the UV protective performance and the antibacterial activity of the fabric. With 3 % on weight of fiber (owf) the UV protection factor increased up to 106, and the antibacterial activity up to 86 % against both types of bacteria tested.

The aim of this study is to investigate the influence of hybrid textile woven fabric structure on the electrical resistivity. Weave structure was varied by varying the weave pattern and the conductive yarn density in the woven fabrics. Electrical surface and volume resistivity were measured and compared to the fabric structural properties. Results showed that not only the conductive yarns percentage has an important effect on the electrical resistivity but also the weave structure. The most influencing structural parameter on surface resistivity was the woven fabric surface profile as it controls the contact quality between the conductive yarns and the measuring electrodes. A high surface resistivity was noticed when the contact quality was poor. When this contact quality was good, a linear correlation was found between surface electrical resistivity and the cover firmness factor, the apparent conductive fiber surface area as well as the conductive yarn floating length of the woven structures.

In the clothing industry, the understanding of the quality is a major issue to well meet the customer needs. The dilemma that faces manufacturers is to find the balance between good quality and “overquality,” what the quality criteria are, and how to target requirements specifications. The aim of this study was to propose a multi-attribute ranking method of products. Ranking is based on an overall quality score. The quality score, here called consumer-based quality, is computed via the combination of textile testing and consumer perception to deterioration. Such a perception has been surveyed, and damage that can lead to end of life has been investigated. Collected data have been translated into a consumer sensitivity using multi-criteria decision making and fuzzy techniques. The fuzzy analytic hierarchy process has been used. Five damage categories have been weighted. A selection of appropriate tests according to standards has been completed to test the product resistance to the damage. The tests results have been computed with the consumer sensitivity to obtain the consumer-based quality score. Finally, the ranking method is applied on T-shirts, and a single score ranking is made possible and objectively depict perceived quality.

Research approaches on the use of ecotechnologies like ozone assisted processes for the decolorization of textiles are being explored as against the conventional alkaline reductive process for the color stripping of the cotton textiles. The evaluation of these ecotechnologies must be performed to assess the environmental impacts. Partial “gate to gate” Life Cycle Assessment (LCA) was implemented to study the ozone based decolorization process of the reactive dyed cotton textiles. Experiments were performed to determine input and output data flows for decolorization treatment of reactive dyed cotton textile using the ozonation process. The functional unit was defined as “treatment of 40 g of reactive dyed cotton fabric to achieve more than 94% color stripping”. Generic and specific data bases were also used to determine flows, and International Life Cycle Data system (ILCD) method was selected to convert all flows into environmental impacts. The impact category “Water resource depletion” is the highest for all the ozonation processes as it has the greatest relative value after normalization amongst all the impact indicators. Electricity and Oxygen formation were found to be the major contributors to the environmental impacts. New experimental conditions have been studied to optimize the impacts.