The detection of shedded fibers in effluents from textile washing has attracted much attention due to its reported contribution to microplastic pollution. Commonly used analytical methods for fiber detection in liquids are based on filtration with subsequent microscopic analysis and/or gravimetric weighing. These approaches are time-consuming and prone to errors. In this study, an approach based on dynamic image analysis was applied in order to set up an efficient method to analyze fibers in effluents from washing processes. In an initial validation step, reliable information on the counts of fibers and the morphological characteristics were confirmed. For wastewaters from polyester-cotton blends, the chemical nature of the fiber debris (natural vs. synthetic origin) was determined by combining the dynamic image analysis with a chemical pre-treatment. In this study, dynamic image analysis was revealed as a rapid, non-destructive, precise, and reliable technology for the characterization and quantification of the fiber debris, offering a promising approach for fiber analysis in liquids.
A novel and intelligent product development approach is required in this fast-growing and advanced technological era. Therefore, textile researchers have worked intensively to create efficient and transparent solutions for complex developments by using advanced modeling and simulation tools and techniques. This paper addresses a process for the thermal simulation of sportswear by considering the human thermophysiological model and important thermal properties of fabrics, i.e., thermal resistance, evaporative resistance, and permeability index. The results of the simulation are illustrated in terms of core body and mean skin temperatures. Moreover, results are validated by wear trials showing good consistency. This study is beneficial to the development of clothing for specific sports and the evaluation of comfort and heat stress during different sports activities.
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