The COVID-19 pandemic has highlighted the critical need to improve indoor air quality (IAQ) through efficient air filtration, especially in heating, ventilation, and air-conditioning (HVAC) systems. While dedicated high-performance filters are effective, their high-pressure drops result in significant energy consumption when used in HVAC systems. Herein, we report the application of machine learning (ML) models to predict filtration efficiency and pressure drop, enabling the design and optimisation of filter media in HVAC. Specifically, three ML models, Gaussian process regression (GPR), artificial neural network (ANN), and decision tree (DT), have been trained on a dataset obtained from the literature. The dataset comprised key structural parameters of a wide range of filter media. The GPR model emerged as the most reliable predictor, exhibiting the highest coefficient of determination (R2) and lowest root mean squared error (RMSE) in predicting filtration efficiency and pressure drop, rendering it the most reliable predictor for small and uncertain datasets. The robustness of the GPR model is further confirmed via validation with commercially available filter media. In addition, the ML models accurately capture the established relationship between filtration efficiency and its characteristic drop at the most penetrating particle size (MPPS).

The textile waste recycling industry, dealing with both post-consumer and post-industrial waste, faces critical challenges such as high operating costs, unstructured resource planning and greenhouse gas (GHG) emissions due to transportation. With 92 million metric tons of textile waste generated annually—ranking as the fourth highest-pressure category and the fifth largest contributor to GHG emissions—there is an urgent need for an intelligent decision support tool to optimise recycling supply chains. Despite significant research on textile waste management, no such tool currently exists to support data-driven decision-making for network design in recycling systems. This research presents a supply chain network design model, integrating key recycling stages including collection centres, sorting facilities, recycling plants, and manufacturers. The tool aims to minimise overall supply chain costs—encompassing capital, operational, and transportation expenses—while simultaneously reducing emissions from processing operations and logistics. By balancing economic and environmental factors, this study provides practical insights for stakeholders on optimal facility locations, facility selection, inter-facility flows, and connectivity within the recycling supply chain. The proposed framework enhances operational efficiency, facilitates sustainable practices, and promotes a circular economy by transforming unstructured planning methods into a cost-effective and environmentally responsible supply chain design. 

The rapid expansion of digital solutions is transforming the landscape of the textile and fashion (T&F) industry. Advanced digitization within the T&F sector is not only altering the way the production and distribution operations are carried out, but also fostering a new paradigm of data, known as big data and data analytics known as big data analytics. This transformation is reshaping the approach of data-driven decision-making in the industry. Aligned with the recent developments, this chapter provides an overview of the characteristics of big data and data analytics, and how the T&F industry can leverage such change to create a competitive advantage.

This report uses system dynamics modeling to evaluate the impact of circular economy interventions on textile production and waste management within the European Union (EU). It analyzes material flows in the textile sector from a broad, macro-level perspective, specifically focusing on apparel consumption, and explores various scenarios that include rental, repair, and reuse practices. The report examines different growth patterns in textile consumption/production, with projections indicating that enhanced efforts in textile collection and sorting could significantly reduce unsorted waste.The growth in consumption is assessed using two approaches: one extrapolated from historical data and the other based on anticipated growth outlined in EU reports. In the first scenario, which relies on historical growth, the volume of sorted textile waste is expected to surpass unsorted waste by 2032. In contrast, the second scenario, which anticipates production growth, suggests that improvements in sorting and collection efforts will stabilize the growth of unsorted waste, leading to only a slight increase by 2035 rather than a significant rise.Furthermore, the report highlights crucial differences between these scenarios, particularly in the context of circular interventions such as rental, repair, and reuse practices. It explores the impact of rental and repair interventions on production and waste outcomes at varying substitution rates, providing insights into how these practices can influence overall sustainability in the textile sector.

Rendering any surface non-wettable requires it to be clean and dry after the droplet is deposited or impacted. Leveraging and quantifying the non-periodic or random topology non-wettable is intricate as the Cassie-Baxter state competes with the Wenzel or impaled state, which becomes further challenging for irregular and heterogeneous nonwoven materials. Herein, we report the fundamental insights of the impalement dynamics of droplets on metastable nonwovens and self-similar nonwoven-titanate nanostructured materials (SS-Ti-NMs) using laser scanning confocal microscopy in three dimensions. Our results represent the first example of liquid imbibition in metastable nonwovens and SS-Ti-NMs involving a complex interplay between a triumvirate of factors – the number of fibres in the defined cross-sectional area (volume), pore features, and intrinsic wetting properties of the constituent entities. Predictive models of the apparent contact angle and breakthrough pressure for nonwovens and their SS-Ti-NMs have been proposed based on micro- and nano-scale structural parameters. Enabled by X-ray microcomputed tomography analysis, a key set of three-dimensional fibre and structural parameters of nonwovens has been unveiled, which played a vital role in validating the predictive models of apparent contact angles.

The purpose of this paper is to understand supply network configuration for small-series production within high-cost contexts, and the context-specific decision logics associated. A total interpretive structural modelling (TISM) and MICMAC mixed-methods approach is used to determine and interpret interrelationships among SNC and capability-related aspects identified from the literature. Respondents come from EU textile/apparel companies, undertaking small-series production/sourcing in the region, with different roles in the value chain. The findings led to several propositions. They highlight the foundational nature of supply chain relationships and digital data sharing; interacting product/process flexibility and specialisation considerations, with associated enablers and barriers; the challenges related to location, which is the crucial supply chain driver; and the need to balance various interrelated capability drivers, such as quality, innovation, and sustainability. These findings can support practitioners for reconfiguration, and the approach can be used to address other contexts and thus enhance generalisability.

The wicking ability of the fabric is one of the most important parameters that affect the comfort of clothing. Silk is a natural long-fiber material, and researching the water-absorption properties of natural silk yarn helps in developing textile products with specific performance characteristics, thereby enhancing the competitiveness of textiles in the market. This research aims to analyze the wicking behavior of silk yarn simulating their interlaced conditions when they were woven. To investigate the effect of yarn twist and yarn fineness, 13 different silk yarns were examined in individual and interlaced scenarios. Results showed that both yarn twist and fineness affect the wicking performance, and the permeability and capillary hydraulic diameter were calculated. A comparison of experimental results and best theoretical fits according to Fries and Dryer’s proposed model. On the other hand, a good correlation was observed between the wicking length of single yarns and interlaced yarns. This suggests that the characteristics of single yarn can be potentially used for predicting the wicking behavior of woven textiles, where yarns form interlacements. This study can usher in innovations and enhancements that can benefit future virtual clothing design and real-world wear. 

Autohesion is a unique class of adhesion that enables the bonding of two identical surfaces by establishing intimate contact at interfaces. Creating intimacy between two identical surfaces poses a challenging task, often constrained by the presence of surface roughness and chemical heterogeneity. To surmount this challenge, we document a variety of autohesive traits in polypropylene-based meltblown nonwovens, accomplished through a facile, scalable, energy-efficient, and cost-effective ultrasonic bonding process. The mean work of autohesion for a single polypropylene bond, serving as a figure of merit, has been computed by extending the classical Johnson−Kendall−Roberts (JKR) theory by factoring in peel strength along with key fiber and structural parameters of nonwoven materials. Achieving a high figure of merit in ultrasonically bonded nonwovens hinges on the synergistic interplay of key process parameters, including static force, power, and welding speed, with the fiber and structural properties acting in concert. In this regard, peel-off force analysis has also been conducted on a series of twenty-seven ultrasonically bonded meltblown nonwovens prepared using a 33 full factorial design by systematically varying process parameters (static force, power, and welding speed) across three levels and extension rate. X-ray microcomputed tomography (microCT) analysis has been performed on select ultrasonically bonded nonwoven samples to discern their bulk characteristics. A broad spectrum of mean work of autohesion for a single polypropylene bond, ranging from 1.88 to 9.93 J/m², has been ascertained by modulating key process parameters.

Prolonging garment longevity is a well-recognized key strategy to reduce the overall environmental impact in the textile and clothing sector. In this context, change or degradation in esthetic or visual appeal of a garment with usage is an important factor that largely influence its longevity. Therefore, to engineer the garments for a required lifetime or prolong longevity, there is a need for predictive systems that can forecast the trajectory of visual degradation based on material/structural parameters or use conditions that can guide the practitioners for an optimal design. This paper develops a deep learning based predictive system for washing-induced visual change or degradation of selected garment areas. The study follows a systematic experimental design to generate and capture visual degradation in garment and equivalent fabric samples through 70 cycles in a controlled environment following guideline from relevant washing standards. Further, the generated data is utilized to train conditional Generative Adversarial Network-based deep learning model that learns the degradation pattern and links it to washing cycles and other seam properties. In addition, the predicted results are compared with experimental data using Frechet Inception Distance, to ascertain that the system prediction are visually similar to the experimental data and the prediction quality improves with training process.