Abstract
Textile materials, due to their large surface area and moisture retention capacity, allow the growth of microorganisms, causing undesired effects on the textile and on the end-users. The textile industry employs nanomaterials (NMs)/composites and nanofibers to enhance textile features such as water/dirt-repellent, conductivity, antistatic properties, and enhanced antimicrobial properties. As a result, textiles with antimicrobial properties are an area of interest to both manufacturers and researchers. In this study, we present novel regression models that predict the antimicrobial activity of nano-textiles after several washes. Data were compiled following a literature review, and variables related to the final product, such as the experimental conditions of nano-coating (finishing technologies) and the type of fabric, the physicochemical (p-chem) properties of NMs, and exposure variables, were extracted manually. The random forest model successfully predicted the antimicrobial activity with encouraging results of up to 70% coefficient of determination. Attribute importance analysis revealed that the type of NM, shape, and method of application are the primary features affecting the antimicrobial capacity prediction. This tool helps scientists to predict the antimicrobial activity of nano-textiles based on p-chem properties and experimental conditions. In addition, the tool can be a helpful part of a wider framework, such as the prediction of products functionality embedded into a safe by design paradigm, where products’ toxicity is minimized, and functionality is maximized.
Highlights
Textile materials are susceptible to microbial contamination due to their structural properties in combination with numerous environmental factors such as humidity and temperature [1]
We developed a Machine learning (ML) tool to predict the antimicrobial efficacy of nano-textiles after several washes
We examined various regression models and discovered that the random forest (RF) model provides a superior prediction with a 70% accuracy
Summary
Textile materials are susceptible to microbial contamination due to their structural properties in combination with numerous environmental factors such as humidity and temperature [1]. The growth of harmful microorganisms imposes a range of impacts on the textile, including the generation of unpleasant odour [2], stains and discoloration [3], reduction of fabrics’ mechanical strength [4], and an increased probability of contamination to the end-user [5]. It is important to impart antimicrobial finishes on textile materials to minimize the growth of microbes during their usage [6]. Microbes have the ability to multiply and survive on the textile from days to months, and colonization of the pathogenic microorganisms on surfaces lead to the formation of biofilms that can become inaccessible to antimicrobial agents [7,8]. Biofilm-associated diseases account for over 80 percent of infections, resulting in increased patient morbidity and significant medical burdens [10]. Numerous studies and investigations have focused on antimicrobial treatment of textile materials, which entails a set of requirements, namely, (1) efficiency against a broad spectrum of species [11]; (2) no adverse effect on the quality of the textile [12]; (3) durability in the course of laundering, dry cleaning, and hot pressing; (4) compatibility with textile chemical processes such as dyeing [13]; and (5) biocompatibility, which consists of cytotoxicity, sensitization, and irritation testing before being marketed [14]
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