Search Results (3)

Flame retardants are commonly used to reduce fire risk in various products and environments, including textiles. While many of these additives contain harmful substances, efforts are underway to reduce their usage. Current research aims to minimize flame-retardant quantities and enhance durability against external factors. This involves utilizing anchor peptides or material-binding peptides (MBPs), which are versatile molecules that bind strongly to surfaces like textiles. MBPs can be equipped with functional molecules, e.g., flame-retardant additives, by chemical or enzymatic bioconjugation. In this research, biohybrid flame retardants and an adapted finishing process are developed. Specifically, biobased adhesion promoters, the so-called MBPs, are used to finish textiles with flame-retardant additives. To date, there is no finishing process for treating textiles with MBPs and so a laboratory-scale finishing process based on foulard was developed. Necessary parameters, such as the take-off speed or the contact pressure of the squeezing rollers, are determined experimentally. In order to develop an adapted finishing process, various trials are designed and carried out. Part of the trials is the testing and comparison of different textiles (e.g., glass woven fabrics and aramid woven fabrics) under different conditions (e.g., different ratios of MBPs and flame retardants). The finished textiles are then analysed and validated regarding their flammability and the amount of adhered flame retardants. Full article

Oregano essential oil was encapsulated in poly-ϵ-caprolactone nanoparticles by a nanoprecipitation method using glycerin as a moisturizer. Nanocapsule characterization was performed by measuring the particle size, colloidal stability and encapsulation efficiency using dynamic light scattering, UV–Vis spectrophotometry and scanning electron microscopy (SEM). The nanoparticles had a mean particle size of 235 nm with a monomodal distribution. In addition, a low polydispersity index was obtained, as well as a negative zeta potential of −36.3 mV and an encapsulation efficiency of 75.54%. Nanocapsules were applied to polyester textiles through bath exhaustion and foulard processing. Citric acid and a resin were applied as crosslinking agents to improve the nanocapsules’ adhesion to the fabric. The adsorption, desorption, moisture content and essential oil extraction were evaluated to determine the affinity between the nanocapsules and the polyester. The adsorption was higher when the citric acid and the resin were applied. When standard oregano nanocapsules were used, almost all of the impregnated nanoparticles were removed when washed with water. The moisture content was evaluated for treated and non-treated textiles. There was a significant increase in the moisture content of the treated polyester compared to the non-treated polyester, which indicates that the polyester hydrophilicity increased with an important absorption of the essential oil nanocapsules; this can improve fabric comfort and probably promote antibacterial properties. Full article

Surrogate models are data-based approximations of computationally expensive simulations that enable efficient exploration of the model’s design space and informed decision making in many physical domains. The usage of surrogate models in the vibroacoustic domain, however, is challenging due to the non-smooth, complex behavior of wave phenomena. This paper investigates four machine learning (ML) approaches in the modelling of surrogates of sound transmission loss (STL). Feature importance and feature engineering are used to improve the models’ accuracy while increasing their interpretability and physical consistency. The transfer of the proposed techniques to other problems in the vibroacoustic domain and possible limitations of the models are discussed. Experiments show that neural network surrogates with physics-guided features have better accuracy than other ML models across different STL models. Furthermore, sensitivity analysis methods are used to assess how physically coherent the analyzed surrogates are. Full article