The problem of increasing plastic pollution has emerged as a significant societal issue. Plastics can originate from various sources, and there is growing concern among researchers to study and investigate this new category of pollution. The plastic waste is found at the macro, micro, and nanoscale, and its study has had great significance according to the perspective of posing hazardous impacts on living organisms. Given the high demand for functional textiles, the textile industries are supporting the coating of different polymeric based finishes on the surface of textile products. The plastic debris emitted from these coated finishes are in the ranges of nanometric scale, so-called polymeric nanoplastics (PNPs). With the new terminology, polymeric nanoplastics (PNPs) released from textile finishes or coatings are being increasingly mentioned, and the term fibrous microplastics (FMPs) can be seen as outdated. This study is based on an intensive review of a very novel category of debris plastics (PNPs) mostly produced from textile finishes or coatings. In fact, FMPs and PNPs released from synthetic textiles and textiles coated with plastic-based finishes during washing activities are considered to be a major cause that contributes to the current overall load of microplastics (MPs) in the environment. A link between the concentration of NPs from textile fibers and NPs from textile polymeric-based coatings in freshwater and sediments within a particular local setting and the extent of activities of the textile industry has been demonstrated. Invested efforts have been paid to consider and concentrate on plastic pollution (nanoplastics from textile polymeric coatings). We also summarize existing methodologies to elucidate the identification and proactive quantification of nanoplastics shed from the textile polymeric coatings. To this end, more than 40 studies have been done to identify the physical, chemical, and mechanical parameters and to characterize nanoplastics.

• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

In this study, an artificial neural network (ANN) is used for the prediction of tensile strength of nano titanium dioxide (TiO2) coated cotton. The coating process was performed by ultraviolet (UV) radiations. Later on, a backpropagation ANN algorithm trained with Bayesian regularization was applied to predict the tensile strength. For a comparative study, ANN results were compared with traditional methods including multiple linear regression (MLR) and polynomial regression analysis (PRA). The input conditions for the experiment were dosage of TiO2, UV irradiation time and temperature of the system. Simulation results elucidated that ANN model provides high performance accuracy than MLR and PRA. In addition, statistical analysis was also performed to check the significance of this study. The results show a strong correlation between predicted and measured tensile strength of nano TiO2-coated cotton with small error values.

• Klíčová slova
• artificial neural network, tensile strength, titanium dioxide nanoparticles,
• Publikační typ
• časopisecké články MeSH

This paper deals with the prediction of methylene blue (MB) dye removal under the influence of titanium dioxide nanoparticles (TiO2 NPs) through deep neural network (DNN). In the first step, TiO2 NPs were prepared and their morphological properties were analysed by scanning electron microscopy. Later, the influence of as synthesized TiO2 NPs was tested against MB dye removal and in the final step, DNN was used for the prediction. DNN is an efficient machine learning tools and widely used model for the prediction of highly complex problems. However, it has never been used for the prediction of MB dye removal. Therefore, this paper investigates the prediction accuracy of MB dye removal under the influence of TiO2 NPs using DNN. Furthermore, the proposed DNN model was used to map out the complex input-output conditions for the prediction of optimal results. The amount of chemicals, i.e., amount of TiO2 NPs, amount of ehylene glycol and reaction time were chosen as input variables and MB dye removal percentage was evaluated as a response. DNN model provides significantly high performance accuracy for the prediction of MB dye removal and can be used as a powerful tool for the prediction of other functional properties of nanocomposites.

• Klíčová slova
• artificial neural network, methylene blue dye removal, titanium dioxide nanoparticles,
• Publikační typ
• časopisecké články MeSH

Polymer based textile composites have gained much attention in recent years and gradually transformed the growth of industries especially automobiles, construction, aerospace and composites. The inclusion of natural polymeric fibres as reinforcement in carbon fibre reinforced composites manufacturing delineates an economic way, enhances their surface, structural and mechanical properties by providing better bonding conditions. Almost all textile-based products are associated with quality, price and consumer's satisfaction. Therefore, classification of textiles products and fibre reinforced polymer composites is a challenging task. This paper focuses on the classification of various problems in textile processes and fibre reinforced polymer composites by artificial neural networks, genetic algorithm and fuzzy logic. Moreover, their limitations associated with state-of-the-art processes and some relatively new and sequential classification methods are also proposed and discussed in detail in this paper.

• Klíčová slova
• Sequential Monte Carlo methods, artificial neural network, classification, fiber reinforced polymer composites, fuzzy logic,
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

The present study deals with modal work that is a type of framework for structural dynamic testing of linear structures. Modal analysis is a powerful tool that works on the modal parameters to ensure the safety of materials and eliminate the failure possibilities. The concept of classification through this study is validated for isotropic and orthotropic materials, reaching up to a 100% accuracy when deploying the machine learning approach between the mode number and the associated frequency of the interrelated variables that were extracted from modal analysis performed by ANSYS. This study shows a new classification method dependent only on the knowledge of resonance frequency of a specific material and opens new directions for future developments to create a single device that can identify and classify different engineering materials.

• Klíčová slova
• anisotropic, isotropic, modal analysis, mode shapes, orthotropic, resonance frequency,
• Publikační typ
• časopisecké články MeSH