The influence of microscopic features on the self-cleaning ability of 3D printed fabric-like structures

Abstract

Self-cleaning surfaces are getting significant attention within multiple scientific and industrial fields. Especially for textile fabrics, it is observed that self-cleaning textile fabric surfaces are created by manipulating the surface features with the help of coatings and nanoparticles, which are considered costly and far more complicated. However, the exploration of the potential for self-cleaning by controlling the fabrication parameters of textile fabrics at the microscopic level has not been addressed. The purpose of this study was to establish the context of self-cleaning textile fabrics by controlling the fabrication parameters of the fabric at the microscopic level. The control of the fabrication parameters is not easy in conventional fabric manufacturing techniques. Due to this reason, most textile fabrics use surface coating methods for self-cleaning features. The current evolution in 3D printed technology provides an opportunity to control the fabrication parameters during fabric manufacturing and generate self- cleaning features at the woven structural level. This study focuses on the possibility of developing a 3D-printed self-cleaning textile fabric using different printing parameters. It also identifies the significance of the fabric’s microscopic features, such as porosity, surface roughness, and wettability, along with the aesthetic look after optimizing these features. Further, the influence of these features on mechanical strength at the fabric woven structure level was tested. The optimization of printing parameters was modelled to identify the optimum self-cleaning properties for the 3D-printed specimens and the validation model was accomplished under a set of experimental methods. The study includes the combination of three printing parameters: layer height (LH) (0.15, 0.13, 0.10 𝑚𝑚) and extruder width (EW) (0.5, 0.4, 0.3 𝑚𝑚), along with two different angular printing orientations (O) (45 ° and 90 °). The other parameters, such as nozzle temperature (℃), print speed (𝑚𝑚/𝑠), and infill density (%), remained constant for all the samples. Three different thermoplastic flexible filaments printing materials are used: thermoplastic polyurethane (TPU 98A), thermoplastic elastomers (TPE felaflex), and thermoplastic co-polyester (TPC flex45). The 162 prepared samples are tested based on an experimental scheme to evaluate the self-cleaning ability. The microscopic features (porosity, roughness, and wettability) which are mainly responsible for this ability, are measured and recorded to evaluate and compare the best values for self- cleaning between the three chosen materials. The data are analysed to define the optimal self-cleaning number. Lastly, the experimental outputs are used in analytical calculations to find the relationship between changes in printing parameters and microscopic features. The study revealed that the printing parameters significantly affect the self- cleaning properties when optimizing the selection of the process parameter combination of layer height, extruder width, and printing orientation. The study successfully created a linear regression model to demonstrate the relationship between 3D printing parameters (layer height, extruder width, and orientation) and the self-cleaning microscopic features of the 3D-printed polymeric textile fabrics. It also identified that the (TPE) has a better self-cleaning ability than the other two materials.