In the presented review, the past investigations have been complied and critically analyzed to highlight the influence of microscopic features on self-cleaning ability. In addition, challenges and research gaps that currently exist are discussed. This review concludes the current methods and processes to obtain self-cleaning ability using the surface features of textile fabrics manipulated with the help of the coatings and nanoparticles. However, no research was conducted to explore the self-cleaning potential of microscopic geometrical features of fabric at the woven structural level.

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The processes for making self-cleaning textile fabrics have been extensively discussed in the literature. However, the exploration of the potential for self-cleaning by controlling the fabrication parameters of the fabric at the microscopic level has not been addressed. The current evolution in 3D printing technology provides an opportunity to control parameters during fabric manufacturing and generate self-cleaning features at the woven structural level. Fabrication of 3D printed textile fabrics using the low-cost fused filament fabrication (FFF) technique has been achieved. Printing parameters such as orientation angle, layer height, and extruder width were used to control self-cleaning microscopic features in the printed fabrics. Self-cleaning features such as surface roughness, wettability contact angle, and porosity were analyzed for different values of printing parameters. The combination of three printing parameters was adjusted to provide the best self-cleaning textile fabric surface: 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°). Three different thermoplastic flexible filaments printing materials were used: thermoplastic polyurethane (TPU 98A), thermoplastic elastomers (TPE felaflex), and thermoplastic co-polyester (TPC flex45). Self-cleaning properties were quantified using a pre-set defined criterion. The optimization of printing parameters was modeled to achieve the best self-cleaning features for the printed specimens.