Fabrication of a superhydrophobic surface using a fused deposition modeling (FDM) 3D printer with poly lactic acid (PLA) filament and dip coating with silica nanoparticles
Source abstract
Abstract
Abstract Fused deposition modeling (FDM) 3D printers are widely used for rapid prototyping and customized products because they are inexpensive and open source 3D printers. 3D printers can be used to produce complex designs, and there are almost no limits in terms of product shape. Various different materials for 3D printers have been widely studied to improve the functionality of 3D-printed parts. One such functional material is superhydrophobic 3D printable material. However, the material preparation is not easy, and relatively expensive material must be used for the whole product even though the material for superhydrophobicity is required only for the surface of the product. In this study, a commercially available FDM 3D printer without any modifications and a widely used polylactic acid (PLA) filament material were used to make patterned surface structures, and a hydrophobic coating with nanoscale structures was realized by a dip coating process using hydrophobic silica nanoparticles and methyl ethyl ketone. The wettability change in the 3D printed part before and after the dip coating process was evaluated by means of static contact angle and sliding angle measurements. The effects of the 3D printed grid and line patterned surface structures were also studied for the superhydrophobic surface. In addition, several complex three-dimensional structures were demonstrated. This process can be easily applied using FDM 3D printers and PLA filament and can be used for many applications such as liquid position control and low water adhesion.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Surface Modification and Superhydrophobicity · Additive Manufacturing and 3D Printing Technologies · Adhesion, Friction, and Surface Interactions
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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