Student Quantitative characterization method for assessing the self-cleaning effect on laser microstructured aluminiumThursday (25.06.2020) 17:00 - 17:20 Room 2
In the last decades, surface functionalization through microstructuring gained an increasing demand in multiple materials for several applications, such as tribology, wettability or antibacterial properties. Concerning the wetting characteristics, it is well known that a superhydrophobic structure leads to a self-cleaning function. That means that a water droplet is unable to adhere to the substrate and thus it collects attached contaminants while rolling off the surface. Within this contribution, an innovative method to characterize the self-cleaning ability of microstructured aluminium is presented. For fabricating these microtextures on aluminium, nanosecond direct laser writing (DLW) was used to create a triangular-like mesh with a lateral feature size of 50 µm, while picosecond direct laser interference patterning (DLIP) was employed to structure a square array of pillars with a spatial period of 7 µm. In addition, both fabrication techniques were combined to produce a hierarchical topography. Organic and inorganic particles consisting of manganese oxide (MnO2) and polyamide (PA) with particle sizes of 1 µm and 100 µm were distributed over the samples’ surfaces representing contamination particles. To clean the surface from contamination, 10 µl water droplets were placed over the samples at different tilting angles, namely 15° and 30°. After each droplet was released, the amount of remaining dirt on the surface was monitored by taking images of the area of interest. After that, the image was inverted and the black and white pixels were counted using ImageJ software. Depending on the number of passing droplets, the amount of remaining contamination changed. Clearly, the more droplets rolled over the surface, the higher was the percentage of self-cleaned area. Remarkably, on the DLIP treated sample a contamination removal up to 93% of 100 µm sized PA and MnO2 particles was observed after 3 released droplets. In contrast to that, on the DLW treated sample up to 30 % of the 1 µm sized MnO2 particles remained on the surface after 9 released droplets. Based on SEM images, it can be concluded that the 1 µm particles become trapped inside the 50 µm deep DLW structures and cannot be collected by the rolling droplets. The presented characterization method was successfully implemented for quantifying the self-cleaning efficiency of superhydrophobic Al surfaces.
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