Dataset on droplet spreading and rebound behavior of water and viscous water-glycerol mixtures on superhydrophobic surfaces with laser-made channelsMendeley Data
Droplet impact, spreading and rebound was investigated experimentally on superhydrophobic laser-textured surfaces, yielding a dataset of 1498 datapoints. Data was collected on twelve types of surfaces with square grids of laser-made channels of various spacings (from 50 µm to 800 µm), with two group...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-08-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340925004275 |
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| Summary: | Droplet impact, spreading and rebound was investigated experimentally on superhydrophobic laser-textured surfaces, yielding a dataset of 1498 datapoints. Data was collected on twelve types of surfaces with square grids of laser-made channels of various spacings (from 50 µm to 800 µm), with two groups of six surfaces possessing either deep or shallow laser-made channels. Droplet impact tests were performed with water and viscous water-glycerol mixtures with viscosity values up to 160 mPa·s and droplet impact behavior was images with a high-speed camera at 5000 fps. Maximum spreading factor, contact time, droplet rebound efficiency, and maximum lamella velocity were extracted from the videos using software image processing. Moreover, information on droplet diameter, velocity, density, surface tension, dynamic viscosity, Weber number, and Reynolds number are provided. A supplementary dataset includes the same quantitative information for droplet impacts on a smooth, hydrophobic surface, resembling the surface between the laser-made channels on other superhydrophobic surfaces (125 additional datapoints). Furthermore, scanning electron microscopy images of the surfaces are provided alongside the measurements of static and dynamic contact angles with water and water-glycerol mixtures.The data may be useful for fields like wettability studies, surface engineering, and anti-icing research. It can help validate theoretical and numerical models of droplet spreading, retracting, and rebounding from poorly wettable surfaces, optimize superhydrophobic surfaces for applications such as self-cleaning and drag reduction, and contribute to machine learning models predicting droplet behavior. The data is particularly relevant for designing anti-icing surfaces by minimizing contact time and maximizing the restitution coefficient. Additionally, it supports applications in 3D printing, coating technologies, and inkjet printing by providing data on viscous liquid impacts on poorly wettable surfaces. |
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| ISSN: | 2352-3409 |