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End date: 2024 × Start date: 2020 × DDC: 620 × Version: publishedVersion × Publisher: Berlin : Springer Nature × WGL Institute: IFWD ×

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    Wettability control of polymeric microstructures replicated from laser-patterned stamps
    (Berlin : Springer Nature, 2020) Fu, Yangxi; Soldera, Marcos; Wang, Wei; Milles, Stephan; Deng, Kangfa; Voisiat, Bogdan; Nielsch, Kornelius; Lasagni, Andrés Fabian
    In this study, two-step approaches to fabricate periodic microstructures on polyethylene terephthalate (PET) and poly(methyl methacrylate) (PMMA) substrates are presented to control the wettability of polymeric surfaces. Micropillar arrays with periods between 1.6 and 4.6 µm are patterned by plate-to-plate hot embossing using chromium stamps structured by four-beam Direct Laser Interference Patterning (DLIP). By varying the laser parameters, the shape, spatial period, and structure height of the laser-induced topography on Cr stamps are controlled. After that, the wettability properties, namely the static, advancing/receding contact angles (CAs), and contact angle hysteresis were characterized on the patterned PET and PMMA surfaces. The results indicate that the micropillar arrays induced a hydrophobic state in both polymers with CAs up to 140° in the case of PET, without modifying the surface chemistry. However, the structured surfaces show high adhesion to water, as the droplets stick to the surfaces and do not roll down even upon turning the substrates upside down. To investigate the wetting state on the structured polymers, theoretical CAs predicted by Wenzel and Cassie-Baxter models for selected structured samples with different topographical characteristics are also calculated and compared with the experimental data.
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    Extracting local nucleation fields in permanent magnets using machine learning
    (Berlin : Springer Nature, 2020) Gusenbauer, Markus; Oezelt, Harald; Fischbacher, Johann; Kovacs, Alexander; Zhao, Panpan; Woodcock, Thomas George; Schrefl, Thomas
    Microstructural features play an important role in the quality of permanent magnets. The coercivity is greatly influenced by crystallographic defects, like twin boundaries, as is well known for MnAl-C. It would be very useful to be able to predict the macroscopic coercivity from microstructure imaging. Although this is not possible now, in the present work we examine a related question, namely the prediction of simulated nucleation fields of a quasi-three-dimensional (rescaled and extruded) system constructed from a two-dimensional image. We extract features of the image and analyze them via machine learning. A large number of extruded systems are constructed from 10 × 10 pixel sub-images of an Electron Backscatter Diffraction (EBSD) image using an automated meshing procedure. A local nucleation field is calculated by micromagnetic simulation of each quasi-three-dimensional system. Decision trees, trained with the simulation results, can predict nucleation fields of these quasi-three-dimensional systems from new images within seconds. As for now we cannot quantitatively predict the macroscopic coercivity, nevertheless we can identify weak spots in the magnet and see trends in the nucleation field distribution.