Regensburg 2025 – wissenschaftliches Programm
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CPP: Fachverband Chemische Physik und Polymerphysik
CPP 15: Poster Session I
CPP 15.6: Poster
Montag, 17. März 2025, 19:00–21:00, P4
Time-resolved structure formation in biohybrid coatings revealed by in-situ GISAXS and machine learning — •Julian E. Heger1, Shachar Dan2, Yufeng Zhai2, Stephan V. Roth2,3, and Peter Müller-Buschbaum1 — 11TUM School of Natural Sciences, Chair for Functional Materials, Garching, Germany — 2Deutsches Elektronen-Synchrotron DESY, Hamburg, Germany — 3Department of Fibre and Polymer Technology, KTH Royal Institute of Technology, Stockholm, Sweden
Relationships between the structure and property of functional films are at the heart of material science, which makes understanding of how film morphology influences its function essential. Achieving a comprehensive and statistically relevant understanding of the film's characteristics often requires the use of indispensable tools like grazing-incidence small-angle X-ray scattering (GISAXS). GISAXS enables the exploration of the film's characteristic morphology in reciprocal space, such as characteristic size distributions. However, a challenge arises due to the loss of phase information during measurements, which inhibits a direct transformation from reciprocal space to real space via inverse Fourier transform. In addressing this obstacle neural networks (NN) emerge as promising solutions, as they offer potential ways to enable a fast transformation of GISAXS data. Here, we present the results of applying a NN which is trained on synthetic GISAXS data to evaluate the film formation of biohybrid coatings during deposition from in-situ GISAXS synchrotron data.
Keywords: Sustainable; Biohybrid; Morphology; GISAXS; Machine learning