6 Materialchemie
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We present a workflow for obtaining fully trained artificial neural networks that can perform automatic particle segmentations of agglomerated, non-spherical nanoparticles from scanning electron microscopy images “from scratch”, without the need for large training data sets of manually annotated images. The whole process only requires about 15 minutes of hands-on time by a user and can typically be finished within less than 12 hours when training on a single graphics card (GPU). After training, SEM image analysis can be carried out by the artificial neural network within seconds. This is achieved by using unsupervised learning for most of the training dataset generation, making heavy use of generative adversarial networks and especially unpaired image-to-image translation via cycle-consistent adversarial networks. We compare the segmentation masks obtained with our suggested workflow qualitatively and quantitatively to state-of-the-art methods using various metrics. Finally, we used the segmentation masks for automatically extracting particle size distributions from the SEM images of TiO2 particles, which were in excellent agreement with particle size distributions obtained manually but could be obtained in a fraction of the time.
Outer membrane lipopolysaccharides (LPS) play a crucial role in determining attachment behavior and pathogenicity of bacteria. The aim of this study was to develop a simple procedure for anchoring bacterial lipopolysaccharides to polystyrene (PS) microparticles as a model system for in situ attachment studies. By using a swellcapture methodology, commercially available LPS of Pseudomonas aeruginosa (strain ATCC 27316 serotype 10.22) was anchored onto PS microparticles in a proof-of-concept study. A detailed chemical and morphological characterization has proven the success of LPS incorporation. It was shown that the coverage and structure of the LPS film was concentration dependent. The procedure can easily be adapted to LPS of other bacterial strains to generate a synthetic model toolkit for attachment studies.