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Anton Sinner

Publications and source records attributed to Anton Sinner.

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Surface-induced ordering and continuous breaking of translational symmetry in conjugated polymers

Surface-induced liquid crystalline phase transitions evoke fundamental interest and can provide deeper insight into the nature of low-dimensional phases. Board-like conjugated polymers are of particular interest because they exhibit novel sanidic liquid crystalline mesophases that have not been widely studied. Furthermore, films of these polymers often exhibit preferential molecular orientation near the free surface, suggesting that the surface influences the ordering process. However, the underlying mechanism of this surface-induced ordering remains unclear. Here, we use surface-sensitive grazing-incidence X-ray scattering to monitor the formation and disappearance of positional order in situ in thin films of two board-like conjugated polymers representative of two classes: polythiophenes and polydiketopyrrolopyrroles. The results show that the free surface induces the formation of a highly oriented, sanidic disordered mesophase at the surface of both conjugated polymers, thus marking the emergence of positional order. This ordering process continues upon cooling and involves multiple transitions into more ordered liquid crystalline mesophases, both at the surface and in the bulk. The positional smectic-like order parameter exhibits continuous temperature dependence near the transition, signifying that the breaking of translational symmetry by the surface occurs continuously. Theoretical analysis allows us to accurately describe the temperature-dependent order parameter when critical behavior is considered.

cond-mat.soft

NeuroQuantify -- An Image Analysis Software for Detection and Quantification of Neurons and Neurites using Deep Learning

The segmentation of cells and neurites in microscopy images of neuronal networks provides valuable quantitative information about neuron growth and neuronal differentiation, including the number of cells, neurites, neurite length and neurite orientation. This information is essential for assessing the development of neuronal networks in response to extracellular stimuli, which is useful for studying neuronal structures, for example, the study of neurodegenerative diseases and pharmaceuticals. However, automatic and accurate analysis of neuronal structures from phase contrast images has remained challenging. To address this, we have developed NeuroQuantify, an open-source software that uses deep learning to efficiently and quickly segment cells and neurites in phase contrast microscopy images. NeuroQuantify offers several key features: (i) automatic detection of cells and neurites; (ii) post-processing of the images for the quantitative neurite length measurement based on segmentation of phase contrast microscopy images, and (iii) identification of neurite orientations. The user-friendly NeuroQuantify software can be installed and freely downloaded from GitHub https://github.com/StanleyZ0528/neural-image-segmentation.

q-bio.QM