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Polydiallyldimethylammonium chloride, P, with a positive charge, was demonstrated to have a strong bond to the surface of anionic montmorrillonite, M, platelets in aqueous dispersions. The thickness of the M layer depended on external voltage applied. Clay platelets formed stacks upon adsorption to the polymer layer. Uniformity and regularity of deposited layers was improved with application of a negative potential during self-assembly of P.

Abstract Background Particle tracking passive microrheology relates recorded trajectories of microbeads, embedded in soft samples, to the local mechanical properties of the sample. The method requires intensive numerical data processing and tools allowing control of the calculation errors. Results We report the development of a software package collecting functions and scripts written in Python for automated and manual data processing, to extract viscoelastic information about the sample using recorded particle trajectories. The resulting program package analyzes the fundamental diffusion characteristics of particle trajectories and calculates the frequency dependent complex shear modulus using methods published in the literature. In order to increase conversion accuracy, segmentwise, double step, range-adaptive fitting and dynamic sampling algorithms are introduced to interpolate the data in a splinelike manner. Conclusions The presented set of algorithms allows for flexible data processing for particle tracking microrheology. The package presents improved algorithms for mean square displacement estimation, controlling effects of frame loss during recording, and a novel numerical conversion method using segmentwise interpolation, decreasing the conversion error from about 100% to the order of 1%.

Abstract Background Soft X-ray spectromicroscopy based absorption near-edge structure analysis, is a spectroscopic technique useful for investigating sample composition at a nanoscale of resolution. While the technique holds great promise for analysis of biological samples, current methodologies are challenged by a lack of automatic analysis software e. g. for selection of regions of interest and statistical comparisons of sample variability. Results We have implemented a set of functions and scripts in Python to provide a semiautomatic treatment of data obtained using scanning transmission X-ray microscopy. The toolkit includes a novel line-by-line absorption conversion and data filtering automatically identifying image components with significant absorption. Results are provided to the user by direct graphical output to the screen and by output images and data files, including the average and standard deviation of the X-ray absorption spectrum. Using isolated mouse melanosomes as a sample biological tissue, application of STXMPy in analysis of biological tissues is illustrated. Conclusion The STXMPy package allows both interactive and automated batch processing of scanning transmission X-ray microscopic data. It is open source, cross platform, and offers rapid script development using the interpreted Python language.