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In the years 2016 to 2018 the Bavarian-Czech Academic Agency (BTHA) funded in summary four bilateral projects between
Aschaffenburg University and CVUT Prague: JEUMICO, TRILAMICO, JODEXRA, and AXROCO. The effective combination
of equipment, manpower, and know-how of both partners resulted in the development of stress-compensated iridium
coatings for astronomical X-ray mirrors. The findings have been published at scientific conferences and in joint papers. This
poster gives a review on the social , political and educational aspects of the collaborations in form of a photo documentation.
Nickel-iron (NiFe) nanowires with a high contentof nickel and large aspect-ratio show promising characteris-tics for applications in modern micro-systems. In this study,nanowires with well-controlled diameter, height and compo-sition are fabricated using nanoporous poly-carbonate (PC)and anodized aluminum-oxide (AAO) membranes. Differ-ences between potentiostatic and pulsed deposition methodswere studied with regard to their flexibility and a recommen-dation for the best use of the individual types is given. Withthe application of the presented processes, future technolo-gies as magnetic energy-harvesting or nano-fluxgate sensorscan be improved.
Applying technologies of additive manufacturing to the field of tissue engineering created a pioneering new approach tomodelcomplex cell systemsartificially. Regarding its huge potential, bioprinting is still in its infancies and many questions are still unanswered. To address this issue, an extrusion-based bioprinting process was used to deposit human embryonic kidney (HEK) cellsin a defined pattern.It wasshownthat cells remained viable in the bioprinted construct for up to 8 days after printing (DAP). This work displays a proof of principle for a controlled cell formation which shall later be appliedtoin vitro drug screening tests.
Understanding the complexity of biological neural
networks like the human brain is one of the scientific
challenges of our century. The organization of the brain
can be described at different levels, ranging from small neural
networks to entire brain regions. Existing methods for
the description of functionally or effective connectivity are
based on the analysis of relations between the activities of
different neural units by detecting correlations or information
flow. This is a crucial step in understanding neural disorders
like Alzheimer’s disease and their causative factors.
To evaluate these estimation methods, it is necessary to refer
to a neural network with known connectivity, which is
typically unknown for natural biological neural networks.
Therefore, network simulations, also in silico, are available.
In this work, the in silico simulation of large scale neural
networks is established and the influence of different topologies
on the generated patterns of neuronal signals is investigated.
The goal is to develop standard evaluation methods
for neurocomputational algorithms with a realistic large
scale model to enable benchmarking and comparability of
different studies.