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A wireless sensor system based on the industrial ZigBee standard for low-rate wireless networking was developed that enables real-time monitoring of gaseous H2O2 during the package sterilization in aseptic food processes. The sensor system consists of a remote unit connected to a calorimetric gas sensor, which was already established in former works, and an external base unit connected to a laptop computer. The remote unit was built up by an XBee radio frequency (RF) module for data communication and a programmable system-on-chip controller to read out the sensor signal and process the sensor data, whereas the base unit is a second XBee RF module. For the rapid H2O2 detection on various locations inside the package that has to be sterilized, a novel read-out strategy of the calorimetric gas sensor was established, wherein the sensor response is measured within the short sterilization time and correlated with the present H2O2 concentration. In an exemplary measurement application in an aseptic filling machinery, the suitability of the new, wireless sensor system was demonstrated, wherein the influence of the gas velocity on the H2O2 distribution inside a package was determined and verified with microbiological tests.
The LAPS (light-addressable potentiometric sensor) platform is one of the most attractive approaches for chemical and biological sensing with many applications ranging from pH and ion/analyte concentration measurements up to cell metabolism detection and chemical imaging. However, although it is generally accepted that LAPS measurements are spatially resolved, the light-addressability feature of LAPS devices has not been discussed in detail so far. In this work, an extended electrical equivalent-circuit model of the LAPS has been presented, which takes into account possible cross-talk effects due to the capacitive coupling of the non-illuminated region. A shunting effect of the non-illuminated area on the measured photocurrent and addressability of LAPS devices has been studied. It has been shown, that the measured photocurrent will be determined not only by the local interfacial potential in the illuminated region but also by possible interfacial potential changes in the non-illuminated region, yielding cross-talk effects. These findings were supported by the experimental investigations of a penicillin-sensitive multi-spot LAPS and a metal-insulator-semiconductor LAPS as model systems.
An amperometric biosensor using a substrate recycling principle was realized for the detection of low adrenaline concentrations (1 nM) by measurements in phosphate buffer and Ringer’s solution at pH 6.5 and pH 7.4, respectively. In proof-of-concept experiments, a Boolean logic-gate principle has been applied to develop a digital adrenaline biosensor based on an enzyme AND logic gate. The obtained results demonstrate that the developed digital biosensor is capable for a rapid qualitative determination of the presence/absence of adrenaline in a YES/NO statement. Such digital biosensor could be used in clinical diagnostics for the control of a correct insertion of a catheter in the adrenal veins during adrenal venous-sampling procedure.
Human-induced pluripotent stem cell-derived cardiomyocytes (hiPS-CM) today are widely used for the investigation of normal electromechanical cardiac function, of cardiac medication and of mutations. Computational models are thus established that simulate the behavior of this kind of cells. This section first motivates the modeling of hiPS-CM and then presents and discusses several modeling approaches of microscopic and macroscopic constituents of human-induced pluripotent stem cell-derived and mature human cardiac tissue. The focus is led on the mapping of the computational results one can achieve with these models onto mature human cardiomyocyte models, the latter being the real matter of interest. Model adaptivity is the key feature that is discussed because it opens the way for modeling various biological effects like biological variability, medication, mutation and phenotypical expression. We compare the computational with experimental results with respect to normal cardiac function and with respect to inotropic and chronotropic drug effects. The section closes with a discussion on the status quo of the specificity of computational models and on what challenges have to be solved to reach patient-specificity.