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Institute
- Fachbereich Medizintechnik und Technomathematik (2052) (remove)
In this work, the catalyst manganese(IV) oxide (MnO2), of calorimetric gas sensors (to monitor the sterilization agent vaporized hydrogen peroxide) has been investigated in more detail. Chemical analyses by means of X-ray-induced photoelectron spectroscopy have been performed to unravel the surface chemistry prior and after exposure to hydrogen peroxide vapor at elevated temperature, as applied in the sterilization processes of beverage cartons. The surface characterization reveals a change in oxidation states of the metal oxide catalyst after exposure to hydrogen peroxide. Additionally, a cleaning effect of the catalyst, which itself is attached to the sensor surface by means of a polymer interlayer, could be observed.
It is well known that the degradation environment can strongly influence the biodegradability and kinetics of biodegradation processes of polymers. Therefore, besides the monitoring of the degradation process, it is also necessary to control the medium in which the degradation takes place. In this work, a micromachined multi-parameter sensor chip for the control of the polymer-degradation medium has been developed. The chip combines a capacitive field-effect pH sensor, a four-electrode electrolyte-conductivity sensor and a thin-film Pt-temperature sensor. The results of characterization of individual sensors are presented. In addition, the multi-parameter sensor chip together with an impedimetric polymer-degradation sensor was simultaneously characterized in degradation solutions with different pH and electrolyte conductivity. The obtained results demonstrate the feasibility of the multi-parameter sensor chip for the control of the polymer-degradation medium.
Multimodal bioimage sensor
(2014)
To visualize the biochemical distribution two-dimensionally, we invented a solid-state-type ion image sensor that indicates the chemical activity of solutions and cells. The device, which consists of a CCD array covered with a functionalized membrane to detect charge accumulation, is highly sensitive to changes in the concentration and two-dimensional distribution of ions and biomaterials.
Background
True date palms (Phoenix dactylifera L.) are impressive trees and have served as an indispensable source of food for mankind in tropical and subtropical countries for centuries. The aim of this study is to differentiate date palm tree varieties by analysing leaflet cross sections with technical/optical methods and artificial neural networks (ANN).
Results
Fluorescence microscopy images of leaflet cross sections have been taken from a set of five date palm tree cultivars (Hewlat al Jouf, Khlas, Nabot Soltan, Shishi, Um Raheem). After features extraction from images, the obtained data have been fed in a multilayer perceptron ANN with backpropagation learning algorithm.
Conclusions
Overall, an accurate result in prediction and differentiation of date palm tree cultivars was achieved with average prediction in tenfold cross-validation is 89.1% and reached 100% in one of the best ANN.