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Sterilisation processes are compulsory in medicine, pharmacy, and food industries to prevent infections of consumers and microbiological contaminations of products. Monitoring the sterilisation by conventional microbiological methods is time- and lab-consuming. To overcome this problem, in this work a novel biosensor has been proposed. The sensor enables a fast method to evaluate sterilisation processes. By means of thin-film technology the sensor's transducer structures in form of IDEs (interdigitated electrodes) have been fabricated on a silicon substrate. Physical characterisation of the developed sensor was done by AFM, SEM, and profilometry. Impedance analyses were conducted for the electrical characterisation. As microbiological layer spores of B. atrophaeus have been immobilised on the sensing structure; spores of this type are a well-known sterilisation test organism. Impedance measurements at a fixed frequency over time were performed to monitor the immobilisation process. A sterilisation process according to aseptic filling machines was applied to demonstrate the sensor functionality. After both, immobilisation and sterilisation, a change in impedance could successfully be detected.
Monitoring of organic acids (OA) and volatile fatty acids (VFA) is crucial for the control of anaerobic digestion. In case of unstable process conditions, an accumulation of these intermediates occurs. In the present work, two different enzyme-based biosensor arrays are combined and presented for facile electrochemical determination of several process-relevant analytes. Each biosensor utilizes a platinum sensor chip (14 × 14 mm²) with five individual working electrodes. The OA biosensor enables simultaneous measurement of ethanol, formate, d- and l-lactate, based on a bi-enzymatic detection principle. The second VFA biosensor provides an amperometric platform for quantification of acetate and propionate, mediated by oxidation of hydrogen peroxide. The cross-sensitivity of both biosensors toward potential interferents, typically present in fermentation samples, was investigated. The potential for practical application in complex media was successfully demonstrated in spiked sludge samples collected from three different biogas plants. Thereby, the results obtained by both of the biosensors were in good agreement to the applied reference measurements by photometry and gas chromatography, respectively. The proposed hybrid biosensor system was also used for long-term monitoring of a lab-scale biogas reactor (0.01 m³) for a period of 2 months. In combination with typically monitored parameters, such as gas quality, pH and FOS/TAC (volatile organic acids/total anorganic carbonate), the amperometric measurements of OA and VFA concentration could enhance the understanding of ongoing fermentation processes.
Abstract of the authors: In many areas of computer science ontologies become more and more important. The use of ontologies for domain modeling often brings up the issue of ontology integration. The task of merging several ontologies, covering specific subdomains, into one united ontology has to be solved. Many approaches for ontology integration aim at automating the process of ontology alignment. However, a complete automation is not feasible, and user interaction is always required. Nevertheless, most ontology integration tools offer only very limited support for the interactive part of the integration process. In this paper, we present a novel approach for the interactive integration of ontologies. The result of the ontology integration is incrementally updated after each definition of a correspondence between ontology elements. The user is guided through the ontologies to be integrated. By restricting the possible user actions, the integrity of all defined correspondences is ensured by the tool we developed. We evaluated our tool by integrating different regulations concerning building design.
The conjunction of (bio-)chemical recognition elements with nanoscale biological building blocks such as virus particles is considered as a very promising strategy for the creation of biohybrids opening novel opportunities for label-free biosensing. This work presents a new approach for the development of biosensors using tobacco mosaic virus (TMV) nanotubes or coat proteins (CPs) as enzyme nanocarriers. Sensor chips combining an array of Pt electrodes loaded with glucose oxidase (GOD)-modified TMV nanotubes or CP aggregates were used for amperometric detection of glucose as a model system for the first time. The presence of TMV nanotubes or CPs on the sensor surface allows binding of a high amount of precisely positioned enzymes without substantial loss of their activity, and may also ensure accessibility of their active centers for analyte molecules. Specific and efficient immobilization of streptavidin-conjugated GOD ([SA]-GOD) complexes on biotinylated TMV nanotubes or CPs was achieved via bioaffinity binding. These layouts were tested in parallel with glucose sensors with adsorptively immobilized [SA]-GOD, as well as [SA]-GOD crosslinked with glutardialdehyde, and came out to exhibit superior sensor performance. The achieved results underline a great potential of an integration of virus/biomolecule hybrids with electronic transducers for future applications in biosensorics and biochips.
Nanotubular tobacco mosaic virus (TMV) particles and RNA-free lower-order coat protein (CP) aggregates have been employed as enzyme carriers in different diagnostic layouts and compared for their influence on biosensor performance. In the following, we describe a label-free electrochemical biosensor for improved glucose detection by use of TMV adapters and the enzyme glucose oxidase (GOD). A specific and efficient immobilization of streptavidin-conjugated GOD ([SA]-GOD) complexes on biotinylated TMV nanotubes or CP aggregates was achieved via bioaffinity binding. Glucose sensors with adsorptively immobilized [SA]-GOD, and with [SA]-GOD cross-linked with glutardialdehyde, respectively, were tested in parallel on the same sensor chip. Comparison of these sensors revealed that TMV adapters enhanced the amperometric glucose detection remarkably, conveying highest sensitivity, an extended linear detection range and fastest response times. These results underline a great potential of an integration of virus/biomolecule hybrids with electronic transducers for applications in biosensorics and biochips. Here, we describe the fabrication and use of amperometric sensor chips combining an array of circular Pt electrodes, their loading with GOD-modified TMV nanotubes (and other GOD immobilization methods), and the subsequent investigations of the sensor performance.
The small animal PET scanners developed by the Crystal Clear Collaboration (ClearPETtrade) detect coincidences by analyzing timemarks which are attached to each event. The scanners are able to save complete single list mode data which allows analysis and modification of the timemarks after data acquisition. The timemarks are obtained from the digitally sampled detector pulses by calculating the baseline crossing of the rising edge of the pulse which is approximated as a straight line. But the limited sampling frequency causes a systematic error in the determination of the timemark. This error depends on the phase of the sampling clock at the time of the event. A statistical method that corrects these errors will be presented
In energy economy forecasts of different time series are rudimentary. In this study, a prediction for the German day-ahead spot market is created with Apache Spark and R. It is just an example for many different applications in virtual power plant environments. Other examples of use as intraday price processes, load processes of machines or electric vehicles, real time energy loads of photovoltaic systems and many more time series need to be analysed and predicted.
This work gives a short introduction into the project where this study is settled. It describes the time series methods that are used in energy industry for forecasts shortly. As programming technique Apache Spark, which is a strong cluster computing technology, is utilised. Today, single time series can be predicted. The focus of this work is on developing a method to parallel forecasting, to process multiple time series simultaneously with R and Apache Spark.