Refine
Year of publication
Document Type
- Conference Proceeding (130)
- Article (77)
- Lecture (5)
- Working Paper (4)
- Poster (1)
- Talk (1)
Language
- English (218) (remove)
Has Fulltext
- yes (218) (remove)
Keywords
- Biosensor (25)
- Finite-Elemente-Methode (12)
- Einspielen <Werkstoff> (10)
- CAD (8)
- civil engineering (8)
- Bauingenieurwesen (7)
- Blitzschutz (6)
- FEM (6)
- Clusterion (5)
- shakedown analysis (5)
- Air purification (4)
- Hämoglobin (4)
- Limit analysis (4)
- Luftreiniger (4)
- Plasmacluster ion technology (4)
- Raumluft (4)
- Shakedown analysis (4)
- Sonde (4)
- Telekommunikationsmarkt (4)
- Einspielanalyse (3)
Institute
- Fachbereich Medizintechnik und Technomathematik (123)
- IfB - Institut für Bioengineering (60)
- Fachbereich Energietechnik (27)
- INB - Institut für Nano- und Biotechnologien (23)
- Fachbereich Maschinenbau und Mechatronik (17)
- Fachbereich Luft- und Raumfahrttechnik (14)
- Fachbereich Chemie und Biotechnologie (12)
- Fachbereich Wirtschaftswissenschaften (11)
- Fachbereich Bauingenieurwesen (7)
- Fachbereich Elektrotechnik und Informationstechnik (7)
- MASKOR Institut für Mobile Autonome Systeme und Kognitive Robotik (5)
- ECSM European Center for Sustainable Mobility (4)
- Nowum-Energy (4)
- Solar-Institut Jülich (4)
- Arbeitsstelle fuer Hochschuldidaktik und Studienberatung (1)
- Fachbereich Gestaltung (1)
- IBB - Institut für Baustoffe und Baukonstruktionen (1)
Reliable automation of the labor-intensive manual task of scoring animal sleep can facilitate the analysis of long-term sleep studies. In recent years, deep-learning-based systems, which learn optimal features from the data, increased scoring accuracies for the classical sleep stages of Wake, REM, and Non-REM. Meanwhile, it has been recognized that the statistics of transitional stages such as pre-REM, found between Non-REM and REM, may hold additional insight into the physiology of sleep and are now under vivid investigation. We propose a classification system based on a simple neural network architecture that scores the classical stages as well as pre-REM sleep in mice. When restricted to the classical stages, the optimized network showed state-of-the-art classification performance with an out-of-sample F1 score of 0.95 in male C57BL/6J mice. When unrestricted, the network showed lower F1 scores on pre-REM (0.5) compared to the classical stages. The result is comparable to previous attempts to score transitional stages in other species such as transition sleep in rats or N1 sleep in humans. Nevertheless, we observed that the sequence of predictions including pre-REM typically transitioned from Non-REM to REM reflecting sleep dynamics observed by human scorers. Our findings provide further evidence for the difficulty of scoring transitional sleep stages, likely because such stages of sleep are under-represented in typical data sets or show large inter-scorer variability. We further provide our source code and an online platform to run predictions with our trained network.
Quantitative nuclear magnetic resonance (qNMR) is considered as a powerful tool for multicomponent mixture analysis as well as for the purity determination of single compounds. Special attention is currently paid to the training of operators and study directors involved in qNMR testing. To assure that only qualified personnel are used for sample preparation at our GxP-accredited laboratory, weighing test was proposed. Sixteen participants performed six-fold weighing of the binary mixture of dibutylated hydroxytoluene (BHT) and 1,2,4,5-tetrachloro-3-nitrobenzene (TCNB). To evaluate the quality of data analysis, all spectra were evaluated manually by a qNMR expert and using in-house developed automated routine. The results revealed that mean values are comparable and both evaluation approaches are free of systematic error. However, automated evaluation resulted in an approximately 20% increase in precision. The same findings were revealed for qNMR analysis of 32 compounds used in pharmaceutical industry. Weighing test by six-fold determination in binary mixtures and automated qNMR methodology can be recommended as efficient tools for evaluating staff proficiency. The automated qNMR method significantly increases throughput and precision of qNMR for routine measurements and extends application scope of qNMR.
The treatment method to deactivate viable microorganisms from objects or products is termed sterilization. There are multiple forms of sterilization, each intended to be applied for a specific target, which depends on—but not limited to—the thermal, physical, and chemical stability of that target. Herein, an overview on the currently used sterilization processes in the global market is provided. Different sterilization techniques are grouped under a category that describes the method of treatment: radiation (gamma, electron beam, X-ray, and ultraviolet), thermal (dry and moist heat), and chemical (ethylene oxide, ozone, chlorine dioxide, and hydrogen peroxide). For each sterilization process, the typical process parameters as defined by regulations and the mode of antimicrobial activity are summarized. Finally, the recommended microorganisms that are used as biological indicators to validate sterilization processes in accordance with the rules that are established by various regulatory agencies are summarized.
An acetoin biosensor based on a capacitive electrolyte–insulator–semiconductor (EIS) structure modified with the enzyme acetoin reductase, also known as butane-2,3-diol dehydrogenase (Bacillus clausii DSM 8716ᵀ), is applied for acetoin detection in beer, red wine, and fermentation broth samples for the first time. The EIS sensor consists of an Al/p-Si/SiO₂/Ta₂O₅ layer structure with immobilized acetoin reductase on top of the Ta₂O₅ transducer layer by means of crosslinking via glutaraldehyde. The unmodified and enzyme-modified sensors are electrochemically characterized by means of leakage current, capacitance–voltage, and constant capacitance methods, respectively.
This paper compares several blade element theory (BET) method-based propeller simulation tools, including an evaluation against static propeller ground tests and high-fidelity Reynolds-Average Navier Stokes (RANS) simulations. Two proprietary propeller geometries for paraglider applications are analysed in static and flight conditions. The RANS simulations are validated with the static test data and used as a reference for comparing the BET in flight conditions. The comparison includes the analysis of varying 2D aerodynamic airfoil parameters and different induced velocity calculation methods. The evaluation of the BET propeller simulation tools shows the strength of the BET tools compared to RANS simulations. The RANS simulations underpredict static experimental data within 10% relative error, while appropriate BET tools overpredict the RANS results by 15–20% relative error. A variation in 2D aerodynamic data depicts the need for highly accurate 2D data for accurate BET results. The nonlinear BET coupled with XFOIL for the 2D aerodynamic data matches best with RANS in static operation and flight conditions. The novel BET tool PropCODE combines both approaches and offers further correction models for highly accurate static and flight condition results.
In traditional microbial biobutanol production, the solvent must be recovered during fermentation process for a sufficient space-time yield. Thermal separation is not feasible due to the boiling point of n-butanol. As an integrated and selective solid-liquid separation alternative, solvent impregnated resins (SIRs) were applied. Two polymeric resins were evaluated and an extractant screening was conducted. Vacuum application with vapor collection in fixed-bed column as bioreactor bypass was successfully implemented as butanol desorption step. In course of further increasing process economics, fermentation with renewable lignocellulosic substrates was conducted using Clostridium acetobutylicum. Utilization of SIR was shown to be a potential strategy for solvent removal from fermentation broth, while application of a bypass column allows for product removal and recovery at once.
In this chapter, the key technologies and the instrumentation required for the subsurface exploration of ocean worlds are discussed. The focus is laid on Jupiter’s moon Europa and Saturn’s moon Enceladus because they have the highest potential for such missions in the near future. The exploration of their oceans requires landing on the surface, penetrating the thick ice shell with an ice-penetrating probe, and probably diving with an underwater vehicle through dozens of kilometers of water to the ocean floor, to have the chance to find life, if it exists. Technologically, such missions are extremely challenging. The required key technologies include power generation, communications, pressure resistance, radiation hardness, corrosion protection, navigation, miniaturization, autonomy, and sterilization and cleaning. Simpler mission concepts involve impactors and penetrators or – in the case of Enceladus – plume-fly-through missions.
For short take-off and landing (STOL) aircraft, a parallel hybrid-electric propulsion system potentially offers superior performance compared to a conventional propulsion system, because the short-take-off power requirement is much higher than the cruise power requirement. This power-matching problem can be solved with a balanced hybrid propulsion system. However, there is a trade-off between wing loading, power loading, the level of hybridization, as well as range and take-off distance. An optimization method can vary design variables in such a way that a minimum of a particular objective is attained. In this paper, a comparison between the optimization results for minimum mass, minimum consumed primary energy, and minimum cost is conducted. A new initial sizing algorithm for general aviation aircraft with hybrid-electric propulsion systems is applied. This initial sizing methodology covers point performance, mission performance analysis, the weight estimation process, and cost estimation. The methodology is applied to the design of a STOL general aviation aircraft, intended for on-demand air mobility operations. The aircraft is sized to carry eight passengers over a distance of 500 km, while able to take off and land from short airstrips. Results indicate that parallel hybrid-electric propulsion systems must be considered for future STOL aircraft.
A light-addressable potentiometric sensor (LAPS) is a field-effect-based (bio-) chemical sensor, in which a desired sensing area on the sensor surface can be defined by illumination. Light addressability can be used to visualize the concentration and spatial distribution of the target molecules, e.g., H+ ions. This unique feature has great potential for the label-free imaging of the metabolic activity of living organisms. The cultivation of those organisms needs specially tailored surface properties of the sensor. O2 plasma treatment is an attractive and promising tool for rapid surface engineering. However, the potential impacts of the technique are carefully investigated for the sensors that suffer from plasma-induced damage. Herein, a LAPS with a Ta2O5 pH-sensitive surface is successfully patterned by plasma treatment, and its effects are investigated by contact angle and scanning LAPS measurements. The plasma duration of 30 s (30 W) is found to be the threshold value, where excessive wettability begins. Furthermore, this treatment approach causes moderate plasma-induced damage, which can be reduced by thermal annealing (10 min at 300 °C). These findings provide a useful guideline to support future studies, where the LAPS surface is desired to be more hydrophilic by O2 plasma treatment.