Refine
Year of publication
Institute
- Fachbereich Medizintechnik und Technomathematik (1564)
- Fachbereich Elektrotechnik und Informationstechnik (711)
- IfB - Institut für Bioengineering (563)
- Fachbereich Energietechnik (561)
- Fachbereich Chemie und Biotechnologie (537)
- INB - Institut für Nano- und Biotechnologien (533)
- Fachbereich Luft- und Raumfahrttechnik (480)
- Fachbereich Maschinenbau und Mechatronik (262)
- Fachbereich Wirtschaftswissenschaften (205)
- Solar-Institut Jülich (161)
Has Fulltext
- no (4685) (remove)
Language
- English (4685) (remove)
Document Type
- Article (3193)
- Conference Proceeding (1033)
- Part of a Book (191)
- Book (144)
- Doctoral Thesis (30)
- Conference: Meeting Abstract (28)
- Patent (25)
- Other (10)
- Report (9)
- Conference Poster (5)
Keywords
- Gamification (6)
- avalanche (6)
- Earthquake (5)
- Enterprise Architecture (5)
- MINLP (5)
- solar sail (5)
- Additive manufacturing (4)
- Diversity Management (4)
- Energy storage (4)
- Engineering optimization (4)
Field-effect capacitive electrolyte-insulator-semiconductor (EIS) sensors functionalised with citrate-capped gold nanoparticles (AuNP) have been used for the electrostatic detection of macromolecules by their intrinsic molecular charge. The EIS sensor detects the charge changes in the AuNP/macromolecule hybrids induced by the adsorption or binding events. A feasibility of the proposed detection scheme has been exemplary demonstrated by realising EIS sensors for the detection of poly-D-lysine molecules.
The artificial olfactory image was proposed by Lundström et al. in 1991 as a new strategy for an electronic nose system which generated a two-dimensional mapping to be interpreted as a fingerprint of the detected gas species. The potential distribution generated by the catalytic metals integrated into a semiconductor field-effect structure was read as a photocurrent signal generated by scanning light pulses. The impact of the proposed technology spread beyond gas sensing, inspiring the development of various imaging modalities based on the light addressing of field-effect structures to obtain spatial maps of pH distribution, ions, molecules, and impedance, and these modalities have been applied in both biological and non-biological systems. These light-addressing technologies have been further developed to realize the position control of a faradaic current on the electrode surface for localized electrochemical reactions and amperometric measurements, as well as the actuation of liquids in microfluidic devices.
Coronavirus disease 2019 (COVID-19) is a novel human infectious disease provoked by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Currently, no specific vaccines or drugs against COVID-19 are available. Therefore, early diagnosis and treatment are essential in order to slow the virus spread and to contain the disease outbreak. Hence, new diagnostic tests and devices for virus detection in clinical samples that are faster, more accurate and reliable, easier and cost-efficient than existing ones are needed. Due to the small sizes, fast response time, label-free operation without the need for expensive and time-consuming labeling steps, the possibility of real-time and multiplexed measurements, robustness and portability (point-of-care and on-site testing), biosensors based on semiconductor field-effect devices (FEDs) are one of the most attractive platforms for an electrical detection of charged biomolecules and bioparticles by their intrinsic charge. In this review, recent advances and key developments in the field of label-free detection of viruses (including plant viruses) with various types of FEDs are presented. In recent years, however, certain plant viruses have also attracted additional interest for biosensor layouts: Their repetitive protein subunits arranged at nanometric spacing can be employed for coupling functional molecules. If used as adapters on sensor chip surfaces, they allow an efficient immobilization of analyte-specific recognition and detector elements such as antibodies and enzymes at highest surface densities. The display on plant viral bionanoparticles may also lead to long-time stabilization of sensor molecules upon repeated uses and has the potential to increase sensor performance substantially, compared to conventional layouts. This has been demonstrated in different proof-of-concept biosensor devices. Therefore, richly available plant viral particles, non-pathogenic for animals or humans, might gain novel importance if applied in receptor layers of FEDs. These perspectives are explained and discussed with regard to future detection strategies for COVID-19 and related viral diseases.
A light-addressable potentiometric sensor (LAPS) can measure the concentration of one or several analytes at the sensor surface simultaneously in a spatially resolved manner. A modulated light pointer stimulates the semiconductor structure at the area of interest and a responding photocurrent can be read out. By simultaneous stimulation of several areas with light pointers of different modulation frequencies, the read out can be performed at the same time. With the new proposed controller electronic based on a field-programmable gate array (FPGA), it is possible to control the modulation frequencies, phase shifts, and light brightness of multiple light pointers independently and simultaneously. Thus, it is possible to investigate the frequency response of the sensor, and to examine the analyte concentration by the determination of the surface potential with the help of current/voltage curves and phase/voltage curves. Additionally, the ability to individually change the light intensities of each light pointer is used to perform signal correction.
Melting probes are a proven tool for the exploration of thick ice layers and clean sampling of subglacial water on Earth. Their compact size and ease of operation also make them a key technology for the future exploration of icy moons in our Solar System, most prominently Europa and Enceladus. For both mission planning and hardware engineering, metrics such as efficiency and expected performance in terms of achievable speed, power requirements, and necessary heating power have to be known.
Theoretical studies aim at describing thermal losses on the one hand, while laboratory experiments and field tests allow an empirical investigation of the true performance on the other hand. To investigate the practical value of a performance model for the operational performance in extraterrestrial environments, we first contrast measured data from terrestrial field tests on temperate and polythermal glaciers with results from basic heat loss models and a melt trajectory model. For this purpose, we propose conventions for the determination of two different efficiencies that can be applied to both measured data and models. One definition of efficiency is related to the melting head only, while the other definition considers the melting probe as a whole. We also present methods to combine several sources of heat loss for probes with a circular cross-section, and to translate the geometry of probes with a non-circular cross-section to analyse them in the same way. The models were selected in a way that minimizes the need to make assumptions about unknown parameters of the probe or the ice environment.
The results indicate that currently used models do not yet reliably reproduce the performance of a probe under realistic conditions. Melting velocities and efficiencies are constantly overestimated by 15 to 50 % in the models, but qualitatively agree with the field test data. Hence, losses are observed, that are not yet covered and quantified by the available loss models. We find that the deviation increases with decreasing ice temperature. We suspect that this mismatch is mainly due to the too restrictive idealization of the probe model and the fact that the probe was not operated in an efficiency-optimized manner during the field tests. With respect to space mission engineering, we find that performance and efficiency models must be used with caution in unknown ice environments, as various ice parameters have a significant effect on the melting process. Some of these are difficult to estimate from afar.
In order to maximize the possible travel distance of battery electric vehicles with one battery charge, it is mandatory to adjust all components of the powertrain carefully to each other. While current vehicle designs mostly simplify the powertrain rigorously and use an electric motor in combination with a gearbox with only one fixed transmission ratio, the use of multi-gear systems has great potential. First, a multi-speed system is able to improve the overall energy efficiency. Secondly, it is able to reduce the maximum momentum and therefore to reduce the maximum current provided by the traction battery, which results in a longer battery lifetime. In this paper, we present a systematic way to generate multi-gear gearbox designs that—combined with a certain electric motor—lead to the most efficient fulfillment of predefined load scenarios and are at the same time robust to uncertainties in the load. Therefore, we model the electric motor and the gearbox within a Mixed-Integer Nonlinear Program, and optimize the efficiency of the mechanical parts of the powertrain. By combining this mathematical optimization program with an unsupervised machine learning algorithm, we are able to derive global-optimal gearbox designs for practically relevant momentum and speed requirements.
The benefits of robotic process automation (RPA) are highly related to the usage of commercial off-the-shelf (COTS) software products that can be easily implemented and customized by business units. But, how to find the best fitting RPA product for a specific situation that creates the expected benefits? This question is related to the general area of software evaluation and selection. In the face of more than 75 RPA products currently on the market, guidance considering those specifics is required. Therefore, this chapter proposes a criteria-based selection method specifically for RPA. The method includes a quantitative evaluation of costs and benefits as well as a qualitative utility analysis based on functional criteria. By using the visualization of financial implications (VOFI) method, an application-oriented structure is provided that opposes the total cost of ownership to the time savings times salary (TSTS). For the utility analysis a detailed list of functional criteria for RPA is offered. The whole method is based on a multi-vocal review of scientific and non-scholarly literature including publications by business practitioners, consultants, and vendors. The application of the method is illustrated by a concrete RPA example. The illustrated
structures, templates, and criteria can be directly utilized by practitioners in their real-life RPA implementations. In addition, a normative decision process for selecting RPA alternatives is proposed before the chapter closes with a discussion and outlook.
The impact of surgical staplers on tissues has been studied mostly in an empirical manner. In this paper, finite element method was used to clarify the mechanics of tissue stapling and associated phenomena. Various stapling modalities and several designs of circular staplers were investigated to evaluate the impact of the device on tissues and mechanical performance of the end-to-end colorectal anastomosis. Numerical simulations demonstrated that a single row of staples is not adequate to resist leakage due to non-linear buckling and opening of the tissue layers between two adjacent staples. Compared to the single staple row configuration, significant increase in stress experienced by the tissue at the inner staple rows was observed in two and three rows designs. On the other hand, adding second and/or third staple row had no effect on strain in the tissue inside the staples. Variable height design with higher staples in outer rows significantly reduced the stresses and strains in outer rows when compared to the same configuration with flat cartridge.