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Cyber-physical systems are ever more common in manufacturing industries. Increasing their autonomy has been declared an explicit goal, for example, as part of the Industry 4.0 vision. To achieve this system intelligence, principled and software-driven methods are required to analyze sensing data, make goal-directed decisions, and eventually execute and monitor chosen tasks. In this chapter, we present a number of knowledge-based approaches to these problems and case studies with in-depth evaluation results of several different implementations for groups of autonomous mobile robots performing in-house logistics in a smart factory. We focus on knowledge-based systems because besides providing expressive languages and capable reasoning techniques, they also allow for explaining how a particular sequence of actions came about, for example, in the case of a failure.
Detection of Adrenaline Based on Bioelectrocatalytical System to Support Tumor Diagnostic Technology
(2017)
The capacitive electrolyte–insulator–semiconductor (EIS) structure is a typical device based on a field-effect sensor platform. With a simple silicon-based structure, EIS have been useful for several sensing applications, especially with incorporation of nanostructured films to modulate the ionic transport and the flat-band potential. In this paper, we report on ion transport and changes in flat-band potential in EIS sensors made with layer-by-layer films containing poly(amidoamine) (PAMAM) dendrimer and single-walled carbon nanotubes (SWNTs) adsorbed on p-Si/SiO 2 /Ta 2 O 5 chips with an Al ohmic contact. The impedance spectra were fitted using an equivalent circuit model, from which we could determine parameters such as the double-layer capacitance. This capacitance decreased with the number of bilayers owing to space charge accumulated at the electrolyte–insulator interface, up to three PAMAM/SWNTs bilayers, after which it stabilized. The charge-transfer resistance was also minimum for three bilayers, thus indicating that this is the ideal architecture for an optimized EIS performance. The understanding of the influence of nanostructures and the fine control of operation parameters pave the way for optimizing the design and performance of new EIS sensors.
Three amperometric biosensors have been developed for the detection of L-malic acid, fumaric acid, and L -aspartic acid, all based on the combination of a malate-specific dehydrogenase (MDH, EC 1.1.1.37) and diaphorase (DIA, EC 1.8.1.4). The stepwise expansion of the malate platform with the enzymes fumarate hydratase (FH, EC 4.2.1.2) and aspartate ammonia-lyase (ASPA, EC 4.3.1.1) resulted in multi-enzyme reaction cascades and, thus, augmentation of the substrate spectrum of the sensors. Electrochemical measurements were carried out in presence of the cofactor β-nicotinamide adenine dinucleotide (NAD+) and the redox mediator hexacyanoferrate (III) (HCFIII). The amperometric detection is mediated by oxidation of hexacyanoferrate (II) (HCFII) at an applied potential of + 0.3 V vs. Ag/AgCl. For each biosensor, optimum working conditions were defined by adjustment of cofactor concentrations, buffer pH, and immobilization procedure. Under these improved conditions, amperometric responses were linear up to 3.0 mM for L-malate and fumarate, respectively, with a corresponding sensitivity of 0.7 μA mM−1 (L-malate biosensor) and 0.4 μA mM−1 (fumarate biosensor). The L-aspartate detection system displayed a linear range of 1.0–10.0 mM with a sensitivity of 0.09 μA mM−1. The sensor characteristics suggest that the developed platform provides a promising method for the detection and differentiation of the three substrates.
A light-addressable potentiometric sensor (LAPS) is a field-effect-based potentiometric sensor with an electrolyte/insulator/semiconductor (EIS) structure, which is able to monitor analyte concentrations of (bio-)chemical species in aqueous solutions in a spatially resolved way. Therefore, it is also an appropriate tool to record 2D-chemical images of concentration variations on the sensor surface. In the present work, two differential, LAPS-based measurement principles are introduced to determine the metabolic activity of Escherichia coli (E. coli) K12 and Chinese hamster ovary (CHO) cells as test microorganisms. Hereby, we focus on i) the determination of the extracellular acidification rate (ΔpH/min) after adding glucose solutions to the cell suspensions; and ii) recording the amplitude increase of the photocurrent (Iph) related to the produced acids from E. coli K12 bacteria and CHO cells on the sensor surface by 2D-chemical imaging. For this purpose, 3D-printed multi-chamber structures were developed and mounted on the planar sensor-chip surface to define four independent compartments, enabling differential measurements with varying cell concentrations. The differential concept allows eliminating unwanted drift effects and, with the four-chamber structures, measurements on the different cell concentrations were performed simultaneously, thus reducing also the overall measuring time.
Engineers and therefore engineering education are challenged by the increasing complexity of questions to be answered globally. The education of future engineers therefore has to answer with curriculums that build up relevant skills. This chapter will give an example how to bring engineering and social responsibility successful together to build engineers of tomorrow. Through the integration of gender and diversity perspectives, engineering research and teaching is expanded with new perspectives and contents providing an important potential for innovation. Aiming on the enhancement of engineering education with distinctive competencies beyond technical expertise, the teaching approach introduced in the chapter represents key factors to ensure that coming generations of engineers will be able to meet the requirements and challenges a changing globalized world holds for them. The chapter will describe how this approach successfully has been implemented in the curriculum in engineering of a leading technical university in Germany.