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Frequency mixing magnetic detection (FMMD) has been widely utilized as a measurement technique in magnetic immunoassays. It can also be used for the characterization and distinction (also known as “colourization”) of different types of magnetic nanoparticles (MNPs) based on their core sizes. In a previous work, it was shown that the large particles contribute most of the FMMD signal. This leads to ambiguities in core size determination from fitting since the contribution of the small-sized particles is almost undetectable among the strong responses from the large ones. In this work, we report on how this ambiguity can be overcome by modelling the signal intensity using the Langevin model in thermodynamic equilibrium including a lognormal core size distribution fL(dc,d0,σ) fitted to experimentally measured FMMD data of immobilized MNPs. For each given median diameter d0, an ambiguous amount of best-fitting pairs of parameters distribution width σ and number of particles Np with R2 > 0.99 are extracted. By determining the samples’ total iron mass, mFe, with inductively coupled plasma optical emission spectrometry (ICP-OES), we are then able to identify the one specific best-fitting pair (σ, Np) one uniquely. With this additional externally measured parameter, we resolved the ambiguity in core size distribution and determined the parameters (d0, σ, Np) directly from FMMD measurements, allowing precise MNPs sample characterization.
An alternative method is presented to numerically compute interior elastic transmission eigenvalues for various domains in two dimensions. This is achieved by discretizing the resulting system of boundary integral equations in combination with a nonlinear eigenvalue solver. Numerical results are given to show that this new approach can provide better results than the finite element method when dealing with general domains.
Frequency mixing magnetic detection (FMMD) has been explored for its applications in fields of magnetic biosensing, multiplex detection of magnetic nanoparticles (MNP) and the determination of core size distribution of MNP samples. Such applications rely on the application of a static offset magnetic field, which is generated traditionally with an electromagnet. Such a setup requires a current source, as well as passive or active cooling strategies, which directly sets a limitation based on the portability aspect that is desired for point of care (POC) monitoring applications. In this work, a measurement head is introduced that involves the utilization of two ring-shaped permanent magnets to generate a static offset magnetic field. A steel cylinder in the ring bores homogenizes the field. By variation of the distance between the ring magnets and of the thickness of the steel cylinder, the magnitude of the magnetic field at the sample position can be adjusted. Furthermore, the measurement setup is compared to the electromagnet offset module based on measured signals and temperature behavior.
Miniaturized electrolyte–insulator–semiconductor capacitors (EISCAPs) with ultrathin gate insulators have been studied in terms of their pH-sensitive sensor characteristics: three different EISCAP systems consisting of Al–p-Si–Ta2O5(5 nm), Al–p-Si–Si3N4(1 or 2 nm)–Ta2O5 (5 nm), and Al–p-Si–SiO2(3.6 nm)–Ta2O5(5 nm) layer structures are characterized in buffer solution with different pH values by means of capacitance–voltage and constant capacitance method. The SiO2 and Si3N4 gate insulators are deposited by rapid thermal oxidation and rapid thermal nitridation, respectively, whereas the Ta2O5 film is prepared by atomic layer deposition. All EISCAP systems have a clear pH response, favoring the stacked gate insulators SiO2–Ta2O5 when considering the overall sensor characteristics, while the Si3N4(1 nm)–Ta2O5 stack delivers the largest accumulation capacitance (due to the lower equivalent oxide thickness) and a higher steepness in the slope of the capacitance–voltage curve among the studied stacked gate insulator systems.
Utilizing an appropriate enzyme immobilization strategy is crucial for designing enzyme-based biosensors. Plant virus-like particles represent ideal nanoscaffolds for an extremely dense and precise immobilization of enzymes, due to their regular shape, high surface-to-volume ratio and high density of surface binding sites. In the present work, tobacco mosaic virus (TMV) particles were applied for the co-immobilization of penicillinase and urease onto the gate surface of a field-effect electrolyte-insulator-semiconductor capacitor (EISCAP) with a p-Si-SiO₂-Ta₂O₅ layer structure for the sequential detection of penicillin and urea. The TMV-assisted bi-enzyme EISCAP biosensor exhibited a high urea and penicillin sensitivity of 54 and 85 mV/dec, respectively, in the concentration range of 0.1–3 mM. For comparison, the characteristics of single-enzyme EISCAP biosensors modified with TMV particles immobilized with either penicillinase or urease were also investigated. The surface morphology of the TMV-modified Ta₂O₅-gate was analyzed by scanning electron microscopy. Additionally, the bi-enzyme EISCAP was applied to mimic an XOR (Exclusive OR) enzyme logic gate.
Concentrating solar power
(2022)
The focus of this chapter is the production of power and the use of the heat produced from concentrated solar thermal power (CSP) systems.
The chapter starts with the general theoretical principles of concentrating systems including the description of the concentration ratio, the energy and mass balance. The power conversion systems is the main part where solar-only operation and the increase in operational hours.
Solar-only operation include the use of steam turbines, gas turbines, organic Rankine cycles and solar dishes. The operational hours can be increased with hybridization and with storage.
Another important topic is the cogeneration where solar cooling, desalination and of heat usage is described.
Many examples of commercial CSP power plants as well as research facilities from the past as well as current installed and in operation are described in detail.
The chapter closes with economic and environmental aspects and with the future potential of the development of CSP around the world.
Biomedical applications of magnetic nanoparticles (MNP) fundamentally rely on the particles’ magnetic relaxation as a response to an alternating magnetic field. The magnetic relaxation complexly depends on the interplay of MNP magnetic and physical properties with the applied field parameters. It is commonly accepted that particle core size is a major contributor to signal generation in all the above applications, however, most MNP samples comprise broad distribution spanning nm and more. Therefore, precise knowledge of the exact contribution of individual core sizes to signal generation is desired for optimal MNP design generally for each application. Specifically, we present a magnetic relaxation simulation-driven analysis of experimental frequency mixing magnetic detection (FMMD) for biosensing to quantify the contributions of individual core size fractions towards signal generation. Applying our method to two different experimental MNP systems, we found the most dominant contributions from approx. 20 nm sized particles in the two independent MNP systems. Additional comparison between freely suspended and immobilized MNP also reveals insight in the MNP microstructure, allowing to use FMMD for MNP characterization, as well as to further fine-tune its applicability in biosensing.
REM sleep without atonia (RSWA) is a key feature for the diagnosis of rapid eye movement (REM) sleep behaviour disorder (RBD). We introduce RBDtector, a novel open-source software to score RSWA according to established SINBAR visual scoring criteria. We assessed muscle activity of the mentalis, flexor digitorum superficialis (FDS), and anterior tibialis (AT) muscles. RSWA was scored manually as tonic, phasic, and any activity by human scorers as well as using RBDtector in 20 subjects. Subsequently, 174 subjects (72 without RBD and 102 with RBD) were analysed with RBDtector to show the algorithm’s applicability. We additionally compared RBDtector estimates to a previously published dataset. RBDtector showed robust conformity with human scorings. The highest congruency was achieved for phasic and any activity of the FDS. Combining mentalis any and FDS any, RBDtector identified RBD subjects with 100% specificity and 96% sensitivity applying a cut-off of 20.6%. Comparable performance was obtained without manual artefact removal. RBD subjects also showed muscle bouts of higher amplitude and longer duration. RBDtector provides estimates of tonic, phasic, and any activity comparable to human scorings. RBDtector, which is freely available, can help identify RBD subjects and provides reliable RSWA metrics.
Next Generation Manufacturing promises significant improvements in performance, productivity, and value creation. In addition to the desired and projected improvements regarding the planning, production, and usage cycles of products, this digital transformation will have a huge impact on work, workers, and workplace design. Given the high uncertainty in the likelihood of occurrence and the technical, economic, and societal impacts of these changes, we conducted a technology foresight study, in the form of a real-time Delphi analysis, to derive reliable future scenarios featuring the next generation of manufacturing systems. This chapter presents the organization dimension and describes each projection in detail, offering current case study examples and discussing related research, as well as implications for policy makers and firms. Specifically, we highlight seven areas in which the digital transformation of production will change how we work, how we organize the work within a company, how we evaluate these changes, and how employment and labor rights will be affected across company boundaries. The experts are unsure whether the use of collaborative robots in factories will replace traditional robots by 2030. They believe that the use of hybrid intelligence will supplement human decision-making processes in production environments. Furthermore, they predict that artificial intelligence will lead to changes in management processes, leadership, and the elimination of hierarchies. However, to ensure that social and normative aspects are incorporated into the AI algorithms, restricting measurement of individual performance will be necessary. Additionally, AI-based decision support can significantly contribute toward new, socially accepted modes of leadership. Finally, the experts believe that there will be a reduction in the workforce by the year 2030.
Hydrogen is playing an increasingly important role in research and politics as an energy carrier of the future. Since hydrogen has commonly been produced from methane by steam reforming, the need for climate-friendly, alternative production routes is emerging. In addition to electrolysis, fermentative routes for the production of so-called biohydrogen are "green" alternatives. The application of microorganisms offers the advantage of sustainable production from renewable resources using easily manageable technologies. In this project, the hyperthermophilic, anaerobic microorganism Thermotoga neapolitana is used for the productio nof biohydrogen from renewable resources. The enzymatically hydrolyzed resources were used in fermentation leading to yield coefficients of 1.8 mole H₂ per mole glucose when using hydrolyzed straw and ryegrass supplemented with medium, respectively. These results are similar to the hydrogen yields when using Thermotoga basal medium with glucose (TBGY) as control group. In order to minimize the supplementation of the hydrolysate and thus increase the economic efficiency of the process, the essential media components were identified. The experiments revealed NaCl, KCl, and glucose as essential components for cell growth as well as biohydrogen production. When excluding NaCl, a decrease of 96% in hydrogen production occured.
Upcoming gasoline engines should run with a larger number of fuels beginning from petrol over methanol up to gas by a wide range of compression ratios and a homogeneous charge. In this article, the microwave (MW) spark plug, based on a high-speed frequency hopping system, is introduced as a solution, which can support a nitrogen compression ratio up to 1:39 in a chamber and more. First, an overview of the high-speed frequency hopping MW ignition and operation system as well as the large number of applications are presented. Both gives an understanding of this new base technology for MW plasma generation. Focus of the theoretical part is the explanation of the internal construction of the spark plug, on the achievable of the high voltage generation as well as the high efficiency to hold the plasma. In detail, the development process starting with circuit simulations and ending with the numerical multiphysics field simulations is described. The concept is evaluated with a reference prototype covering the frequency range between 2.40 and 2.48 GHz and working over a large power range from 20 to 200 W. A larger number of different measurements starting by vector hot-S11 measurements and ending by combined working scenarios out of hot temperature, high pressure and charge motion are winding up the article. The limits for the successful pressure tests were given by the pressure chamber. Pressures ranged from 1 to 39 bar and charge motion up to 25 m/s as well as temperatures from 30◦ to 125◦.
In order to reduce energy consumption of homes, it is important to make transparent which devices consume how much energy. However, power consumption is often only monitored aggregated at the house energy meter. Disaggregating this power consumption into the contributions of individual devices can be achieved using Machine Learning. Our work aims at making state of the art disaggregation algorithms accessibe for users of the open source home automation platform Home Assistant.
Today’s society is undergoing a paradigm shift driven by the megatrend of sustainability. This undeniably affects all areas of Western life. This paper aims to find out how the luxury industry is dealing with this change and what adjustments are made by the companies. For this purpose, interviews were conducted with managers from the luxury industry, in which they were asked about specific measures taken by their companies as well as trends in the industry. In a subsequent evaluation, the trends in the luxury industry were summarized for the areas of ecological, social, and economic sustainability. It was found that the area of environmental sustainability is significantly more focused than the other sub-areas. Furthermore, the need for a customer survey to validate the industry-based measures was identified.
Amino acid-based surfactants are valuable compounds for cosmetic formulations. The chemical synthesis of acyl-amino acids is conventionally performed by the Schotten-Baumann reaction using fatty acyl chlorides, but aminoacylases have also been investigated for use in biocatalytic synthesis with free fatty acids. Aminoacylases and their properties are diverse; they belong to different peptidase families and show differences in substrate specificity and biocatalytic potential. Bacterial aminoacylases capable of synthesis have been isolated from Burkholderia, Mycolicibacterium, and Streptomyces. Although several proteases and peptidases from S. griseus have been described, no aminoacylases from this species have been identified yet. In this study, we investigated two novel enzymes produced by S. griseus DSM 40236ᵀ . We identified and cloned the respective genes and recombinantly expressed an α-aminoacylase (EC 3.5.1.14), designated SgAA, and an ε-lysine acylase (EC 3.5.1.17), designated SgELA, in S. lividans TK23. The purified aminoacylase SgAA was biochemically characterized, focusing on its hydrolytic activity to determine temperature- and pH optima and stabilities. The aminoacylase could hydrolyze various acetyl-amino acids at the Nα -position with a broad specificity regarding the sidechain. Substrates with longer acyl chains, like lauroyl-amino acids, were hydrolyzed to a lesser extent. Purified aminoacylase SgELA specific for the hydrolysis of Nε -acetyl-L-lysine was unstable and lost its enzymatic activity upon storage for a longer period but could initially be characterized. The pH optimum of SgELA was pH 8.0. While synthesis of acyl-amino acids was not observed with SgELA, SgAA catalyzed the synthesis of lauroyl-methionine.
This thesis aims at the presentation and discussion of well-accepted and new
imaging techniques applied to different types of flow in common hydraulic
engineering environments. All studies are conducted in laboratory conditions and
focus on flow depth and velocity measurements. Investigated flows cover a wide
range of complexity, e.g. propagation of waves, dam-break flows, slightly and fully
aerated spillway flows as well as highly turbulent hydraulic jumps.
Newimagingmethods are compared to different types of sensorswhich are frequently
employed in contemporary laboratory studies. This classical instrumentation as well
as the general concept of hydraulic modeling is introduced to give an overview on
experimental methods.
Flow depths are commonly measured by means of ultrasonic sensors, also known as
acoustic displacement sensors. These sensors may provide accurate data with high
sample rates in case of simple flow conditions, e.g. low-turbulent clear water flows.
However, with increasing turbulence, higher uncertainty must be considered.
Moreover, ultrasonic sensors can provide point data only, while the relatively large
acoustic beam footprint may lead to another source of uncertainty in case of
relatively short, highly turbulent surface fluctuations (ripples) or free-surface
air-water flows. Analysis of turbulent length and time scales of surface fluctuations
from point measurements is also difficult. Imaging techniques with different
dimensionality, however, may close this gap. It is shown in this thesis that edge
detection methods (known from computer vision) may be used for two-dimensional
free-surface extraction (i.e. from images taken through transparant sidewalls in
laboratory flumes). Another opportunity in hydraulic laboratory studies comes with
the application of stereo vision. Low-cost RGB-D sensors can be used to gather
instantaneous, three-dimensional free-surface elevations, even in flows with very
high complexity (e.g. aerated hydraulic jumps). It will be shown that the uncertainty
of these methods is of similar order as for classical instruments.
Particle Image Velocimetry (PIV) is a well-accepted and widespread imaging
technique for velocity determination in laboratory conditions. In combination with
high-speed cameras, PIV can give time-resolved velocity fields in 2D/3D or even as
volumetric flow fields. PIV is based on a cross-correlation technique applied to small
subimages of seeded flows. The minimum size of these subimages defines the
maximum spatial resolution of resulting velocity fields. A derivative of PIV for
aerated flows is also available, i.e. the so-called Bubble Image Velocimetry (BIV). This
thesis emphasizes the capacities and limitations of both methods, using relatively
simple setups with halogen and LED illuminations. It will be demonstrated that
PIV/BIV images may also be processed by means of Optical Flow (OF) techniques.
OF is another method originating from the computer vision discipline, based on the
assumption of image brightness conservation within a sequence of images. The
Horn-Schunck approach, which has been first employed to hydraulic engineering
problems in the studies presented herein, yields dense velocity fields, i.e. pixelwise
velocity data. As discussed hereinafter, the accuracy of OF competes well with PIV
for clear-water flows and even improves results (compared to BIV) for aerated flow
conditions. In order to independently benchmark the OF approach, synthetic images
with defined turbulence intensitiy are used.
Computer vision offers new opportunities that may help to improve the
understanding of fluid mechanics and fluid-structure interactions in laboratory
investigations. In prototype environments, it can be employed for obstacle detection
(e.g. identification of potential fish migration corridors) and recognition (e.g. fish
species for monitoring in a fishway) or surface reconstruction (e.g. inspection of
hydraulic structures). It can thus be expected that applications to hydraulic
engineering problems will develop rapidly in near future. Current methods have not
been developed for fluids in motion. Systematic future developments are needed to
improve the results in such difficult conditions.
High aerodynamic efficiency requires propellers with high aspect ratios, while propeller sweep potentially reduces noise. Propeller sweep and high aspect ratios increase elasticity and coupling of structural mechanics and aerodynamics, affecting the propeller performance and noise. Therefore, this paper analyzes the influence of elasticity on forward-swept, backward-swept, and unswept propellers in hover conditions. A reduced-order blade element momentum approach is coupled with a one-dimensional Timoshenko beam theory and Farassat's formulation 1A. The results of the aeroelastic simulation are used as input for the aeroacoustic calculation. The analysis shows that elasticity influences noise radiation because thickness and loading noise respond differently to deformations. In the case of the backward-swept propeller, the location of the maximum sound pressure level shifts forward by 0.5 °, while in the case of the forward-swept propeller, it shifts backward by 0.5 °. Therefore, aeroacoustic optimization requires the consideration of propeller deformation.
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 this work, the effects of carbon sources and culture media on the production and structural properties of bacterial cellulose (BC) synthesized by Medusomyces gisevii have been studied. The culture medium was composed of different initial concentrations of glucose or sucrose dissolved in 0.4% extract of plain green tea. Parameters of the culture media (titratable acidity, substrate conversion degree etc.) were monitored daily for 20 days of cultivation. The BC pellicles produced on different carbon sources were characterized in terms of biomass yield, crystallinity and morphology by field emission scanning electron microscopy (FE-SEM), atomic force microscopy and X-ray diffraction. Our results showed that Medusomyces gisevii had higher BC yields in media with sugar concentrations close to 10 g L−1 after a 18–20 days incubation period. Glucose in general lead to a higher BC yield (173 g L−1) compared to sucrose (163.5 g L−1). The BC crystallinity degree and surface roughness were higher in the samples synthetized from sucrose. Obtained FE-SEM micrographs show that the BC pellicles synthesized in the sucrose media contained densely packed tangles of cellulose fibrils whereas the BC produced in the glucose media displayed rather linear geometry of the BC fibrils without noticeable aggregates.
Background
Aminoacylases are highly promising enzymes for the green synthesis of acyl-amino acids, potentially replacing the environmentally harmful Schotten-Baumann reaction. Long-chain acyl-amino acids can serve as strong surfactants and emulsifiers, with application in cosmetic industries. Heterologous expression of these enzymes, however, is often hampered, limiting their use in industrial processes.
Results
We identified a novel mycobacterial aminoacylase gene from Mycolicibacterium smegmatis MKD 8, cloned and expressed it in Escherichia coli and Vibrio natriegens using the T7 overexpression system. The recombinant enzyme was prone to aggregate as inclusion bodies, and while V. natriegens Vmax™ could produce soluble aminoacylase upon induction with isopropyl β-d-1-thiogalactopyranoside (IPTG), E. coli BL21 (DE3) needed autoinduction with lactose to produce soluble recombinant protein. We successfully conducted a chaperone co-expression study in both organisms to further enhance aminoacylase production and found that overexpression of chaperones GroEL/S enhanced aminoacylase activity in the cell-free extract 1.8-fold in V. natriegens and E. coli. Eventually, E. coli ArcticExpress™ (DE3), which co-expresses cold-adapted chaperonins Cpn60/10 from Oleispira antarctica, cultivated at 12 °C, rendered the most suitable expression system for this aminoacylase and exhibited twice the aminoacylase activity in the cell-free extract compared to E. coli BL21 (DE3) with GroEL/S co-expression at 20 °C. The purified aminoacylase was characterized based on hydrolytic activities, being most stable and active at pH 7.0, with a maximum activity at 70 °C, and stability at 40 °C and pH 7.0 for 5 days. The aminoacylase strongly prefers short-chain acyl-amino acids with smaller, hydrophobic amino acid residues. Several long-chain amino acids were fairly accepted in hydrolysis as well, especially N-lauroyl-L-methionine. To initially evaluate the relevance of this aminoacylase for the synthesis of N-acyl-amino acids, we demonstrated that lauroyl-methionine can be synthesized from lauric acid and methionine in an aqueous system.
Conclusion
Our results suggest that the recombinant enzyme is well suited for synthesis reactions and will thus be further investigated.