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Due to their anion exchange characteristics, layered double hydroxides (LDHs) are suitable for the detoxification of aqueous, fatty acid containing fermentation substrates. The aim of this study is to examine the adsorption mechanism, using crude glycerol from plant oil esterification as a model system. Changes in the intercalation structure in relation to the amount of fatty acids adsorbed are monitored by X-ray diffraction and infra-red spectroscopy. Additionally, calcination of LDH is investigated in order to increase the binding capacity for fatty acids. Our data propose that, at ambient temperature, fatty acids can be bound to the hydrotalcite by adsorption or in addition by intercalation, depending on fatty acid concentration. The adsorption of fatty acids from crude glycerol shows a BET-like behavior. Above a fatty acid concentration of 3.5 g L−1, intercalation of fatty acids can be shown by the appearance of an increased interlayer spacing. This observation suggests a two phase adsorption process. Calcination of LDHs allows increasing the binding capacity for fatty acids by more than six times, mainly by reduction of structural CO32−.
In der Molkeverarbeitung dominieren Membranfiltrationsverfahren die Prozessführung. Hierbei werden üblicherweise Aufkonzentrierungen der Proteine und deren Trennung von dem Milchzucker Lactose durchgeführt. Der Prozess der adsorptiven Aufreinigung soll als kostengünstige Alternative zu den bisher gebräuchlichen Verfahren dienen. Weiterhin eröffnet sich durch das Verfahren die Möglichkeit, einzelne Proteinfraktionen während der Verarbeitung anzureichern. Als Proteinquellen wurden für die Untersuchungen Modellproteine, Lösungen aus Molkenproteinisolat, Dünnmolke und Molkekonzentrat verwendet. Die Eignung zur Proteinbindung wurden an Tonmaterialien, Silicaten und y-Aluminiumoxiden in Pulverform, in Form von Granulaten sowie Extrudaten als auch sphärischen Partikeln überprüft. Adsorbentien aus Bentonit/Silica und c-Aluminiumoxid können sowohl a-Lactalbumin (aLA) als auch b-Lactoglobulin (bLG) binden, wohingegen Materialien aus Siliciumoxid lediglich ein starkes Adsorptionsverhalten gegenüber bLG zeigen. Mischmaterialien aus Siliciumoxid und a-Aluminiumoxid zeigen dasselbe Verhalten wie Materialien aus Siliciumoxid, weisen jedoch eine geringere Kapazität auf. Die Materialen wurden hinsichtlich ihres Einsatzes in chromatographischen Verfahren und Batch-Prozessen untersucht und ein Prozessentwurf für einen zweistufigen Batch-Prozess im Rührkessel erarbeitet.
Adsorptive Vorbehandlung von Rohglycerin für die 1,3-Propandiol Fermentation mit Clostridium diolis
(2014)
Bei der Gewinnung von Fettsäuren aus Pflanzenölen, z. B. zur Herstellung von Biopolymeren, oder bei der Biodiesel- und Seifenproduktion, fällt Glycerin als Nebenprodukt an. Bei der Biokonversion dieses Rohstoffes zu 1,3-Propandiol wird der Produktionsorganismus Clostridium diolis durch Verunreinigungen im Rohglycerin gehemmt. Als inhibierende Substanzen konnten freie Fettsäuren identifiziert werden. Mithilfe eines adsorptiven Aufarbeitungsverfahrens ist es gelungen, die Fettsäuren zu entfernen und die Konversionseffizienz zu 1,3-Propandiol zu erhöhen.
To fulfil the CO2 emission reduction targets of the European Union (EU), heavy-duty (HD) trucks need to operate 15% more efficiently by 2025 and 30% by 2030. Their electrification is necessary as conventional HD trucks are already optimized for the long-haul application. The resulting hybrid electric vehicle (HEV) truck gains most of the fuel saving potential by the recuperation of potential energy and its consecutive utilization. The key to utilizing the full potential of HEV-HD trucks is to maximize the amount of recuperated energy and ensure its intelligent usage while keeping the operating point of the internal combustion engine as efficient as possible. To achieve this goal, an intelligent energy management strategy (EMS) based on ECMS is developed for a parallel HEV-HD truck which uses predictive discharge of the battery and adaptive operating strategy regarding the height profile and the vehicle mass. The presented EMS can reproduce the global optimal operating strategy over long phases and lead to a fuel saving potential of up to 2% compared with a heuristic strategy. Furthermore, the fuel saving potential is correlated with the investigated boundary conditions to deepen the understanding of the impact of intelligent EMS for HEV-HD trucks.
Traditional vulcanization mold manufacturing is complex, costly, and under pressure due to shorter product lifecycles and diverse variations. Additive manufacturing using Fused Filament Fabrication and high-performance polymers like PEEK offer a promising future in this industry. This study assesses the compressive strength of various infill structures (honeycomb, grid, triangle, cubic, and gyroid) when considering two distinct build directions (Z, XY) to enhance PEEK’s economic and resource efficiency in rapid tooling. A comparison with PETG samples shows the behavior of the infill strategies. Additionally, a proof of concept illustrates the application of a PEEK mold in vulcanization. A peak compressive strength of 135.6 MPa was attained in specimens that were 100% solid and subjected to thermal post-treatment. This corresponds to a 20% strength improvement in the Z direction. In terms of time and mechanical properties, the anisotropic grid and isotropic cubic infill have emerged for use in rapid tooling. Furthermore, the study highlights that reducing the layer thickness from 0.15 mm to 0.1 mm can result in a 15% strength increase. The study unveils the successful utilization of a room-temperature FFF-printed PEEK mold in vulcanization injection molding. The parameters and infill strategies identified in this research enable the resource-efficient FFF printing of PEEK without compromising its strength properties. Using PEEK in rapid tooling allows a cost reduction of up to 70% in tool production.
Advanced on-wafer multiport calibration methods for mono- and mixed-mode device characterization
(2004)
Sleep spindles are neurophysiological phenomena that appear to be linked to memory formation and other functions of the central nervous system, and that can be observed in electroencephalographic recordings (EEG) during sleep. Manually identified spindle annotations in EEG recordings suffer from substantial intra- and inter-rater variability, even if raters have been highly trained, which reduces the reliability of spindle measures as a research and diagnostic tool. The Massive Online Data Annotation (MODA) project has recently addressed this problem by forming a consensus from multiple such rating experts, thus providing a corpus of spindle annotations of enhanced quality. Based on this dataset, we present a U-Net-type deep neural network model to automatically detect sleep spindles. Our model’s performance exceeds that of the state-of-the-art detector and of most experts in the MODA dataset. We observed improved detection accuracy in subjects of all ages, including older individuals whose spindles are particularly challenging to detect reliably. Our results underline the potential of automated methods to do repetitive cumbersome tasks with super-human performance.