20005 research outputs found
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A novel method for quantifying enzyme immobilization in porous carriers using simple NMR relaxometry
No full textEnzyme immobilization plays a crucial role in enhancing the stability and recyclability of enzymes for industrial applications. However, traditional methods for quantifying enzyme loading within porous carriers are limited by time-consuming workflows, cumulative errors, and the inability to probe enzymes adsorbed inside the pores. In this study, we introduce Time-Domain Nuclear Magnetic Resonance (TD-NMR) relaxometry as a novel, non-invasive technique for directly quantifying enzyme adsorption within porous carriers. Focusing on epoxy methyl acrylate carriers, commonly used in biocatalysis, we correlate changes in T2 relaxation times with enzyme concentration, leading to the development of an NMR-based pore-filling ratio that quantifies enzyme loading. Validation experiments demonstrate that TD-NMR-derived adsorption curves align closely with traditional photometric measurements, offering a reliable and reproducible alternative for enzyme quantification. The accessibility of tabletop TD-NMR spectrometers makes this technique a practical and cost-effective tool for optimizing biocatalytic processes. Furthermore, the method holds promise for real-time monitoring of adsorption dynamics and could be adapted for a wider range of carrier materials and enzymes
The Role of Wind Velocity in Saline Water Evaporation from Porous Media and Surface Salt Crystallization Dynamics
No full textThis study systematically investigates the effect of wind flow on evaporation dynamics and salt crystallization patterns in porous media. Well-controlled experiments were conducted in a laboratory wind tunnel, where the surface of sand columns saturated with freshwater and NaCl solutions at concentrations of 10%, 15%, and 20% were subjected to wind flows of 0.5 and 5 m/s, corresponding to laminar and turbulent flow regimes, respectively. Mass loss measurements from the samples, combined with optical imaging of their surfaces, revealed distinct evaporation dynamics and crystallization patterns. We observed that the interaction between intermittent turbulent airflow and evolving salt crystals on the surface resulted in a relatively uniform crystallization pattern. In contrast, under laminar airflow conditions, salt crystal nucleation and formation primarily occurred at the leading edge of the sample, particularly at lower salt concentrations. We further investigated the impact of enhanced evaporative mass loss in the presence of wind on crystallization dynamics by quantifying the crystal coverage and its lateral extent on the surface. Under turbulent flow conditions, we observed that full coverage of the surface with salt crystals requires 2 to 3 times higher evaporative losses in 10% NaCl sample relative to the 15% and 20% samples, respectively. These findings highlight the complex interplay between evaporation and crystallization processes under varying airflow conditions, thus offering valuable insight for improving hydrological and climatological modeling
Surface roughness formation in powder bed fusion of copper using Gaussian and ring-shaped laser beam profiles
No full textBoth Gaussian laser beam profiles and ring-shaped laser beam profiles demonstrate the capability to produce specimens with a part density of more than 99.5% and an electrical conductivity above 99% compared to the International Annealed Copper Standard (IACS) in powder bed fusion of copper using a laser beam (PBF-LB/M/Cu). However, the influence of the beam profile on the surface roughness and surface characteristic has not yet been investigated in detail. In order to close this gap, inclined cube geometries are manufactured with varying laser power and scan speed using a Gaussian and ring-shaped beam profiles. The results reveal that the beam profile achieving the lowest surface roughness Sa in the investigated parameter range depends on the surface orientation. For the down-skin surfaces, the use of ring-shaped beam profiles reduces the surface roughness Sa by up to 29.9% compared to the Gaussian beam profile, whereas the surface roughness Sa for the up-skin and side surface is more than doubled. This is related to differences in surface roughness formation of the Gaussian and ring-shaped beam profiles, which are discussed in detail. For all investigated beam profiles, changes in the height of the peaks, depth of the valleys and the number of attached particles on the surface are observed with varying laser power and scan speed. Furthermore, a trend between the surface roughness Sa of the inclined cube geometry and the aspect ratio R of the weld geometry is found, indicating that the welding regime influences the surface roughness
Printing photonic-based thermal barrier coatings onto metal alloy
No full textReflective coatings based on photonic crystals and photonic glasses are usually produced by traditional colloidal self-assembly techniques characterised by limited control over the deposition surface and lengthy processing times. The emergence of Additive Manufacturing combined with Colloidal Assembly (AMCA) has enabled fast and precise deposition of homogeneous photonic structures, whilst circumventing issues such as the undesired coffee-ring effect. However, the application of this technique was limited to flat substrates. This study investigates the AMCA of ceramic-based colloidal structures onto metallic curved surfaces, relevant to the field of thermal barrier coatings (TBCs). Our results demonstrate the homogeneous ceramic-based photonic glass coatings can be AMCA-printed on different substrates only when a conscious surface charge matching between the colloidal particles and the substrates is made. It also demonstrates the importance of controlling the contact angle of the suspension on the substrates and the printing geometry strategy, differing from traditional direct writing. We further demonstrate the versatility of this method by printing highly porous three-dimensional gadolinium zirconate structures onto curved Inconel substrates. These coatings are engineered for their use as reflective “photonic-based” thermal barrier coatings (rTBCs), capable of suppressing both radiative and conductive heat transport. The resultant AMCA-printed Gd2Zr2O7 rTBCs outperform state-of-the-art TBCs in terms of their reflectance properties and provide a reliable thermal protection to the underlying Inconel alloy, lowering its temperature by about 150 °C in a torch experiment
Comparison of static analysis architecture recovery tools for microservice applications
No full textArchitecture recovery tools help software engineers obtain an overview of the structure of their software systems during all phases of the software development life cycle. This is especially important for microservice applications because they consist of multiple interacting microservices, which makes it more challenging to oversee the architecture. Various tools and techniques for architecture recovery (also called architecture reconstruction) have been presented in academic and gray literature sources, but no overview and comparison of their accuracy exists. This paper presents the results of a multivocal literature review with the goal of identifying architecture recovery tools for microservice applications and a comparison of the identified tools’ architectural recovery accuracy. We focused on static tools since they can be integrated into fast-paced CI/CD pipelines. 13 such tools were identified from the literature and nine of them could be executed and compared on their capability of detecting different system characteristics. The best-performing tool exhibited an overall F1-score of 0.86. Additionally, the possibility of combining multiple tools to increase the recovery correctness was investigated, yielding a combination of four individual tools that achieves an F1-score of 0.91
Two-agent case-based reasoning for prediction
No full textDecision-making and prediction using Case-Based Reasoning involve two sequential steps: retrieval, where similar cases are selected, and adaptation, where these retrieved cases are used to infer a solution to the target problem. Traditionally, both steps are performed by a single agent, and prior research has emphasized the importance of adaptation-guided retrieval, i.e. considering the adaptation method when conducting retrieval. This paper explores an alternative setting, in which retrieval and adaptation are carried out by two distinct agents. A particularly relevant scenario arises when retrieval is performed by an AI agent, while adaptation is handled by a human. Since adaptation is conduced externally, adaptation-guided retrieval is only feasible if there is a model of how adaptation is performed. Two scenarios are examined: (1) when the retrieval agent knows the target solution and seeks to guide the adaptation toward it, and (2) when the retrieval agent does not have access to the target solution. Our approach is evaluated with a series of experiments on both a symbolic and a numerical task, using models of varying complexity. The results highlight the importance of inferring a correct model of the adaptation
Managing uncertainty by leveraging flexibility in smart energy systems: AI-supported distributionally robust chance-constrained optimization
No full textThe rapid decarbonization of the energy systems to meet climate targets is driving increased electrification across various energy sectors, with the electrification of the household heating sector posing significant challenges for electrical networks in many parts of Europe. Electric heat pumps (UP), paired with thermal storage (TS), have cemented themselves as the key technology in this transition, offering high efficiency and signif-icant flexibility for load shifting and energy storage. However, this flexibility is subject to uncertainty due to variable weather conditions and user behavior. In this paper, an AI -enhanced framework for optimizing the operation of a UP-dominated res-idential network under uncertainty is presented. The framework utilizes a Bayesian neural network to generate forecasts for UP demand based on real measured data, creating a data-driven, AI-enhanced ambiguity set in the distributionally robust chance-constrained (DRCC) optimization. This approach enhances the confidence level in the proposed dispatch plan by addressing forecast uncertainty in a statistically robust manner. By effectively leveraging the thermal system's flexibility, it mitigates the risk of constraint violations and ensures reliable grid operation. Val-idation on a residential network demonstrates that the proposed framework achieves higher robustness and reliability compared to traditional deterministic models, highlighting its potential to support the energy transition.Bundesministerium für Wirtschaft und Energie (BMWE
Renewable energy supply via carbon-based molecules – A techno-economic assessment of various import pathways
No full textThe EU's transition to net-zero greenhouse gas emissions (GHG) likely necessitates renewable energy imports. This paper assesses various import pathways for energy-rich “green” molecules into the EU, focusing on carbon-based molecules like “green” methanol and synthetic natural gas (SNG). These energy carriers, produced using hydrogen derived from renewable electricity and non-fossil CO2, are compared with alternative import pathways, including liquid hydrogen, ammonia, and liquid organic hydrogen carriers (LOHCs). Different forms of final energy supply are analyzed, including pure hydrogen and hydrogen derivatives. Results show, that among the examined pathways relying on carbon-based molecules, energy imports via methanol with largely closed carbon cycles are particularly promising. A closed carbon cycle reduces the cost of energy supply with methanol by around 15 % compared to CO2 provision via Direct Air Capture (DAC). For methanol, SNG and ammonia, direct use is more economical than reconversion into hydrogen. For pure hydrogen supply, importing gaseous hydrogen by pipeline or liquid hydrogen by ship results in the lowest hydrogen supply cost (∼0.15 €/kWhH2,LHV). If hydrogen is imported via carriers, methanol or ammonia should be preferred, while SNG and LOHCs are less competitive
Enhancing swelling kinetics of pNIPAM lyogels: the role of crosslinking, copolymerization, and solvent
No full textStimuli-responsive lyogels are known for their ability to undergo significant macroscopic changes when exposed to external stimuli. While thermo-responsive gels, such as poly-N-isopropylacrylamide (pNIPAM), have been extensively studied across various applications, solvent-induced swelling has predominantly been investigated in aqueous solutions. This study explores the tailoring of lyogel formulations for future applications by controlling their solvent-induced swelling behavior, comparing both homopolymeric and semi-interpenetrating polymer networks (semi-IPNs). It is structured in two parts: the first focuses on characterization techniques, including NMR relaxometry, swelling degree measurements, mechanical testing, and SEM analysis, while the second part delves into swelling kinetic analysis, applying solvent exchange as a stimulus for varying gel formulations and solvents. In contrast to most previous studies, the impact of chemical and physical crosslinking, as well as copolymer inclusion, on the swelling behavior and mechanical properties of lyogels in organic solvents is examined and compared with solvent-induced swelling kinetics measurements. The results demonstrate that increasing chemically crosslinking in homopolymers and physically crosslinking in semi-IPNs enhances mechanical stability, while improving mass transport properties and solvent exchange kinetics. However, increases degree of crosslinking results in a prolonged response time to the solvent exchange stimulus and a reduction in the overall swelling capacity of the lyogels. Furthermore, variations in solvent properties, including molecular size and diffusion rates, significantly influence the swelling kinetics, whereas smaller, faster-diffusing solvents leading to more pronounced solvent spillage effects. Our findings highlight the complex interplay between gel formulation, network structure, and solvent nature in determining the solvent-induced swelling kinetics of lyogels, providing insights into how these materials can be tailored for specific applications especially those requiring short response times and optimized mechanical properties
Usability & Learning Experience von Onlinekursen verbessern
No full textOnlinekurse werden an Hochschulen zumeist im Standard-Lernmanagementsystem (LMS) der Hochschule und nicht in dezidierten Lernplattformen angeboten, die spezifisch auf die Usability von Onlinekursen ausgerichtet sind. LMS wie Moodle bieten eine Fülle an Funktionen und eignen sich potenziell für verschiedene Lehr-Lernszenarien. Diese Optionsvielfalt hat den Nachteil, das Kursersteller:innen Onlinekurse nicht automatisch so gestalten, dass diese eine gute Usability haben und an der Learning Experience der Nutzer:innen ausgerichtet sind.
Unsere auf dem LMS Moodle basierende Lernplattform „SDG Campus“ ist auf Online-Selbstlernkurse ausgerichtet. In den vergangenen vier Jahren wurden verschiedene Kursdesigns ausprobiert und auf der Basis von umfangreichem Nutzer:innen- und Expert:innenfeedback optimiert. Aus dieser Erfahrung leiten wir technisch-gestalterische und didaktische Tipps für die Verbesserung der Usability und der Learning Experience von Onlinekursen ab und stellen diese pointiert anhand eines Vorher-Nachher-Kursbeispiels vor. Die meisten der Tipps lassen sich auf andere LMS als Moodle übertragen.
Thematisiert werden u.a. Wahl des Kursformats, Gliederung und Strukturierungsprinzip von Kursen, Navigationskonzept, Aufbau von Lerneinheiten und Aufgaben, Wahl von Aufgabentypen, verschiedene didaktische Funktionen von Aufgaben und technischen Features (z.B. Vorwissensaktivierung, Lernstandsverfolgung, Motivationsförderung, Lebensweltbezug) oder Einsatz von Gestaltungselementen. Das Ziel sind ansprechendende, intuitiv nutzbare Kurse und eine Förderung von Lernmotivation und Lernerfolg