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Process development for heterotrophic terpene production in Cupriavidus necator
(2025) Becker, Lucas Ernst
The global demand for the sesquiterpene α-humulene is steadily increasing due to its wide range of applications in the food, fragrance, cosmetics, and pharmaceutical industries. The process parameters for microbial α-humulene production using Cupriavidus necator still offer optimization potential to fully exploit the significant advantages of biotechnological production over traditional production methods. The primary objective of this work was, therefore, the optimization of heterotrophic α-humulene production using C. necator, with a focus on in-depth investigation and optimization of various process parameters. In initial studies, the used statistical experimental design demonstrated that a temperature range of 25 °C to 28 °C resulted in increased plasmid-based α-humulene production, along with improved product/substrate and product/biomass yield coefficients compared to the established standard temperature of 30 °C. Subsequent optimization of individual process parameters led to enhanced biomass formation and α-humulene production. The fructose concentration was increased from 4 g/L to 8 g/L, the iron (II) sulfate heptahydrate concentration from 0.75 mg/L to 3.75 mg/L, and the L-rhamnose inducer concentration from 0.2 % to 2 % (w/v). In addition, the cultivation temperature was divided into two stages at 30 °C and 25 °C, whereas previously it was constant at 30 °C. The combination of these optimized parameters resulted in a 241 % increase in α-humulene production compared to the non-optimized standard process. A robustness assessment of these optimized parameters indicated a highly stable production process using C. necator pKR-hum. For α-humulene production under varying process conditions, a robustness value of -0.155 ± 0.143 was observed, while biomass formation demonstrated even greater robustness with a value of -0.002 ± 0.002, approaching the ideal robustness value of 0. Even under the influence of simulated process disturbances, the process maintained 79 % of the maximum α-humulene level compared to the undisturbed process run, with a robustness value of -0.045 ± 0.001, highlighting the robust properties of the process. Furthermore, for the first time, a dose-dependent anti-inflammatory effect of α-humulene on lipopolysaccharide-induced human THP-1 cells was observed, with a maximum reduction in interleukin-6 levels of 60 % following the administration of 100 μM α-humulene. These findings are expected to significantly contribute to the optimization of microbial-based terpenoid production processes. Additionally, initial insights were obtained that confirm α-humulene’s potential as an alternative, nature-based, and promising therapeutic approach for the reduction of elevated interleukin-6 levels and chronic inflammation.
The Influence of Gaze-Augmented Reflection on Students' Problem Solving
(2026) Langner, Axel
Problem-solving activities support conceptual understanding and the development of competencies needed to resolve problems and make informed decisions. In organic chemistry, problem solving relies heavily on representations that encode both explicit and implicit information. However, students often struggle to use These representations in their problem solving. Although various instructions have been shown to support problem solving with representations, they often benefit only specific subgroups of students. In contrast, reflection on one’s own problem solving—enhanced by showing students their eye movements and providing guiding prompts in an eye-gaze-augmented retrospective—offers a differentiated and personalized approach. This Dissertation investigated the influence of such gaze-augmented reflection on students’ Problem solving in two exploratory studies—with and without the retrospective. This investigation integrates and extends the methodological and empirical foundations established in: Langner A. & Graulich N. (2024). From sight to insight – reflection processes in an eye-gaze-augmented retrospective. International Journal of Science Education, 1–24. https://doi.org/10.1080/09500693.2024.2430804 Langner A., Hain L., & Graulich N. (2025). Defining Areas of Interest in Organic Chemistry Education Eye-Tracking Research. Journal of Chemical Education, 102(3), 1285–1297. https://doi.org/10.1021/acs.jchemed.4c00830 Langner A., Sahba M., Popova M., & Graulich N. (2025). An Integrated Approach to Characterizing Changes in Organic. Journal of Chemical Education. https://doi.org/10.1021/acs.jchemed.5c00849 The findings indicate that gaze-augmented reflection was associated with more goal-driven allocation of attention and that, despite highly individual trajectories shaped by initial problem-solving accuracy and student characteristics, accuracy converged across students. Overall, these findings highlight the potential of gaze-augmented reflection as a personalized approach to supporting students’ problem solving with representations.
Deep Learning–Based Data-Driven Analysis of Complex Plasmas in a Direct Current Discharge
(2026) Dormagen, Niklas Joseph
Complex plasmas, which are composed of electrons, ions, neutral gas atoms and micrometer sized particles, provide a unique platform for studying fundamental physical phenomena. Because the interparticle distances are comparatively large, it is possible to resolve individual particles optically. This makes it possible to investigate processes such as crystallisation, phase transitions and collective excitations. One of the experimental platforms for studying complex plasmas is the Plasma Crystal Experiment 4 (PK-4), which operates under direct current (DC) conditions. It is used on Earth and in microgravity environments, like on the International Space Station (ISS) and during parabolic flights. To reach the full potential of complex plasmas, however, robust methods must be developed to detect, track, and classify microscopic particles. Traditional image processing techniques often reach their limits in this context, especially for large datasets or under experimentally induced image noise. Therefore, modern machine learning approaches and deep neural networks offer a promising way to optimize and automate the analysis of complex plasmas, where possible. This dissertation presents a comprehensive framework for using deep learning Methods to analyze complex plasmas in the PK-4 experiment. The work is organized into three main contributions. First, a compact U-Net architecture is developed for efficient and accurate particle detection and tracking in dense plasmas. Second, WignerNet, a PointNet based model, is introduced to enable the local classification of crystalline domains using three-dimensional, Voronoi-based representations. Third, an extended graph neural network approach is used to identify more complex structures. Combining these methods greatly improves the diagnosis of complex plasmas by enabling scalable analysis at the single-particle level. In this way, the dissertation contributes both methodologically and experimentally to a deeper understanding of the dynamics and self-organization of complex plasma systems.
Dynamic Capacity Allocation in Motor-Cognitive Dual-Tasking - probed by Semantic Auditory Stimuli
(2025) Müller, Jelena
Human performance in multitasking situations is constrained by limited processing capacity, requiring dynamic allocation of cognitive and motor resources across simultaneously executed tasks. The present work investigates adaptive capacity allocation during motor-cognitive dual- and triple-task performance using semantically loaded auditory probe stimuli. Building on classical capacity theories, multiple resource models, and structural bottleneck approaches, a two-dimensional time-regime model is proposed that integrates both temporal processing demands and accuracy-related resource allocation. Across a series of experiments, participants performed combinations of a continuous motor tracking task, a cognitive calculation task, and an auditory reaction time task under varying task-load conditions. Performance was assessed using reaction time and error, calculation time and error, motor time lag, and motor accuracy measures. In addition, semantically meaningful auditory stimuli were introduced to examine content-specific interference effects on ongoing task performance. Event-related analyses further differentiated interference patterns across distinct temporal regions of interest before, during, and after stimulus processing. Results consistently demonstrated performance decrements during multitasking compared to single-task conditions, particularly reflected in prolonged reaction times, increased motor delays, and reduced motor precision. However, error rates often remained comparatively stable, suggesting adaptive redistribution of processing capacity to preserve task accuracy. Semantic stimuli affected reaction times and cognitive processing, while motor performance showed both interference and compensatory stabilization effects depending on task demands and processing phase. Event-related analyses revealed that performance impairments were temporally dynamic rather than constant, supporting the assumption of flexible resource allocation across task phases. The findings support the proposed time-regime model, according to which tasks operating under flexible temporal constraints are prolonged to maintain accuracy, whereas tasks with fixed temporal requirements are more susceptible to interference and performance breakdown. The findings contribute to a deeper understanding of cognitive-motor interference mechanisms and offer a framework for investigating real-world multitasking behavior in domains such as driving, sports, rehabilitation, and human-machine interaction.
Machbarkeit für ein nachhaltiges Ernährungsnetzwerk in Stadt und Landkreis Kassel – Abschlussbericht BioRegion Kassel Stadt + Land
(2025-12) Herzig, Christian; Keller, Martina; Bruse, Maike; Tolle, Nils; Flörke, Silke; Ross, Stefanie; Büning, Lena; Herrlich, Lara; Nutz, Katharina
(IBAE Bericht; 2025, Dezember)
Das Projekt „BioRegion Kassel – Stadt und Land: Aufbau und Stärkung bioregionaler Wertschöpfungsketten vom Acker auf den Teller" untersuchte die Voraussetzungen für eine Versorgung der Gemeinschaftsverpflegung (GV) mit ökologischen Lebensmitteln aus der Region Kassel/Nordhessen. Projektpartner waren das Institut für Betriebslehre der Agrar- und Ernährungswirtschaft (IBAE) der Justus-Liebig-Universität Gießen, das ZÖL e.V. als Träger der Ökomodellregion Nordhessen sowie der GV-Praxispartner Ganz+Gar. In einem transdisziplinären Mixed-Methods-Ansatz wurden mittels Akteursmapping, Interviews und Workshops Hemmnisse und Potenziale entlang von Wertschöpfungsketten (WSK) identifiziert. Eine Foodshed-Analyse ermittelte die regionale Eigenversorgungskapazität u. a. für Getreide, Eier und Kartoffeln. Da die aktuelle Nachfrage kein neues physisches Bündelzentrum trägt, richtete sich der Fokus des Forschungs- und Entwicklungsprojekts auf den Ausbau und die Stärkung bestehender Bündel- und Logistikstrukturen. Hierfür wurden als zentrale Ergebnisse drei praxistaugliche Funktionsmodelle zur Warenbündelung entwickelt sowie Schlüsselfaktoren – insbesondere fehlende Verarbeitungsinfrastrukturen und instabile Nachfrage – identifiziert. Die Ergebnisse bieten eine fundierte Entscheidungsgrundlage für Kommunalpolitik und WSK-Akteur*innen in der Projektkulisse Kassel - Stadt und Landkreis.