Eldorado - Repository of the TU Dortmund
Resources for and from Research, Teaching and Studying
This is the institutional repository of the TU Dortmund. Ressources for Research, Study and Teaching are archived and made publicly available.
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Recent Submissions
Prozess- und Wirkungsevaluation zweier Schulstraßen in Dortmund
(2025-05-09) Brockhaus, Theresa; Westhoff, Norina; Scheiner, Joachim
Enhancing EPR capabilities
(2025) Schumann, Simon Lennard; Kasanmascheff, Müge; Clever, Guido
Electron paramagnetic resonance (EPR) spectroscopy is a technique with many different application fields. It is gaining popularity in medicine, material science, and biochemistry. As EPR was further established in other research fields, several new methodologies arose. Over the years, methods have been developed to detect interactions between two paramagnetic centers and a paramagnetic center and a magnetic nucleus. These diverse methodologies allow for structural and function analysis through distance measurements and coupling analysis.
The need for higher precision measurements of minimal distances grew, and methods were developed and employed to satisfy this need. This thesis modifies 19F ENDOR measurements for very short distances from 94 GHz to 34 GHz, enhancing the technique's accessibility for a broader scientific audience. It also investigates DNA G-quadruplexes (GQ), which are critical to essential biological processes such as telomerase maintenance and gene expression. This research showcases the successful application of the 19F-ENDOR methodology at 34 GHz, overcoming the limitations posed by the complexity and scarcity of higher-frequency spectrometers. Notably, the approach retains sensitivity and orientational resolution, enhancing our understanding of GQs and expanding the methodological toolbox for studying other macromolecules.
Furthermore, analyzing biological processes sometimes means looking outside the established boundaries. In some cases, life exists in extreme environments that are not easily reproduced in lab scenarios, like high-pressure deep-sea environments, and are not always reliant on abundant amounts of substances; in some cases, a low amount of molecules can already change biological function. Both of these edge cases are not easily accessible for EPR spectroscopy. A robust high-pressure EPR setup for pressures up to 4 kbar was constructed and tested during this thesis. This not only allows for basic EPR experiments but also opens the door to the full variety of dipolar spectroscopy methods available in EPR by following an out-of-spectrometer approach. This allows the application to be independent of the later spectrometer setup, simplifying the application drastically. Additionally, a high-sensitivity resonator with an extra large sample entrance for microwave and radio frequency double resonance experiments was built and established to allow for measurements of very low-concentration samples that were not feasible in a timely manner with commercially available resonators.
Fast estimation of the dynamic start-up behavior of Line-Start Synchronous Reluctance Machines with modern rotor designs
(2025) Rituper, Jannik; Pfost, Martin; Gottkehaskamp, Raimund
Line-Start Synchronous Reluctance Machines are considered a more efficient and sustainable alternative to the widely used Induction Machines. However, the research has been impeded by the lack of appropriate simulation models, as most of the studies only use the time-consuming transient two-dimensional Finite Element Method. This thesis aims to fill this gap by presenting an improved numerical parameter model that can support the development process of Line-Start Synchronous Reluctance Machines in a fast and accurate way. The focus is on machine designs, in which the conductors are placed within the flux barriers.
First, a circuit-coupled two-dimensional Finite Element model is derived. In this context, a 3D Finite Element-based method is introduced with which the end ring parameters can be calculated accurately. It is demonstrated that it is also important to take the skin effect into account for the end ring. This has a major influence on the rotor cage, as the bars are usually much deeper than in classic Induction Machines. In this model, the
effect is considered by applying the multi-layer method, which divides the rotor cage into multiple separate cages to account for the changing resistance and inductance.
Secondly, an existing numerical parameter model is adapted to be applied to nonstandard rotor cage geometries of Line-Start Synchronous Reluctance Machines. Especially the consideration of skin effect and saturation are improved. For the skin effect, the multi-layer method is adopted from the Finite Element model. For the saturation, on the other hand, the existing global saturation factor method is extended to a dq-saturation
factor method, which takes the different saturation behavior of the machine’s d- and qaxis into account. The validation with measurements and the Finite Element model prove that both methods can significantly improve the accuracy of the model with regard to the
studied effects.
The research results represent a further step towards developing fast and accurate simulation models that can enhance the design process and therefore the development of Line-Start Synchronous Reluctance Machines. In particular, the numerical parameter model turns out to be faster from the first start-up simulation on, as it takes minutes instead of hours for one single start-up as soon as the parameters for the model have been
determined.
The effect of quantum confinement on the spin properties of lead halide perovskites probed by resonant Raman spectroscopy
(2025) Harkort, Carolin Sophie; Yakovlev, Dmitri; Reiter, Doris
Lead halide perovskites have emerged as exceptional semiconductor materials for photovoltaic and optoelectronic applications, offering easy tunability and lower production costs than conventional semiconductors. Their band gap energy can be adjusted through compositional changes, particularly by modifying the halide content, and through quantum confinement in low-dimensional systems. While the effects of com- position and dimensionality on optical properties are well established, their influence on spin properties is far from being well understood. In this work, the technique of spin-flip Raman spectroscopy is employed to investigate three-, two-, and zero-dimensional lead halide perovskites, focusing on a key band structure parameter defining the coupling of spins to external magnetic fields: the Landé 𝑔-factor. In particular, the impact of quantum confinement on the carrier 𝑔-factor is examined in Ruddlesdon-Popper type two- dimensional perovskites and zero-dimensional CsPbBr3 perovskite nanocrystals. The dependence of their 𝑔-factors on the effective band gap energy is compared to the universal dependence of the electron and hole 𝑔-factors in three-dimensional lead halide perovskites. This work reveals that while the general trend of both electron and hole 𝑔-factors follows the bulk dependence, significant deviations in their absolute values occur in two- and zero-dimensional lead halide perovskites, highlighting the pronounced impact of quantum confinement on spin properties. From a technological perspective, it is particularly interesting that the 𝑔-factor can be engineered by adjusting the number of inorganic layers in 2D perovskites or the size of the nanocrystals. Spin-flip Raman spectroscopy also reveals the domain structure of a bulk MAPbI3 single crystal by identifying the presence of domains with different crystal orientations through the 𝑔-factor anisotropy. Furthermore, rare double spin-flip processes involving two electrons or two holes are detected. Next to spin-flip processes, confined acoustic phonon modes are discovered in the Raman spectra of CsPbBr3 nanocrystals. A comparison of experimental results and density functional theory calculations enable the identification of these phonon modes and offers a complementary optical tool to probe structural properties such as the shape, structural phase, and size of the nanocrystals that are, in turn, key to understanding spin interactions.
Compilation-based explainability of tree ensembles
(2025) Murtovi, Alnis; Steffen, Bernhard; Jansen, Nils
Machine learning, particularly deep learning, has achieved remarkable success in areas such as image recognition, natural language processing, and speech recognition.
However, for structured or tabular data, tree ensemble approaches, such as random forests and gradient boosted trees, often outperform deep learning-based approaches.
Although they perform strongly, tree ensembles are in general considered to be black boxes, because the complexity of combining multiple decision trees makes it difficult to trace the reasoning behind individual predictions.
Recent advancements in Explainable Artificial Intelligence have presented heuristic post-hoc explanation methods like LIME and SHAP.
Nevertheless, these methods often rely on a model's input-output behavior and therefore only approximate how it internally arrives at its predictions.
As a result, there is an urgent need for efficient and precise approaches to explaining ensembles, especially in domains where safety or fairness is critical.
To tackle the interpretability gap, this thesis explores compilation-based techniques that transform an entire tree ensemble into a single, semantically equivalent structure such as a directed acyclic graph.
This single graph representation reveals the ensemble's underlying logic, making it amenable to formal analysis.
Once compiled, the model's internal logic becomes more transparent, allowing efficient generation of formal explanations, as well as support for verification tasks such as pre- and postcondition checks and model equivalence checking.
One significant advantage is that, after the one-time cost of building the unified representation, subsequent explanations can be generated efficiently.
This makes the proposed solutions particularly well-suited for real-time or interactive settings where many explanations are requested in sequence.
The main focus of this thesis is efficiency and scalability.
Existing compilation-based approaches can be very expensive on large ensembles.
To address this, the thesis introduces novel compilation algorithms and optimizations, significantly reducing transformation time and memory usage while maintaining exact equivalence with the original tree ensemble.
The experimental results show over an order of magnitude speedup in model compilation and multiple orders of magnitude in explanation generation compared to state-of-the-art solver-based approaches.
Furthermore, the thesis introduces a user-friendly, web-based tool (Forest GUMP) that allows non-experts to train, visualize, verify, and explain tree ensembles interactively.
Overall, these contributions advance the field of explainable AI by delivering efficient, formally grounded, and practical solutions for tree ensemble interpretability and explainability.