Physics

Title of thesis:&nbsp;Explainable AI for Decision Making
Abstract:&nbsp;This thesis explores the use of a Retrieval Augmented Generation (RAG) AI system, built on Large Language Models (LLMs), to support root cause analysis of quality issues in industrial and marine power systems. Conducted in collaboration with Volvo Penta, the study evaluates whether pre-trained LLMs and a tailored database can operate effectively in a specialized domain.&nbsp;
Examiner: Mats Granath Supervisor: Bettina Linder Opponents: Kevin To &amp; Joel Gråsjö&nbsp;
Location of presentation: <a rel="noopener" href="https://maps.chalmers.se/#7241f650-27c9-11ef-a21b-017390dd2bcd" target="_blank" title="Chalmers maps" data-anchor="#7241f650-27c9-11ef-a21b-017390dd2bcd">von Bahr, Soliden 3</a><a rel="noopener" href="https://maps.chalmers.se/#24d8b7be-1f59-4388-be28-a07861b429b2" target="_blank" title="Chalmers Maps" data-anchor="#24d8b7be-1f59-4388-be28-a07861b429b2"></a><a rel="noopener" href="https://maps.chalmers.se/#36a610c1-e9b3-48bd-b01c-9501f9af79fe" target="_blank" title="Chalmers map" data-anchor="#36a610c1-e9b3-48bd-b01c-9501f9af79fe"></a>
Online: <a rel="noopener" href="https://chalmers.zoom.us/j/68569050177" target="_blank" title="Zoom">Zoom</a>

Title of thesis:&nbsp;<span style="font-size: 11.0pt; font-family: 'Aptos',sans-serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: Aptos; color: black; mso-ligatures: standardcontextual; mso-ansi-language: EN-GB; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-GB">Diagnosing Brachycephalic Obstructive Airway Syndrome in Dogs Using Computer Vision and Machine Learning
Abstract:&nbsp;
Brachycephalic Obstructive Airway Syndrome (BOAS) is a breathing disorder that is common among dogs of certain breeds, impairing their quality of life. Diagnosing BOAS requires licensed veterinarians to undergo specialized training, which limits accessibility. There is demand for a more accessible solution, which artificial intelligence could provide. This thesis evaluates two different approaches of machine learning to explore the possibility of classifying BOAS based on audio recorded from Android mobile phones, using Python. The first approach extracts signal features from the audio, which are used to train a simple machine learning model. The second approach relies on computer vision, using spectrograms; visual representations of the audio signals, to train a Convolutional Neural Network (CNN). Due to limited data, I employed augmentation techniques to artificially expand the dataset for training the models. My findings suggest that the spectrogram-based model is better suited for the problem, with a perfect prediction accuracy when the dogs were recorded after a short exercise test, suggesting strong performance under that condition. For dogs at rest, this model achieved an 81.7% accuracy, indicating somewhat promising results even under less favourable conditions. However, due to the limited dataset, the predictive performance was evaluated on few samples, and therefore additional data is needed for a more robust conclusion. Furthermore, one particular augmentation technique designed to account for differences in the recording devices' frequency response enhanced the model's general performance and could be further refined to improve accuracy, especially for dogs at rest.
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Examiner: Magnus Karlsteen Supervisor: Magnus Karlsteen Opponent: Jennie Berndtson
Location of presentation: <a rel="noopener" href="https://maps.chalmers.se/#eece58ce-9a1e-4ab1-af3d-d4fc2cb1126c" target="_blank" title="Chalmers map" data-anchor="#eece58ce-9a1e-4ab1-af3d-d4fc2cb1126c">Nexus, Fysik Origo</a>

Title of thesis: Acoustic Signal Analysis and Feature-Based Classification of BOAS:&nbsp;For the Health and Welfare of Brachycephalic Dogs
Abstract:&nbsp;
Brachycephalic Obstructive Airway Syndrome (BOAS) is a chronic condition that significantly impairs breathing and reduces the quality of life in popular brachycephalic dog breeds such as Bulldogs, French Bulldogs, and Pugs. As these breeds become more popular, the number of affected dogs increases, highlighting the need for accessible and reliable methods to assess the severity of BOAS.
Currently, BOAS is diagnosed through a functional grading test performed by certified veterinarians. However, the limited availability of accredited professionals restricts access to this test. As of early 2025, Swedish law requires all brachycephalic dogs to pass this test to be eligible for breeding, further increasing the demand for accessible testing solutions.
This thesis explores using acoustic signal analysis and machine learning to classify dogs as BOAS-positive or BOAS-negative based on their breathing sounds. Audio recordings from several dogs were analyzed, with features extracted using the openSMILE toolbox. The Mann-Whitney U-test was then applied to reduce the feature set to those most correlated with BOAS classification.
Two modeling approaches were tested, one using separate models for pre- and post-exercise data and a hybrid model combining both. The hybrid model performed best, with statistical analysis showing post-exercise data to be more informative. The final model correctly classified all five dogs in the new dataset, achieving an AUC of 1.0 and an average prediction confidence of 86.8%. These results show promising potential for an acoustic, feature-based BOAS classification model, though additional data is needed to improve its generalization.
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Examiner: Magnus Karlsteen Supervisor: Magnus Karlsteen Opponent: Tim Pagrell
Location of presentation: <a rel="noopener" href="https://maps.chalmers.se/#eece58ce-9a1e-4ab1-af3d-d4fc2cb1126c" target="_blank" title="Chalmers map" data-anchor="#eece58ce-9a1e-4ab1-af3d-d4fc2cb1126c">Nexus, Fysik Origo</a>

Title of thesis: Multivariate anomaly detection with LSTM layered Variational Autoencoder
Abstract:The aim of this thesis was to develop and evaluate the effectiveness of a recurrent neural network layered autoencoder model for detecting anomalies in multivariate time-series data, with a focus on improving the accuracy and reliability of diagnostic data for Volvo Penta’s boats. The primary goal was to leverage the relationships and correlations between signals to identify deviations that traditional models may fail to detect. The model’s performance was assessed in terms of its ability to learn the structure of normal data, detect synthetic anomalies, and provide meaningful insights without relying on labeled datasets.
Examiner: Mats Granath Supervisor: Mats Granath Opponents: Atefeh Mirzbeigi, Amir Ali Barani, Sean Lidin
Location of presentation: <a rel="noopener" href="https://maps.chalmers.se/#7241f650-27c9-11ef-a21b-017390dd2bcd" target="_blank" title="Chalmers maps" data-anchor="#7241f650-27c9-11ef-a21b-017390dd2bcd">von Bahr, Soliden 3</a><a rel="noopener" href="https://maps.chalmers.se/#24d8b7be-1f59-4388-be28-a07861b429b2" target="_blank" title="Chalmers Maps" data-anchor="#24d8b7be-1f59-4388-be28-a07861b429b2"></a><a rel="noopener" href="https://maps.chalmers.se/#36a610c1-e9b3-48bd-b01c-9501f9af79fe" target="_blank" title="Chalmers map" data-anchor="#36a610c1-e9b3-48bd-b01c-9501f9af79fe"></a>
Online: <a rel="noopener" href="https://chalmers.zoom.us/j/66477666274" target="_blank" title="Zoom">Zoom</a>

Title of thesis:&nbsp;More Reliable Binding Affinity Prediction of Protein Ligands Combining Molecular Dynamics Simulations and Machine Learning Models
Abstract:&nbsp;Efficient and accurate prediction of protein-ligand binding affinities is essential for advancing drug discovery. This thesis presents a novel approach that combines molecular docking results with features derived from short molecular dynamics(MD) simulations to enhance the prediction accuracy of binding affinities. The thesis aims to bridge the efficiency of traditional docking methods with the more accurate Free Energy perturbation (FEP) techniques. MD simulations were conducted on a dataset of around 5,000 protein-ligand complexes and extracted features related to binding enthalpy and dynamic behavior related to the more complex binding entropy. These features, alongside molecular docking scores, were used to train machine learning models. The CatBoost regressor was identified as the most effective model, achieving better predictive accuracy than Molecular Docking alone. This method facilitates more reliable binding affinity predictions by successfully integrating docking insights with dynamic protein-ligand behavior, thereby accelerating the early stages of drug discovery.
Examiner: Paul Erhart Supervisors: Marco Klähn Opponent: Richard Nunstedt and Erik Linghammar
Location of presentation: <a rel="noopener" href="https://maps.chalmers.se/#24d8b7be-1f59-4388-be28-a07861b429b2" target="_blank" title="Chalmers map" data-anchor="#24d8b7be-1f59-4388-be28-a07861b429b2">PJ-salen</a>

Title of thesis:&nbsp;On the Feasibility of Neutron Noise Diagnostics in Fast Gen-IV Reactors – Comparisons of One-Dimensional Neutron Noise Characteristics in Fast and Thermal Systems using Linear Multi-Group Diffusion Theory
Abstract: This master's thesis studies neutron noise, i.e. the temporal fluctuations from the mean neutron flux, in nuclear reactors. Neutron noise can be used to identify and localise faults and anomalies in such systems through a technique called neutron noise diagnostics, potentially providing early warning and leading to increased safety and operational availability. Due to the limited amount of neutron flux instrumentation available in a typical power reactor, the spatial shape of the noise is the key quantity to characterise the type of noise present. This study differentiates itself from prior research on neutron noise diagnostics, which mostly focused on Light-Water Reactors (LWR), whereas this work investigates the properties of the neutron noise in both LWRs and in Liquid Metal-cooled Fast Reactors (LMFR). More specifically, we compare the axial shape of the neutron noise of: Pressurised Water Reactors (PWR), Boiling Water Reactors (BWR), Sodium-cooled Fast Reactor (SFR) and Lead-Cooled Fast Reactors (LFR). The former two are LWRs, and the latter two are LMFRs. Each system's deviation from point kinetics is analysed, which is essential to evaluate the capability of noise diagnostics.
To this end, a 1D numerical neutron transport solver was developed using linear multi-group diffusion theory with a variable number of delayed neutron families. The solver uses group constants generated by the Monte Carlo code Serpent-2 for each modelled system. The static neutron flux is described by an eigenvalue problem, which is solved by the power iteration method, accelerated using Wieland's technique. The induced noise arises from an absorber of variable strength-type perturbation in the macroscopic absorption cross-section, and the resulting source-type problem is solved by matrix inversion.&nbsp;
Our computations show that the induced neutron noise of the LWRs deviates more from point-kinetics, and their axial shape is thus more informative than that of the LMFRs. Also, perturbations closer to the reactor's periphery induce a neutron noise that is more informative. The point-kinetic behaviour of the LFR is strongly dictated by the active height of the core, more so than by the fast spectrum. We also show that reactors fuelled by plutonium are more point-kinetic than those fuelled by uranium.
For the results to become more conclusive, more work is needed regarding the types of reactors studied, with more analysis aimed towards what parameters affect the point-kinetic nature of the noise. Studies can also be made in higher spatial dimensions that more accurately take into account the complex geometries of nuclear reactors, and incorporating burn-up calculations when generating the group constants.
Examiner: Christophe Demaziere Supervisors: Christophe Demaziere Opponent: Pablo Arriagada Torres
Location of presentation: <a rel="noopener" href="https://maps.chalmers.se/#24d8b7be-1f59-4388-be28-a07861b429b2" target="_blank" title="Chalmers map" data-anchor="#24d8b7be-1f59-4388-be28-a07861b429b2">PJ-salen</a>

Title of thesis:&nbsp;Don't Judge a Malware by its Binary&nbsp; Similarity-Based Embeddings for Efficient Malware Analysis
Abstract:<span style="font-size: 12.0pt; font-family: 'Aptos',sans-serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: Aptos; mso-ansi-language: SV; mso-fareast-language: SV; mso-bidi-language: AR-SA;">Cyberattacks are projected to cost the global economy more than $10 trillion annually by 2025. Malware classification still leans on manually engineered binary-level features, a process that is expensive and brittle. We investigated whether Windows PE samples could be mapped into a continuous “behavior space” without running each file in a Sandbox, with the aim of enabling finer-grained distinctions than existing malware family labels provide. Static Endgame Malware BEnchmark for Research (EMBER) feature vectors were paired with dynamic behavior reports from Recorded Future’s Triage Sandbox. A deep metric-learning model with triplet loss (FaceNet-style) was trained to project EMBER vectors into clusters defined by behavioral similarity. The model achieved a weighted F1 score of 0.95 for malware-family classification, proving the model could create valuable embedding spaces for classifying malware by family. Text embeddings for the reports were computed with both BM25 combined with cosine similarity and a transformer encoder. When we projected Sandbox reports into text-embedding space, both BM25 combined with cosine similarity and a transformer encoder revealed finer-grained behavioral structure. In contrast, static EMBER feature vectors showed almost no alignment with dynamic behavior, indicating that they carry insufficient behavioral features. Rich behavioral embeddings can be built directly from Sandbox reports using transformer encoders, scaling more efficiently with corpus size than BM25 combined with cosine similarity.
Examiner: Mats Granath Supervisor: Anders Hansson Opponents: Rikard Radovac, Carolina Rönnewall
Location of presentation: <a rel="noopener" href="https://maps.chalmers.se/#7241f650-27c9-11ef-a21b-017390dd2bcd" target="_blank" title="Chalmers maps" data-anchor="#7241f650-27c9-11ef-a21b-017390dd2bcd">von Bahr, Soliden 3</a><a rel="noopener" href="https://maps.chalmers.se/#24d8b7be-1f59-4388-be28-a07861b429b2" target="_blank" title="Chalmers Maps" data-anchor="#24d8b7be-1f59-4388-be28-a07861b429b2"></a><a rel="noopener" href="https://maps.chalmers.se/#36a610c1-e9b3-48bd-b01c-9501f9af79fe" target="_blank" title="Chalmers map" data-anchor="#36a610c1-e9b3-48bd-b01c-9501f9af79fe"></a>
Online: <a rel="noopener" href="https://chalmers.zoom.us/j/65417104675" target="_blank" title="Zoom">Zoom</a>

Title of thesis:&nbsp;Tendon Cells from Younger and Older Patients Exhibit Similar Mechanical and Biological Properties in a 3D In Vitro Model
Abstract:&nbsp;The Achilles tendon (AT) is the most frequently injured tendon in the human body, with incidence rates on the rise, particularly among older individuals. The healing capacity decline with age based on functional outcomes post an Achilles tendon rupture. However, the cellular mechanisms behind this remains largely unknown as there is a limited number of studies in humans. The aim of this study was therefore to investigate mechanical and biological differences in new tendon tissue formation between young (n=6, mean age=40.5 years, range=21-53 years) and old (n=7, mean age=70, range=57-76 years) patients with chronic Achilles tendon ruptures. To achieve this, primary human cells extracted from tendon tissue were cultured in a 3D fibrin hydrogel model to form tendon constructs. Pull-to-failure tests were performed to determine the mechanical properties, including the maximum force, stress, stiffness, and Young's modulus. The results show that there is no significant difference between the two groups in any of the measured parameters. These findings imply that neither the structural integrity nor the quality of the tendon construct seems to be affected by increasing age. The biological characteristics were assessed through cell health assays, histology, migration assay, and gene expression analysis. By examining metabolic cell activity and cytotoxicity it could be concluded that there was no change in cell health with age. Histology revealed no differences in cell density, circularity, eccentricity, or proteoglycan content nor in collagen type I and type III ratio. The migration assay showed that no difference could be found in cell motility with age. Analysis of the gene expression showed significant difference in two of the studied genes, namely DCN and S100A4, at one time-point of collection. The compiled results from the biological tests indicate that cells extracted from healing tissue does not seem to behave differently due to increasing age of the patients, except for the gene expression of two of the investigated genes. The mechanical and biological test results indicate that the capacity to repair and form new tissue does not decline with age, which is an encouraging outcome from a patient perspective. To the best of our knowledge, this is the first study to investigate age-related differences in healing Achilles tendons using patient derived primary human cells in an in vitro model, providing novel insights into Achilles tendon healing capacity as a function of age.
Examiner: Julie Gold,&nbsp; Supervisors: Pernilla Eliasson and Christer Thomsen Opponent: &nbsp;Lisa Aronsson&nbsp;
Location of presentation: <a rel="noopener" href="https://maps.chalmers.se/#96de5091-96fe-4e70-8def-1faa831680ae" target="_blank" title="Chalmers maps" data-anchor="#96de5091-96fe-4e70-8def-1faa831680ae">F Room Raven &amp; Fox</a><a rel="noopener" href="https://maps.chalmers.se/#24d8b7be-1f59-4388-be28-a07861b429b2" target="_blank" title="Chalmers map" data-anchor="#24d8b7be-1f59-4388-be28-a07861b429b2"></a>

Title of thesis:&nbsp;Characterization of solid electrolytes and sodium metal anodes
Abstract:&nbsp;Solid-state sodium batteries are possible candidates for next-generation electrochemical energy storage, with improved sustainability and safety over current lithium-ion technologies. In addition, solid electrolytes could potentially enable the use of sodium metal anodes for increased energy density. However, these electrodes currently have short lifetime due to electro-chemo-mechanically coupled degradation mechanisms, which need to be well understood in order to mitigate the problems and extend the lifetime of solid-state sodium batteries. This thesis investigates a sulfide solid electrolyte, with a particular interest in processes at the interface with sodium metal. The study involves electrochemical characterization techniques and imaging with X-ray tomographic microscopy, in order to gain insights into the processes that limit cell lifetime and efficiency. Results show rapid polarization of the cells, likely caused by chemical degradation between sodium metal and the electrolyte. The reaction products seem to form an interphase layer that fails to passivate the electrode, leading to capacity loss due to the continuous consumption of sodium metal. Protective interlayers could be a promising path of future research in order to improve the interface stability and extend the lifetime of sodium metal electrodes.
Examiner: Aleksandar Matic Supervisors: Elin Dufvenius Esping Opponent: Simon Dovrén
Location of presentation: <a rel="noopener" href="https://maps.chalmers.se/#24d8b7be-1f59-4388-be28-a07861b429b2" target="_blank" title="Chalmers map" data-anchor="#24d8b7be-1f59-4388-be28-a07861b429b2">PJ-salen</a>

Title of thesis: <span style="font-size: 12.0pt; font-family: 'Aptos',sans-serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: Aptos; color: black; mso-ansi-language: SV; mso-fareast-language: SV; mso-bidi-language: AR-SA;">Water-induced phase separation in amorphous solid dispersions studied with SAXS
Abstract:&nbsp;Amorphous solid dispersions (ASDs) are pharmaceutical formulation in which a drug is molecularly dispersed within a carrier matrix. ASDs are designed to address the challenges of drugs with low solubility in their crystalline state. The carrier matrix, typically a polymer, helps stabilizing the drug in its amorphous phase, which allows ASDs to enhance solubility and dissolution rates. This thesis examines ASDs composed of the drug indomethacin and the polymer PVPVA as carrier, with the objective to correlate water-induced phase separation to drug release behaviour. Samples of varying drug concentrations, both in their dry state and after exposure to a neutral buffer, were analysed using small-angle X-ray scattering (SAXS), to understand the phase separation processes on the nanoscale. &nbsp;The mobility of the samples was analysed with differential scanning calorimetry (DSC) and the drug release was measured through dissolution tests. Nanoscale phase separation occurred upon exposure to a neutral buffer, for drug loadings &gt;10%, after absorbing &gt;40% buffer. Higher drug loadings and buffer concentration led to a higher degree of phase separation with distinct interphases. This impacted the dissolution properties, as samples with 5% and 10% drug loading had faster drug release, in comparison to the 20% and 30% samples. &nbsp;These results offer insight into the phase behaviour of ASDs upon dissolution, though additional investigation of ASDs in acidic conditions would enhance the understanding of in vivo phase separation processes.
Examiner: Aleksandar Matic Supervisors: Martina Olsson Opponent: Andreas Erlandsson
Location of presentation: <a rel="noopener" href="https://maps.chalmers.se/#24d8b7be-1f59-4388-be28-a07861b429b2" target="_blank" title="Chalmers map" data-anchor="#24d8b7be-1f59-4388-be28-a07861b429b2">PJ-salen</a>

Title of thesis:&nbsp;<span style="font-size: 11.0pt; font-family: 'Aptos',sans-serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: Aptos; color: #212121; mso-ligatures: standardcontextual; mso-ansi-language: EN-US; mso-fareast-language: SV; mso-bidi-language: AR-SA;" lang="EN-US">Extracting Fission observables in solenoidal spectrometers&nbsp;
Abstract:&nbsp;
Modern subatomic physics experiments often require large accelerator facilities in order to produce and study exotic particles and isotopes. Experiments with highly segmented detectors as well as sophisticated analysis software are nowadays state-of-the-art. This project is motivated by an upcoming experiment in the ISOLDE facility at CERN that aims to study the fission barrier of 230Ac through 229Ac(d,pf) reactions. This is a first step to explore exotic neutron rich isotopes, and the role fission has in the astrophysical rapid neutron capture process (r-process).
This master thesis focuses on development and tests of the analysis software to include newly added detectors before the start of the experiment. By converting simulated data to mimic experimental data it is possible to test the analysis software in advance. Comparing the data output from the software with the same data before the conversion, allows to spot bugs in the software. Thus, the software can be prepared and tested before the experiment, as it removes the need to collect measured data. This means that the software can be used as a diagnostic tool already during the setup phase prior to the experiment.
Examiner: Andreas Heinz Supervisor: Andreas Heinz Opponent: <span style="font-size: 11.0pt; font-family: 'Aptos',sans-serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: Aptos; color: #212121; mso-ligatures: standardcontextual; mso-ansi-language: EN-US; mso-fareast-language: SV; mso-bidi-language: AR-SA;" lang="EN-US">Hazel Ndulue
Location of presentation: <a rel="noopener" href="https://maps.chalmers.se/#24d8b7be-1f59-4388-be28-a07861b429b2" target="_blank" title="Chalmers map" data-anchor="#24d8b7be-1f59-4388-be28-a07861b429b2">PJ-salen</a>

Title of thesis: Creation of a digital twin for simulation of melt pool geometry in metal additive manufacturing
Abstract:&nbsp;Additive manufacturing (AM) is a production technique that allows for the creation of complex geometries and components in near net shape by fusing the material layer by layer. It is a versatile but expensive method and due to this cost, simulating the process is of utmost interest. Since it requires complex multi-physics models at a very low scale, producing simulations of desired accuracy takes a lot of time and computational power. Simpler analytical models have shown promise as they run in a fraction of the time, but at the cost of accuracy. The present work explores the possibility to train an analytical model by adjusting material and process parameters to compensate for the lack of physics. The goal is a digital twin of the more complex model, which generates melt pool geometries comparable to the multi-physics model in a fraction of the time. This was accomplished by minimising a cost function based on the width and depth of a number of isotherms in a simulated melt pool in an electron beam melting AM process. The work demonstrates the feasibility of using a low resolution analytical model with modified material and process parameters as a digital twin to a multi-physics based simulation. Thus demonstrating the feasibility of bridging the gap between computational efficiency and model accuracy. It also establishes a foundation for more cost-effective and scalable simulations in additive manufacturing, advancing the potential for innovation in the field.
Examiner: Mattias Thuvander Supervisor: Erik Granhed Opponent: Filip Bergqvist
Location of presentation: <a rel="noopener" href="https://maps.chalmers.se/#dc1c9950-1f4d-11f0-a594-0dc76ca9af78" target="_blank" title="Chalmers Maps" data-anchor="#dc1c9950-1f4d-11f0-a594-0dc76ca9af78">F-6004</a>
Online: <a rel="noopener" href="https://chalmers.zoom.us/j/2989797445?omn=64612038301" target="_blank" title="Zoom" data-anchor="?omn=64612038301">Zoom</a>

Title of thesis:&nbsp;Fuel cell catalyst coating quality for reliable ORR activity results
Abstract: Proton exchange membrane fuel cells (PEMFCs) are energy conversion devices that could play a key role in the reduction of greenhouse gas emissions. However, one major problem for PEMFCs is the slow oxygen reduction reaction (ORR), which causes large overpotentials and requires a large amount of expensive platinum catalyst to be sped up. For that reason, evaluation of the ORR activity of catalysts is important, and the rotating disk electrode (RDE) method is commonly used for this purpose. However, there are still problems with achieving reliable results of the ORR activity with RDE, specifically due to the activity’s dependence on the quality of the catalyst coating on the electrode. This thesis investigates how different parameters of a catalyst ink influence the catalyst coating and how to achieve a uniform thin coating. For one catalyst, inks with different alcohol contents and platinum loadings were tested, and for a second catalyst, different alcohol contents and pH levels were tested. The alcohol content did not appear to have a significant impact on the ORR activity or the homogeneity of the thin films. A high alcohol content facilitated the spreading out of the ink thanks to a lower surface tension, but it was also harder to keep it from spreading outside the electrode disk. With platinum loading, there seemed to be a lower limit between 10 and 20 μgPt/cm2 for the catalyst to cover the entire disk homogenously. Below this limit, the mean specific ORR activity was about 20 % lower. At 34.8 μgPt/cm2, the results were about the same as for 20 μgPt/cm2, meaning no upper limit appears to have been reached. For the second catalyst, stability issues were observed, which did not significantly change with the alcohol content. Increasing the pH of the ink by adding KOH improved ink stability, but it was still not possible to produce a completely homogeneous coating. Further investigations on how to achieve a homogeneous coating with this catalyst are therefore needed.
Examiner: Björn Wickman Supervisor: Marika Männikkö Opponent: William Colliander
Location of presentation: <a rel="noopener" href="https://maps.chalmers.se/#96de5091-96fe-4e70-8def-1faa831680ae" target="_blank" title="Chalmers Map" data-anchor="#96de5091-96fe-4e70-8def-1faa831680ae">Raven &amp; Fox</a><a rel="noopener" href="https://maps.chalmers.se/#24d8b7be-1f59-4388-be28-a07861b429b2" target="_blank" title="Chalmers map" data-anchor="#24d8b7be-1f59-4388-be28-a07861b429b2"></a>

Title of thesis:&nbsp;Establishing rheological methodology and analyzing data for PEMFC catalyst ink &nbsp;
Abstract:The shift toward cleaner, low-emission energy solutions are pacing up and hydrogen powered Proton Exchange Membrane Fuel Cells (PEMFCs) are emerging as one of the leading technologies driving this transformation. Catalyst ink is a key component of membrane assembly in the PEMFC, which is composed of platinum based catalyst particles supported on carbon and an ionomer. Composition of catalyst inks, preparation, dispersion, application of catalyst layers and storage largely affect the PEMFC performance. In this study, catalyst inks of varying compositions were tested using the rheometer. Different types of rheology tests were conducted on catalyst inks samples to understand the viscous-elastic responses and their possible impact during electrode coating application. &nbsp;Ionomer to Carbon (I/C) ratio of the catalyst powder was noted to be the main factor in bringing stable viscosity response under shear conditions for the catalyst ink. Solid content follows as a second important factor, however combination of higher I/C ratio with 45-60% content of N-Propanol (NPA) as solvent responds well during the oscillatory sweep tests results. Catalyst ink of such composition shows dominant elastic modulus, higher relaxation time and cross over point (Elastic Modulus G’= Loss Modulus G’’) with stable behavior in linear visco-elastic region (LVER) region. Furthermore, the key parameter Deborah (De) was found to be close to 1 for the best performing catalyst inks, which in rheology is used to characterize the fluidity vs. elasticity of a material under deformation or flow. These findings are also validated and supported by similar results obtained from subscale testing of these CIs membrane assemblies. While these tests and analysis helped make headway in initial understanding of catalyst inks used at PowerCell Group, more research work is needed to understand morphology correlation and response of particle interactions at the nano scale using advanced techniques like cryo-TEM, SEM and SAXS.
Examiner: Björn Wickman Supervisor: Marcus Liljenberg Opponents: Lorensu Hewa Welle Kamkanamge Sujith Priyantha
Location of presentation: <a rel="noopener" href="https://maps.chalmers.se/#96de5091-96fe-4e70-8def-1faa831680ae" target="_blank" title="Chalmers Map" data-anchor="#96de5091-96fe-4e70-8def-1faa831680ae">Raven &amp; Fox</a>

Title of thesis:&nbsp;Uncertainty-Aware Weather-Related Road Surface Condition Classification
Abstract: Autonomous vehicles rely on collaborative systems for decision-making during operation, one of which is machine learning algorithms. Using cameras and sensors mounted on the car, these algorithms can analyse captured data to interpret its surroundings, enabling it to make informed and safe driving decisions. The algorithms are trained on data gathered in the field. In real-time use, samples that have never been seen before, referred to as Out-Of-Distribution (OOD) samples, can occur, which the algorithm then confidently uses to make driving decisions. In a worst-case scenario, this can lead to vehicle failure, resulting in injuries to passengers and/or property. The primary objective of this thesis is to implement and analyse methods for detecting out-of-distribution (OOD) samples in a road surface condition classifier. The machine learning algorithm was a convolutional neural network, trained to detect dry, wet and snowy road conditions. The methods used to detect OOD samples were Maximum Softmax Probability, Energy-Based OOD detection, Outlier Exposure, Virtual Outlier Synthesis, Rectified Activations, Deep Nearest Neighbour, and Virtual Logit Matching. The datasets used for evaluation of the methods were three datasets, cars (car in front of vehicle), slush (slushy road condition), glare (sun causes glare on the road surface), constructed from the same database as the data the model was trained on and are considered as near OOD data along side with two publicly available datasets, CIFAR10 and Textures, are considered far OOD data. The methods that performed the best were Deep Nearest Neighbour and Virtual Logit Matching when evaluated on CIFAR10 and Textures datasets, achieving near-perfect results. When tested on the cars dataset, Virtual Logit Matching exhibited a notable deviation in performance compared to the other methods, although it still performed poorly. In general, except as previously mentioned, the detectors did not outperform the two baseline detectors, Energy-Based OOD detection and Maximum Softmax Probability.
Examiner: Daniel Midtvedt Supervisor: Daniel Midtvedt Opponents: William Nilsen, Gustav Wikman
Location of presentation: <a rel="noopener" href="https://maps.chalmers.se/#eece58ce-9a1e-4ab1-af3d-d4fc2cb1126c" target="_blank" title="Chalmers maps" data-anchor="#eece58ce-9a1e-4ab1-af3d-d4fc2cb1126c">Nexus</a>

Title of thesis: Advancing Sub-Visible Particle Characterization in Inhaled Biologics
Subtitle:&nbsp;Micro-Flow Imaging for Particle Analysis and Sub-Population Differentiation
Abstract: Protein aggregation poses a significant immunogenicity risk in inhaled biologics, where aggregated proteins may trigger adverse immune responses. Conventional methods, such as Light Obscuration, are used to quantify sub-visible particles, but often face challenges in detecting small and translucent particles.
This thesis explores how continuous flow microscopy, specifically Micro-Flow Imaging, can be used to differentiate particle sub-populations by analyzing morphological and optical data. Drug products and excipients are examined to identify and quantify these sub-populations, their composition, aiming to enhance the understanding of the particulate environment in the formulations.
The results reveal that different formulations exhibit particles with distinct characteristics. Particle distributions have been established, resulting in unique profiles that could potentially be used to identify the presence of specific formulations in unknown samples. However, a deeper understanding of the interactions and complexities among various substances in the formulations is necessary to enhance the accuracy of the identification test.
Examiner: Jan Swenson Supervisors: <span style="font-size: 12.0pt; font-family: 'Aptos',sans-serif; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: Aptos; mso-ansi-language: SV; mso-fareast-language: SV; mso-bidi-language: AR-SA;">Noemi Gaglianone, Gerard Masdeu Opponent: <span style="font-size: 12.0pt; font-family: 'Aptos',sans-serif; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: Aptos; mso-ansi-language: SV; mso-fareast-language: SV; mso-bidi-language: AR-SA;">Olivia Jonsson
Location of presentation: <a rel="noopener" href="https://maps.chalmers.se/#24d8b7be-1f59-4388-be28-a07861b429b2" target="_blank" title="Chalmers map" data-anchor="#24d8b7be-1f59-4388-be28-a07861b429b2">PJ-salen</a>

Title of thesis:&nbsp;Sequential Graph-Based Decoding of the Surface Code using a Hybrid Graph and Recurrent Neural Network Model
Abstract:In order to achieve reliable quantum computation with noisy qubits, quantum error correction (QEC) is necessary. Quantum error-correcting codes mitigate the inherent noise in quantum systems by distributing the logical state over several qubits, thereby introducing redundancy. One such promising code is the surface code. It encodes the logical qubit using a two-dimensional lattice of physical data qubits and ancilla qubits. By taking and decoding measurements on the ancilla qubits of the surface code, one can deduce whether a logical bit- or phase-flip has occurred. However, this is a complex and potentially time-consuming task. Multiple decoding algorithms exist, such as the classical minimum weight perfect matching (MWPM) decoder. In recent years, data-driven algorithms have been shown to decode the surface code with a high degree of accuracy. In this thesis, we present a machine learning approach to decoding the surface code using a combination of graph neural networks (GNN) and recurrent neural networks (RNN). Specifically, graph representations are constructed over a short, sliding time window of syndrome measurement data. Each representation is processed by a GNN and its output is used as a learned high-dimensional embedding for an RNN. This enables continuous decoding of measurement patterns over longer time series. While the decoder is trained on relatively short syndromes, it is able to generalize for new data and longer syndromes, outperforming the classical MWPM algorithm across both short and long time series. This work opens up a new approach to reliable and potentially fast decoding of QEC codes.
Examiner: Mats Granath Supervisor: Mats Granath Opponent: Tobias Wallström&nbsp;
Location of presentation: <a rel="noopener" href="https://maps.chalmers.se/#37d8de3e-1806-4420-92e7-b0b773f4fc88" target="_blank" title="Chalmers Maps" data-anchor="#37d8de3e-1806-4420-92e7-b0b773f4fc88">FL41</a><a rel="noopener" href="https://maps.chalmers.se/#24d8b7be-1f59-4388-be28-a07861b429b2" target="_blank" title="Chalmers Maps" data-anchor="#24d8b7be-1f59-4388-be28-a07861b429b2"></a><a rel="noopener" href="https://maps.chalmers.se/#36a610c1-e9b3-48bd-b01c-9501f9af79fe" target="_blank" title="Chalmers map" data-anchor="#36a610c1-e9b3-48bd-b01c-9501f9af79fe"></a>
Online: <a rel="noopener" href="https://chalmers.zoom.us/j/62345043051" target="_blank" title="Zoom">Zoom</a>

Title of thesis:&nbsp;Microscopy for pH mapping in nanofluidic devices
Abstract: Nanotechnology, along with related fields such as nanofluidics, is an area of science with much potential. Research into material properties at the nano-scale is driving innovation across various fields, such as the development of new drugs in medicine, and improvements to solar cells in the development of sustainable energy.&nbsp; This thesis examines the possibility of creating and measuring pH gradients in nanofluidic channels, as a novel method to study nanoparticles. Such a method could potentially be used to study nanoparticles subjected to different pH environments. The presented process relies on darkfield microscopy and a pair of double Amici prisms. When observing fluorescent particles or dyes in the nanochannels, the fluorescence is angularly dispersed by the prisms. This enables measurements that capture spatial distribution of particles along the nanochannels, and corresponding spectral information dispersed perpendicularly.&nbsp; The experiments confirm that it is possible both to generate a pH gradient and to measure local pH values within the nanochannels. The presented results are largely based on the use of the pH-sensitive fluorescent dye SNARF-1, and a foundation for future study with FRET nanoparticle probes is also provided. 
Examiner: Christoph Langhammer Supervisor: Bohdan Yeroshenko&nbsp; Opponents: Jacob Billvén and Nikhil Saraswat
Location of presentation: <a rel="noopener" href="https://maps.chalmers.se/#96de5091-96fe-4e70-8def-1faa831680ae" target="_blank" title="Chalmers Maps" data-anchor="#96de5091-96fe-4e70-8def-1faa831680ae">Raven &amp; Fox</a><a rel="noopener" href="https://maps.chalmers.se/#24d8b7be-1f59-4388-be28-a07861b429b2" target="_blank" title="Chalmers Maps" data-anchor="#24d8b7be-1f59-4388-be28-a07861b429b2"></a><a rel="noopener" href="https://maps.chalmers.se/#36a610c1-e9b3-48bd-b01c-9501f9af79fe" target="_blank" title="Chalmers map" data-anchor="#36a610c1-e9b3-48bd-b01c-9501f9af79fe"></a>
Online: <a rel="noopener" href="https://chalmers.zoom.us/j/8250031978" target="_blank" title="Zoom">Zoom</a>

Title of thesis: Pd-Pt nanoparticles for plasmonic sensing of hydrogen in humid environments
Abstract: Using hydrogen as energy storage and as fuel in fuel cells is an attractive idea that has been investigated for decades. One of the big problems with using hydrogen for this purpose is that hydrogen, due to its small size, can easily escape, and only a small amount is required for it to be explosive. To help detect leaks and prevent accidents, a fast and reliable method for the detection of hydrogen gas is necessary. One way to do this is to look at the plasmonic response of nanoparticles to changes in the environment. For this purpose, the most interesting element to utilise is Pd. This is due to its ability to readily dissociate hydrogen on its surface and absorb it into its lattice structure, forming a hydride. The problem with Pd is that it is vulnerable to poisoning effects from several different compounds, one of which being water. One way to circumvent this problem is to use Pt in combination with Pd since Pt is highly active in the catalytic hydrogen oxidation reaction and high sensitivity in plasmonic sensing. This thesis shows that the binary system does indeed solve the problem of poisoning from water and try to understand the interplay of the underlying processes by comparing different compositions both before and after annealing.
Examiner: Christoph Langhammer Supervisor: Carl Andersson, Athanasios Theodoridis, Sara Nilsson&nbsp;&nbsp; Opponent: Hugo Mårtensson&nbsp;
Location of presentation: <a rel="noopener" href="https://maps.chalmers.se/#96de5091-96fe-4e70-8def-1faa831680ae" target="_blank" title="Chalmers Maps" data-anchor="#96de5091-96fe-4e70-8def-1faa831680ae">Raven &amp; Fox</a><a rel="noopener" href="https://maps.chalmers.se/#24d8b7be-1f59-4388-be28-a07861b429b2" target="_blank" title="Chalmers Maps" data-anchor="#24d8b7be-1f59-4388-be28-a07861b429b2"></a><a rel="noopener" href="https://maps.chalmers.se/#36a610c1-e9b3-48bd-b01c-9501f9af79fe" target="_blank" title="Chalmers map" data-anchor="#36a610c1-e9b3-48bd-b01c-9501f9af79fe"></a>

Title of thesis:&nbsp;Optimizing Power Cable Routing using AI&nbsp;
Abstract: Planning underground transmission cable routes is traditionally a labor-intensive and expert-driven task involving multiple, often conflicting, objectives. To meet increasing energy demands, scalable and intelligent methods are needed to enhance efficiency while accounting for diverse spatial constraints. This study investigates how AI-driven optimization algorithms can improve underground cable routing by integrating them with Geographic Information Systems (GIS) data. Key geospatial constraints, such as soil type, land use, and vegetation, are identified and systematically encoded into a unified cost surface using the Analytic Hierarchy Process (AHP). Three algorithmic approaches are implemented and evaluated: Dijkstra’s algorithm (including single- and bi-objective variants), the A* algorithm with weighted heuristics, and a bi-objective Ant Colony Optimization (ACO) algorithm. These are assessed across synthetic and real-world environments, including the planned Gotland link in Sweden. While each model exhibits distinct strengths and limitations, all produce competitive and adaptable routes compared to manual planning, effectively balancing traversal cost and distance, and identifying Pareto-optimal solutions that highlight strategically important areas. . This suggests that the integration of AI and GIS has strong potential to automate and improve cable routing processes. At the same time, the study underscores the importance of tuning both model architecture and environmental representation to maximize real-world applicability, revealing that model performance is not only algorithm-dependent but also highly sensitive to the spatial structure and scaling of the input data.
Examiner: Mats Granath Supervisor: Josef Abriren&nbsp; Opponent: Pontus Kjellström (MPCAS), Niklas Lundblad (MPCAS)&nbsp;
Location of presentation: <a rel="noopener" href="https://maps.chalmers.se/#37d8de3e-1806-4420-92e7-b0b773f4fc88" target="_blank" title="Chalmers Maps" data-anchor="#37d8de3e-1806-4420-92e7-b0b773f4fc88">FL41</a><a rel="noopener" href="https://maps.chalmers.se/#24d8b7be-1f59-4388-be28-a07861b429b2" target="_blank" title="Chalmers Maps" data-anchor="#24d8b7be-1f59-4388-be28-a07861b429b2"></a><a rel="noopener" href="https://maps.chalmers.se/#36a610c1-e9b3-48bd-b01c-9501f9af79fe" target="_blank" title="Chalmers map" data-anchor="#36a610c1-e9b3-48bd-b01c-9501f9af79fe"></a>
Online: <a rel="noopener" href="https://chalmers.zoom.us/j/63341168344" target="_blank" title="Zoom">Zoom</a>

Title of thesis:&nbsp;Advancing emergency operations using sensor fusion with AI features&nbsp;
Abstract: In recent years, society has experienced rapid technological advancements across all fields, pushing the boundaries of what was once thought possible. Emergency services, such as fire departments, have largely been left behind. Operating in hazardous, smoke-filled environments places firefighters at extreme risk, where no compromises can be made on the tools and technologies that support them. This project addresses that gap by exploring the development of a Multi-Environment Camera (MEV-Cam) system designed to aid firefighters during rescue operations in visually impaired environments. The study focuses on the fusion of LiDAR and thermal imaging, investigating how the sensors can complement each other weaknesses to provide more reliable scene understanding.&nbsp; To address these challenges, four tailored datasets were created to simulate the intended application scenario, each containing synchronized data from the sensors. Fusion based models were developed to enable the sensors to complement each other’s weaknesses. For scene understanding, deep learning models were investigated to both LiDAR and thermal data, using architectures based on YOLO and PointNet. Additionally, pose estimation was explored using monocular visual odometry. A major focus of the project was on enhancing the raw collected data to ensure the sensors could be effectively used in this system. In parallel, a hardware prototype was developed to enable efficient data collection in real world environments. Results show that airborne particles significantly degrade LiDAR performance, while thermal sensors remain relatively unaffected. However, sensor fusion can compensate for these limitations. Deep learning models demonstrated the ability to accurately interpret scene structure under degraded conditions. After effective data enhancement, the MEV-Cam system showed improved performance across its various modules. While these results highlight the promise of the technology, they also reveal current limitations and suggest several innovative directions for future work. This project marks the beginning of a new project to support life saving operations, by utilizing the latest sensor technology and AI.
Examiner: Magnus Karlsteen Supervisors: Magnus Karlsteen Opponents: Simon Nygren, Gustav Mosséen&nbsp;
Location of presentation: <a rel="noopener" href="https://maps.chalmers.se/#24d8b7be-1f59-4388-be28-a07861b429b2" target="_blank" title="Chalmers map" data-anchor="#24d8b7be-1f59-4388-be28-a07861b429b2">PJ-salen</a>

Title of thesis:&nbsp;Universal interface growth in substrate etching processes
Abstract: Etching is a process that has been used both for art and in industry for hundreds of years. Although well understood on larger spatial scales, when acting on a small-scale two-dimensional lattice other effects seem to take place. This project has aimed at using a Monte Carlo simulation to study the effects on a honeycomb lattice and study how the system evolves over time depending on the underlying crystal structure and different relative decay rates for solitary atoms.
The implementation shows that during short times the crystal structure very much plays a role in the shapes that develop creating shapes likening a hexagon with slightly rounded corners. As time passes, the holes created become more circular and by increasing reactivity for the atoms that are more solitary, the shape becomes nearly completely circular at large times. This is supported by several metrics such as area error measurement as well as radial error measurements. Moreover, the radius grows linearly with time, indicating that as more atoms become exposed, the more reactions occur, but as the interface grows larger the more atoms need to be annihilated to increase the radius. Longer time frames need to be studied further for more reactive conditions, to see where the hexagonal error will plateau.
Examiner: Johannes Hofmann Supervisors: Johannes Hofmann Opponents: Ole Fjeldså and Gustaf Jonasson Johansson
Location of presentation: <a rel="noopener" href="https://maps.chalmers.se/#24d8b7be-1f59-4388-be28-a07861b429b2" target="_blank" title="Chalmers map" data-anchor="#24d8b7be-1f59-4388-be28-a07861b429b2">PJ-salen</a>

Title of thesis:&nbsp;Pushing the Boundary: Simulating Interface Growth in Bacterial Colonies
Abstract: Bacteria are life forms that exist all over Earth, thriving in both the human body and in nature. Creating models to simulate the complex behaviors that may arise in bacterial colonies is a valuable asset in microbiology and biomedicine, for example these models can be used to study antibiotic resistance. This thesis aims to develop a computational model to simulate bacterial colony growth. The model is based on a lattice growth model and is grown with Eden dynamics. In addition there is a nutrient field representing nutrient concentrations. This nutrient field adds a more realistic complement to the Eden model and can model environmental constraints that impact how and where the colony can grow. The colony grows as bacterial cells consume nutrients and divide, pushing the colony's boundary outward. The colony is more likely to expand into areas with higher nutrient availability. The parameter determining the growth rate and colony longevity is the nutrient consumption coefficient, which controls at what rate nutrients are consumed during growth. The results show how this coefficient affects the colony's size, radial expansion, and roughness of the boundary. Additionally, a stability analysis is conducted to determine the threshold value of the nutrient consumption coefficient required for the colony to sustain itself.
Examiner: Johannes Hofmann Supervisor: Johannes Hofmann Opponent: Rakel Hellberg
Location of presentation: <a rel="noopener" href="https://maps.chalmers.se/#7241f650-27c9-11ef-a21b-017390dd2bcd" target="_blank" title="Chalmers maps" data-anchor="#7241f650-27c9-11ef-a21b-017390dd2bcd">Eva von Bahr</a>

Title of thesis:&nbsp;Tracking meta-dynamic recrystallization in a Ni-based superalloy using time-resolved synchrotron X-ray diffraction
Abstract:&nbsp;
The drive towards more sustainable aviation has gained traction as the global aviation industry expands alongside increasing demands to reduce carbon emissions. This drive has pushed research in materials science aimed at developing alloys capable of withstanding high operating temperatures while maintaining metallurgical stability. The nickel-based superalloy Haynes 282 has emerged as a promising material for use in aero-engine applications. Recrystallization plays a critical role in determining the final microstructure and, consequently, the mechanical properties of metallic materials. This project aims to develop computational methods for studying the dynamics of meta-dynamic recrystallization in Haynes 282, to gain a deeper understanding of how the recrystallization process evolves. The superalloy samples were first deformed at elevated temperatures. Right after the deformation stopped, in-situ synchrotron X-ray diffraction (XRD) measurements were performed while maintaining the constant deformation temperature. Diffraction patterns were recorded as a function of time. A method was developed to separate the signal into contributions from the deformed and recrystallized grains. This enabled the calculation of the recrystallized fraction, and the tracking of recrystallized grain evolution over time. These analyses were applied to samples deformed at different temperatures. For the sample deformed at 1100°C , the recrystallized fraction increased rapidly, from an initial average of approximately 5% to 90% within 120 seconds. Higher deformation temperatures resulted in faster recrystallization kinetics and higher final fractions. The progression of new grains and their size distribution was also studied, showing an increase in both the number of grains and their average size over time.
The results were compared to previous experimental measurements of Haynes 282, where electron backscatter diffraction was used to quantify the recrystallization and grain growth. The findings of this project align with conclusions previously drawn. The methodology developed in this work demonstrates that time-resolved synchrotron XRD data can be used to effectively study recrystallization, enabling high temporal resolution and potentially greater sensitivity to small grains.
Examiner: Magnus Hörnqvist Colliander Supervisors: Magnus Hörnqvist Colliander Opponent: Jonathan Borsander&nbsp;
Location of presentation: <a rel="noopener" href="https://maps.chalmers.se/#24d8b7be-1f59-4388-be28-a07861b429b2" target="_blank" title="Chalmers map" data-anchor="#24d8b7be-1f59-4388-be28-a07861b429b2">PJ-salen</a>

Title of thesis:&nbsp;From magnetic field seed generation to dynamo in collisionless plasmas
Abstract: The origin of the cosmic magnetization present throughout the universe, greatly impacting the development of a diverse set of astrophysical systems, has long been a subject of research. Observations of cosmic magnetic fields, for example in the weakly collisional intracluster medium, show field magnitudes consistent with having been produced through the dynamo process, which causes small seed magnetic fields in plasmas to grow exponentially through the conversion to magnetic energy from kinetic energy in turbulent flows. Magnetohydrodynamics, the standard method of modeling dynamo, is not valid for weakly collisional systems since it assumes that a high collisionality dominates over competing small-scale processes, such as magnetic field seed-generating plasma instabilities. However, a fully kinetic treatment of the problem in electron-proton plasmas is prohibitively expensive numerically. &nbsp;
In this thesis an advanced collisionless fluid plasma model, with a complexity between that of magnetohydrodynamics and a kinetic model, is used to study magnetic field growth in a plasma in the presence of driven turbulent flows. A significant decoupling of the ion and electron dynamics is observed for the employed mass-ratio, allowing access to previously unexplored physics. The simulations demonstrate both the dynamo process and magnetic field seed generation through the electron Weibel instability. The fluid and magnetic Reynolds numbers, key quantities in classical dynamo theory, are generalized for a collisionless system. This is done by studying the dependence of flow and magnetic energy damping rates on the strength of the pressure isotropization encoded in the ion and electron heat flux closures of the fluid model. A scan over the effective magnetic Reynolds number is performed for the dynamo simulations by adjusting the strength of the electron closure. Strong pressure isotropization from the closure leads to behavior of the magnetic field growth expected from classical dynamo theory, whereas a small isotropization results in seed generation from pressure anisotropy-driven instabilities dominating, as previously observed in kinetic simulations of electron-positron plasmas.&nbsp;
Examiner: Istvan Pusztai Supervisor: Istvan Pusztai Opponent: Liam Antonsson 
Location of presentation: <a rel="noopener" href="https://maps.chalmers.se/#7241f650-27c9-11ef-a21b-017390dd2bcd" target="_blank" title="Chalmers maps" data-anchor="#7241f650-27c9-11ef-a21b-017390dd2bcd">Eva von Bahr</a>
Online: <a rel="noopener" href="https://chalmers.zoom.us/j/62452572114?pwd=daFN9pigWaUHkDRRGGsavI9lqzEoPN.1&amp;from=addon" target="_blank" title="Zoom" data-anchor="?pwd=daFN9pigWaUHkDRRGGsavI9lqzEoPN.1&amp;from=addon">Zoom</a>

Title of thesis:&nbsp;Modeling of Electron-Phonon Coupling in Perovskites
Abstract:&nbsp;Electron-phonon coupling plays a central role in determining key material properties, such as carrier mobility and optical response. However, accurately modeling these interactions remains computationally demanding and methodologically complex. In this work, different modeling approaches to electron-phonon coupling are explored and applied to two prototypical perovskite systems: the halide perovskite cesium lead bromide and the oxide perovskite calcium titanate. For the oxide, the study also includes Ruddlesden–Popper phases. The objective is to understand temperature-dependent electronic properties and to develop models for band edge fluctuations and polarization behavior. For cesium lead bromide, the study focuses on the cubic phase. During molecular dynamics simulations, band gap and valence band maximum data were obtained from density functional theory calculations, and corresponding phonon mode coordinates were extracted. A feature-based model was developed to describe band edge fluctuations as a function of squared mode coordinates. Several feature selection methods were tested, and the model was found to adequately capture fluctuations in the valence band maximum using a subset of the total phonon modes. For calcium titanate, tensorial neuroevolution potentials were trained to predict Born effective charges using density functional theory data from the perovskite structure and two Ruddlesden-Popper phases. Subsequently, predictions were made for Ruddlesden-popper phases outside of the training set. The predicted Born effective charges were used to calculate spontaneous polarization, and the model successfully reproduced the polarization drop across the ferroelectric–paraelectric phase transition.
Examiner: Paul Erhart Supervisors: Erik Fransson Opponent: Felix Uddén
Location of presentation: <a rel="noopener" href="https://maps.chalmers.se/#24d8b7be-1f59-4388-be28-a07861b429b2" target="_blank" title="Chalmers map" data-anchor="#24d8b7be-1f59-4388-be28-a07861b429b2">PJ-salen</a>

Title of thesis:&nbsp;Self-trapped excitons in the surface of BiVO4
Abstract: In recent years, BiVO4 has emerged as one of the most promising photocatalysts for usage in solar-driven water-splitting applications. Its favourable placement of band edges close to the redox potentials of water, together with the physical stability of the material, makes it stand out from many of its competitors. However, previous studies have demonstrated that the bulk structure of BiVO4 suffers from considerable charge localisation, with reduced charge mobility and worsened photocatalytic properties as a result. This study aims to continue these investigations by analysing the formation of localised states at the material-vacuum interface. Using hybrid density functional theory, simulations are performed on two different surface structures in order to identify the most commonly appearing charge localisations, as well as compute their respective formation energies. For both surfaces, several different localised states are found, with self-trapped excitons appearing both in the direct vacuum interface and in the layer immediately below. By providing energy levels deep within the band gap of BiVO4, these localisations are shown to potentially hamper the overall capability of the material to drive the redox reaction
Examiner: Julia Wiktor Supervisors: Tobias Möslinger Opponent: Leo Ögren Westin and Nikhil Saraswat
Location of presentation: <a rel="noopener" href="https://maps.chalmers.se/#24d8b7be-1f59-4388-be28-a07861b429b2" target="_blank" title="Chalmers map" data-anchor="#24d8b7be-1f59-4388-be28-a07861b429b2">PJ-salen</a>

Title of thesis:&nbsp;Electromagnetic modeling of the effect of plasma sheath on radar signatures of hypersonic targets
Abstract: Hypersonic missiles have been suggested to have significantly different radar signatures compared to similar slower vehicles. This has been attributed to the plasma sheath that forms when high-temperature air in the hypersonic shock front partially ionizes air molecules. Under certain conditions, the free electrons in this plasma can absorb electromagnetic radiation. It is unclear whether this effect could dampen the scattered radiation from targeted hypersonic missiles, effectively making them stealthy to radar. However, the physics of plasma-induced stealth of hypersonic vehicles is quite complex, involving the spatial distributions of plasma and electron–neutral collision frequencies as well as the microwave frequency. For this reason, broad claims about the stealth of hypersonic vehicles must be supported by accurate numerical simulations. In this thesis, the effects of a thin plasma sheath have been studied in various settings and geometries with the aid of electromagnetic as well as hydrodynamic simulations.
The ability of a plasma to shield rough surfaces, which otherwise have a large radar signature, was investigated by simulating the electromagnetic wave scattering of a plasma-covered metal slab with surface roughness in a 2D geometry. Somewhat unexpectedly, the plasma did not behave like a mirror for low frequencies and when the radiation was expected to be attenuated it was instead amplified. Additionally, the ability of the plasma sheath to distort scattered electromagnetic waves was studied analytically. The distortion was exaggerated in this simple model compared to a real-world scenario, but still it was negligible for almost every frequency. Therefore the distortion can be neglected. Finally, to study the effect of a plasma sheath with a realistic plasma density, the plasma frequency and collision frequency distributions were computed for a metal sphere flying at speeds of 3-5 km s−1 in an ambient pressure representative of a 40 km altitude. While the calculated radar signature has a limited accuracy below 1 GHz, we can conclude that in that frequency range the presence of the plasma sheath can either increase or reduce the radar signal, depending on the frequency. At some frequencies the decrease was as large as 10 dB. Our results also indicate that the plasma sheath only has minor effects on the radar signatures of hypersonic vehicles traveling below 3 km s−1, for the microwave bands above 1 GHz.
Examiner: Istvan Pusztai Supervisor: Andréas Sundström&nbsp; Opponent: Nils Müller
Location of presentation: <a rel="noopener" href="https://maps.chalmers.se/#7241f650-27c9-11ef-a21b-017390dd2bcd" target="_blank" title="Chalmers maps" data-anchor="#7241f650-27c9-11ef-a21b-017390dd2bcd">Eva von Bahr</a>
Online: <a rel="noopener" href="https://chalmers.zoom.us/j/68504068099?pwd=ajUWKmwdOePpW61PhdKhSUzA2gBnxq.1&amp;from=addon" target="_blank" title="Zoom" data-anchor="?pwd=ajUWKmwdOePpW61PhdKhSUzA2gBnxq.1&amp;from=addon">Zoom</a>

Title of thesis:&nbsp;Hydrogen in Ferrite Systems with Carbide Precipitates
Abstract:&nbsp;This work investigates the behavior of hydrogen in ferritic steel, two distinct complex metal carbides, and hybrid ferrite–carbide systems using computational methods. A neural network potential was trained on density functional theory data and used to perform molecular dynamics simulations on large-scale systems over nanosecond timescales. Hydrogen diffusivity was evaluated across ferrite and carbide systems of varying compositions. The two carbides were found to exhibit distinct diffusion pathways and hydrogen trapping sites. In the combined ferrite–carbide systems, hydrogen trapping occurred both at the interface and within the carbide bulk, with some differences between the two carbide types.
Examiner: Paul Erhart Supervisors: Erik Fransson Opponent: Olle Lyth Andersson
Location of presentation: <a rel="noopener" href="https://maps.chalmers.se/#24d8b7be-1f59-4388-be28-a07861b429b2" target="_blank" title="Chalmers map" data-anchor="#24d8b7be-1f59-4388-be28-a07861b429b2">PJ-salen</a>

Title of thesis:&nbsp;Kinetic simulations of the proton-alpha instability in collisionless shocks
Abstract: The solar wind is a dynamic stream of collisionless plasma originating from the Sun. When it reaches the Earth's magnetosphere, it abruptly slows down from super-Alfvénic to sub-Alfvénic speeds, creating a bow shock. This compresses and heats the inflowing plasma. The solar wind consists primarily of electrons, protons and alpha-particles, which interact differently with the shock. A relative drift between protons and alpha particles develops, which can trigger a streaming instability, driving waves. Such waves have previously been identified in spacecraft observations. The dependence of the instability on the particle species temperature, density, and flow velocity has previously been investigated using the dispersion relation for an unmagnetized plasma.
In this work, we investigate this proton-alpha streaming instability using two-dimensional fully kinetic Particle-In-Cell simulations. This approach offers the flexibility to freely choose the initial conditions while simulating the full non-linear dynamics. We study the parametric dependence of the instability on the flow velocity of the alpha particles, the density ratio of the ions as well as the influence of a background magnetic field. In particular, we want to quantify the heating of different particle species due to the instability. Additionally, we aim to test the applicability of the simplified linear theory.
Our simulations show that the instability mediates a strong energy exchange from the flow speed of the alphas into plasma heating of the ions. We attribute this to a Landau resonance between the proton and the alpha distribution, which produces large plateaus in velocity space. The electron dynamics do not change significantly. The wave properties are consistent with the linear theory when the external magnetic field is small. Generally, the angle between the wave and the drift direction increases with the flow speed. The fraction of energy converted to plasma heating reaches a maximum at a wave angle of 45 deg to the drift direction. Overall, the existence of a background magnetic field slightly suppresses the instability, due to the magnetization of electrons. We conclude that the proton-alpha streaming instability can play an important role in heating proton and alpha particles across collisionless shocks.
Examiner: Istvan Pusztai Supervisor: Konrad Steinvall Opponent: Lise Hanebring
Location of presentation: <a rel="noopener" href="https://maps.chalmers.se/#7241f650-27c9-11ef-a21b-017390dd2bcd" target="_blank" title="Chalmers maps" data-anchor="#7241f650-27c9-11ef-a21b-017390dd2bcd">Eva von Bahr</a>
Online: <a rel="noopener" href="https://chalmers.zoom.us/j/66511118346?pwd=AyVGa5AHrnmCp7uK4e1VmtJE55NhQv.1&amp;from=addon" target="_blank" title="Zoom" data-anchor="?pwd=AyVGa5AHrnmCp7uK4e1VmtJE55NhQv.1&amp;from=addon">Zoom</a>

Title of thesis:&nbsp;AI-Driven Real Estate Valuation in Non-Metropolitan Sweden: A Hybrid Modeling Approach
Abstract: This thesis investigates the potential of machine learning to enhance real estate valuation accuracy in non-metropolitan regions of Sweden, where traditional appraisal methods often falter due to sparse transaction data and diverse property characteristics. Using a dataset of approximately 90,000 residential properties across Östergötland, Småland, and Blekinge, the study evaluates a hybrid modeling approach combining artificial neural networks and gradient boosting. The ANN leverages the entity embeddings for complex categorical variables. Additionally, feature importance and interpretability techniques, such as SHAP values, are employed to uncover valuation drivers and enhance model transparency. Results indicate that ML-based models can significantly outperform traditional hedonic pricing models in both accuracy and robustness, particularly in data-sparse settings. The findings support the use of hybrid AI techniques as viable complements to conventional valuation tools, offering improved scalability, consistency, and insight into property value determinants for stakeholders such as lenders, appraisers, and public agencies.&nbsp;
 Examiner: Mats Granath Supervisor: Magnus Persson&nbsp; Opponent: Robin Böhme, Lucas Kallenberg
Location of presentation: <a rel="noopener" href="https://maps.chalmers.se/#37d8de3e-1806-4420-92e7-b0b773f4fc88" target="_blank" title="Chalmers Maps" data-anchor="#37d8de3e-1806-4420-92e7-b0b773f4fc88">FL41</a><a rel="noopener" href="https://maps.chalmers.se/#24d8b7be-1f59-4388-be28-a07861b429b2" target="_blank" title="Chalmers Maps" data-anchor="#24d8b7be-1f59-4388-be28-a07861b429b2"></a><a rel="noopener" href="https://maps.chalmers.se/#36a610c1-e9b3-48bd-b01c-9501f9af79fe" target="_blank" title="Chalmers map" data-anchor="#36a610c1-e9b3-48bd-b01c-9501f9af79fe"></a>
Online: <a rel="noopener" href="https://chalmers.zoom.us/j/65533019596" target="_blank" title="Zoom">Zoom</a>

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Master thesis presentations at Physics

May 12, 2025

10:00- 11:00 Johanna Vestgöte Norell & Fabian Kaneby, MPDSC

May 21, 2025

10:00- 11:00 Tim Pagrell, MPPHS

11:00- 12:00 Jennie Berndtson, MPMED

May 26, 2025

13:15- 14:00 Linus Kullmyr & Emily Blixt, MPCAS

15:00- 16:00 Marcus Hansen, MPCAS

May 27, 2025

13:00- 14:00 Hampus Hansen, MPPHS & Theodor Hansson, MPCAS

June 2, 2025

9:00- 10:00 August Klynne & Malte Åqvist, MPCAS

10:00- 11:00 Ebba Danielsson & Josefin Ewerman, MPBIO

13:00- 14:00 Andreas Erlandsson, MPPHS

14:00- 15:00 Simon Dovrén, MPPHS

June 3, 2025

9:00- 10:00 Björn Johansson, MPPHS

10:00- 11:00 Oscar Karlsson, MPCAS

10:00- 11:00 Hugo Mårtensson, MPPHS

11:00- 12:00 Ahsin Hafeez, MPSES

11:00- 12:00 Erman Kulaglic & Jakob Bergman, MPCAS

13:00- 14:00 Sophie You, MPMED

13:15- 14:15 Ole Fjeldså & Gustaf Jonasson Johansson, MPCAS

13:00- 13:45 Victor Enevold, MPPHS

13:45- 14:30 William Colliander, MPPHS

June 4, 2025

10:00- 11:00 Johan Larsson & Oliver De Rosa, MPCAS

10:30- 11:30 Joel Carlsson, MPCAS & Denis Grahovic, MPSYS

June 5, 2025

14:00- 15:00 Tobias Wallström, MPCAS

15:00- 16:00 Markus Utterström, MPCAS

15:30- 16:30 Lovisa Åkesson, MPPHS

June 10, 2025

13:00- 14:00 Lise Hanebring, MPPHS

13:00- 14:00 Leo Ögren Westin, MPPHS

14:00- 15:00 Olle Lyth Andersson, MPPHS

14:10- 15:10 Liam Antonsson, MPPHS

15:00- 16:00 Felix Uddén, MPPHS

15:20- 16:20 Nils Müller, MPCAS

June 13, 2025

13:15- 14:15 Leonard Smedenman & Teddy Sallén, MPCAS