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Maria Gabriela Maridueña Zavala

• 30/04/2026
  
• 4 p.m.
• Venue: Campus Groenenborger, G.T.135
• Supervisors: Gerrit Beemster, Hamada AbdElgawad & Carlos Noceda
• Department of Biology

Abstract

Plant viruses pose significant constraints on crop productivity, with papaya exhibiting particular susceptibility due to the rapid impact of viral infections on fruit yield and quality. Systems-level approaches, including transcriptomics, metabolomics, and epigenomics, are crucial for elucidating the complex host responses that underlie susceptibility and tolerance. Babaco mosaic virus (BabMV) remains insufficiently characterized at the molecular level, while integrative multi-omics approaches are still rarely applied in plant virus research, together limiting the identification of effective ways to manage the disease and the selection of more resistant papaya plants. This thesis addresses this knowledge gap by presenting an integrated, cultivar-specific framework for understanding BabMV–papaya interactions. ​ ​

The main objective of this doctoral thesis was to investigate the temporal response of Carica papaya to Babaco mosaic virus (BabMV) through a multi-layered integration, supported by a novel genomic assembly. The analyses reveal that the papaya response constitutes a synchronized transition through distinct biological phases, rather than a static stress state. ​ ​

The results demonstrate that BabMV infection progresses through distinct early and late stages, as reflected in transcriptomic, metabolomic, and epigenetic data. The early stage is characterized by cell-wall reinforcement, and initial effects on photosynthesis, including chlorophyll decline, without complete metabolic disruption. Concurrently, metabolites associated with sulfur metabolism, phenolic compounds, and tricarboxylic acid (TCA) cycle organic acids increase, indicating early carbon reorganization and the initiation of secondary metabolism activation. ​ ​

In contrast, the late stage is characterized by the predominance of stress-response, redox, and antioxidant programs, consistent with sustained oxidative pressure and intensified metabolic reprogramming. Defense-related metabolites, particularly those associated with proline, polyamine, and phenylpropanoid pathways, become increasingly prominent, alongside elevated phenylalanine ammonia-lyase (PAL) activity and enhanced antioxidant buffering. These later responses are accompanied by broader DNA methylation remodeling, with genes involved in catalytic activity, endomembrane organization, sugar metabolism, and chloroplast redox regulation. Collectively, these findings indicate that BabMV infection represents a coordinated transition from early adjustment to a later defense- and stress-dominated state, rather than a simple on-off response. ​ ​

This thesis offers new insights into BabMV–papaya interactions by constructing a temporally resolved, multi-omics perspective of infection. Together, these findings provide a more complete framework for understanding disease progression and create a stronger basis for future studies aimed at improving papaya virus management. ​ ​

Ali Ghojavand

• 27/04/2026
  
• 4 p.m.
• Venue: Campus Groenenborger, US.025
• Supervisors: Milorad Milosevic, Cem Sevik & Eric Bousquet
• Department of Physics

Abstract

Magnetoelectric (ME) coupling in multiferroics has attracted considerable attention over the past decade due to its wide range of applications in spintronic devices, data storage technologies, and electrically tunable microwave components. Different ME coupling mechanisms underpin distinct device functionalities, and a fundamental understanding of their dynamics remains essential for advancing high-performance multiferroic materials. Recently, two-dimensional van der Waals magnets have emerged as a particularly promising platform for realizing and tuning such ME phenomena at the atomic scale. Since the discovery of antiferromagnetic order in FePS3 crystals, this rapidly growing family of materials has revealed a wealth of exotic magnetic phases and offers a versatile foundation for engineering heterostructures due to their weak interlayer interactions. Within this family, NiX2 (X = I, Br, Cl) monolayers stand out as experimentally realized systems, successfully fabricated in both few-layer and monolayer forms. ​ ​

In this thesis, we develop effective design strategies to tailor the ME properties of NiX2 monolayers. We first investigate their intrinsic magnetism, where the interplay between magnetic frustration and biquadratic exchange defines the ground-state properties. We then establish strain as a powerful control parameter: both biaxial and uniaxial strains prove highly effective in tuning critical temperatures, and stabilizing a variety of spin configurations, including ferromagnetic, spiral, and skyrmionic phases. ​ ​

Beyond magnetism, we demonstrate that strain directly controls the magnitude and direction of electric polarization by tuning the noncollinear spin textures and ME tensor components. This reveals a robust and highly tunable ME coupling in these materials. ​ ​

This comprehensive exploration is extended to evaluate the impact of point defects, revealing their critical role in pinning magnetic textures and subsequently tailoring the resulting spin-induced electric polarization. All the findings together establish a multi-faceted engineering strategy, combining strain and defect control, to master coupled magnetic and ferroelectric orders in a single 2D material, paving the way for innovative device concepts in nanoelectronics and spintronics.

Bereket Alemayehu Arba

• 23/04/2026
  
• 2.30 p.m.
• Venue: Campus Drie Eiken, Building O, Aula O.5
• ​​
• Supervisors: Herwig Leirs & Aberham Megaze
• Department of Biology

Abstract

Cutaneous leishmaniasis (CL) is a vector-borne disease transmitted by the bite of infected female sandflies. In Ethiopia, CL is primarily caused by Leishmania aethiopica and is considered a zoonosis involving sandfly vectors and hyrax reservoirs. Complex ecological and epidemiological interactions among hosts, vectors, and environmental conditions influence its transmission. Understanding these dynamics is essential for designing effective prevention and control strategies. This PhD study aimed to: (i) assess community awareness and epidemiology of CL, and (ii) investigate the ecology of CL, with emphasis on sandfly abundance and distribution. These aims were addressed in four components: (1) assessing community knowledge, attitudes, and practices (KAP) regarding the disease; (2) exploring the disease eco-epidemiology and confirming active transmission among humans, sandflies, and hyraxes; (3) determining sandfly abundance and distribution along altitudinal gradients, focusing on Phlebotomus pedifer; and (4) assessing the abundance of P. pedifer across habitat types. A community-based cross-sectional design was used for the KAP and epidemiological investigations. Structured questionnaires were administered to household heads. Suspected CL cases were sampled for laboratory confirmation. Sandflies were collected indoors and outdoors using CDC light and sticky traps, and hyraxes were captured using snare traps. Human, sandfly, and hyrax samples were screened for Leishmania infection, and blood-meal analysis was conducted to identify blood-meal origins for sandflies. Data were statistically analyzed, and appropriate tests were performed for associations. The results showed a low level of community knowledge of CL and its prevention. The overall CL prevalence (active lesions and scars combined) was 12.7%, with children being the most affected. L. aethiopica infection was confirmed in humans, sandflies, and hyraxes, demonstrating active local transmission. Sandflies from the genera Phlebotomus and Sergentomyia were identified. Humans were the predominant blood-meal source for P. pedifer. Sandfly abundance and distribution varied by altitude, habitat, and season. Phlebotomus pedifer density increased with altitude and was highest in highland areas, whereas P. alexandri, P. gibiensis, and Sergentomyia spp. were more common at lower elevations. In conclusion, the study confirms active CL transmission and highlights significant knowledge and prevention gaps within the study communities. The findings emphasize the need for targeted health education and vector control strategies, particularly around hyrax dwellings and human residences in the study areas.

Hanan Barich

• 21/04/2026
  
• 4 p.m.
• Venue: Campus Drie Eiken, Building O, room O.1
• Supervisor: Karolien De Wael
• Department of Bioscience Engineering

Abstract

Growing environmental concerns and stricter sustainability regulations highlight the urgent need for improved monitoring of chemical pollutants in water systems. Among these, phenolic compounds are particularly problematic due to their widespread use and associated risks, including toxicity, persistence, bioaccumulation and endocrine-disrupting effects. Their occurrence in industrial and environmental waters highlights the need for advanced detection strategies that are both selective and sensitive.

This PhD research addresses this challenge through the development of a combi-electrosensor, combining square voltammetric (SWV) and photoelectrochemical (PEC) sensing, for the selective and sensitive detection of phenolic compounds in industrial wastewater. The SWV sensor enables selective identification of phenolic compounds via electrochemical fingerprinting, while the PEC sensor allows sensitive quantification. The PEC sensor uses a type II photosensitiser (PS), a highly fluorinated zinc phthalocyanine derivative, which generates singlet oxygen (1O2) under illumination to oxidise phenolic compounds. The oxidised phenols are subsequently reduced at the electrode surface, establishing an electrocatalytic redox cycle that generates a measurable photocurrent, indicative for the total phenol content.

This thesis includes the optimisation and validation of the SWV sensor for selective phenol detection, followed by its integration with the PEC sensor to create a complementary system capable of both identification and quantification. The resulting combi-electrosensor demonstrated strong performance in industrial wastewater samples, outperforming commercial test kits in terms of analysis time, selectivity (via SWV) and sensitivity (via PEC), with detection limits reaching the nanomolar range. To enhance sustainability and accessibility, an alternative commercially available PS, pheophorbide a, was evaluated, reducing reliance on fluorinated compounds. In parallel, graphite-based screen-printed electrodes were developed in-house, enabling reproducible, cost-effective and scalable sensor fabrication with performance comparable to commercial electrodes. Finally, the integration of the combi-electrosensor into a flow system highlighted the adaptability and versatility of this approach, enabling real-time, continuous monitoring of phenolic compounds. This dual-mode in-flow sensing approach enhances sensitivity, while offering additional benefits such as SWV sensor reusability, faster analysis, automated sample injection, and real-time detection of low-concentration phenols. Altogether, it provides a robust and efficient strategy that outperforms commercial test kits and supports sustainable, cost-effective water quality monitoring.

Jenevieve Hara

• 21/04/2026
  
• 2 p.m.
• Venue: Campus Drie Eiken, Building R, Aula R3
• ​
• Supervisors: Raewyn Town & Ronny Blust (University of Antwerp) - Jana Asselman & Colin Janssen (University of Ghent)
• Department of Biology

Abstract

Micro- and nanoplastics (MNPs) are pervasive in marine environments, posing significant ecological risks due to their persistence and physicochemical heterogeneity. Environmental aging alters particle properties, influencing interactions with marine organisms. However, mechanistic understanding of their biological effects under environmentally realistic conditions remains limited. This thesis addresses these gaps by elucidating the biological reactivity of aged MNPs, focusing on cellular, tissue, and organismal responses under environmentally relevant exposure scenarios. ​ ​

A systematic assessment of MNP toxicity in marine invertebrates was conducted using chronic exposure data from the Toxicity of Microplastic Explorer (ToMEx) for Aquatic Organisms database (version v2.0). Species sensitivity distributions were constructed based on fitness-related endpoints, accounting for two distinct toxicity mechanisms. Derived HC₅ values were used to propose provisional thresholds for protecting marine invertebrate communities. ​ ​

Complementary 𝘪𝘯 𝘷𝘪𝘷𝘰 and 𝘪𝘯 𝘷𝘪𝘵𝘳𝘰 experiments provided integrative evidence that aged polyethylene terephthalate (PET) MPs (~1.9 µm) and NPs (~0.68 µm) elicit cellular- and tissue-level effects in mussels (𝘔𝘺𝘵𝘪𝘭𝘶𝘴 𝘦𝘥𝘶𝘭𝘪𝘴) under environmentally relevant exposure conditions. Results revealed both immediate cellular perturbations and recovery potential. Histopathological analyses demonstrated structural alterations in key organs, particularly the gills, indicating potential impairment of essential physiological functions. Although no mortality or growth effects were observed, exposure induced systemic sublethal responses that may compromise organismal fitness under chronic or combined stress conditions. ​ ​

Through label-free 3D live-cell imaging by holotomography, unlabeled aged PET NPs were successfully resolved within living hemocytes, revealing particle internalization and localization patterns, as well as associated morphological changes. ​ ​

Overall, this thesis advances mechanistic understanding, spanning from community-specific hazard thresholds to the intricate cellular mechanisms underlying particle-induced toxicity. ​ ​

Benjamin Vandersmissen

• 20/04/2026
  
• 5 p.m.
• Venue: Stadscampus, S.R.219
• ​​
• Supervisor: José Antonio Oramas Mogrovejo
• Department of Computer Science

Abstract

After the discovery that the training of neural network can be vastly accelerated by exploiting the parallelization capacities of commercially available GPUs, deep learning models have been used to tackle increasingly complex problems. Together with the complexity of these problems, the number of trainable parameters in a neural network has also seen a drastic increase. While additional parameters are often necessary to tackle more complex problems, most models have vastly more parameters than strictly required for the specific task. This has the effect that computational costs in both the training and inference phase are significantly higher than they could be. ​ ​

This overparameterization is often deemed necessary for good convergence behaviour on the problem setting. Indeed, larger models often generalize better than smaller models. To reduce computational cost, techniques have been introduced that shrink the model while retaining the same generalization. Because naively training a smaller network results in worse generalization, typical approaches start from a large dense network that is fully trained, and condense their encoded representations into a smaller network. In this thesis we explore two different techniques to achieve this, namely output matching, and weight pruning. ​ ​

First we introduce Twin Network Augmentation (TNA) which is a methodology that minimizes the distance between the outputs of two small neural networks during training. We show that this method results in better generalization than training a single bigger neural network, while requiring the same computational resources during training, and being more efficient during inference. We furthermore extend this formulation by introducing techniques promoting smarter matching of the outputs, and generalize the formulation to include an arbitrary number of neural networks. ​&�Բ�����;

Next, we investigate the Lottery Ticket Hypothesis (LTH). This hypothesis states that within any dense overparameterized network there exists a sparse network that can be trained to the same generalization as the dense network. Specifically we focus on different aspects of the common algorithm – Iterative Magnitude Pruning (IMP) – to generate these sparse networks, namely the pruning criterion determining which connections should be removed from the network, and analyze the impact of different hyperparameters on generalization. ​ ​

Finally, we integrate Twin Network Augmentation (TNA) with the Iterative Magnitude Pruning (IMP) algorithm and employ it to significantly reduce the computational cost associated with finding sparse trainable neural networks. Our contributions in this work thus serve to push the boundaries of parameter-efficient neural networks. ​ ​

Bharadwaj Mysore Ramesha

• 02/04/2026
  
• 1 p.m.
• ​
• Supervisor: Vera Meynen
• Department of Chemistry

Abstract

Porous materials have revolutionized the world of chemistry by the role they play in catalysis, separation, gas storage, carbon capture, waste management and many other (industrial) applications. Hybrid porous materials are a subclass of porous materials wherein inorganic and organic precursors are combined synergistically to obtain porous frameworks whose surface chemistry and pore architectures are adjusted to fit the application. However, the limited solubility of Ti(IV) ions and the high reactivity of titania precursors often leads to materials that are poorly organized, lacking controlled porosity. This is further complicated by the addition of organodiphosphonic precursors whose chelating ability is dependent on the phosphonic functionality (ester versus acid). In this PhD, insights into the formation of titanium phosphonates by combining titanium alkoxides with organodiphosphonic precursors were achieved by identifying the role of key parameters in the synthesis protocol, such as the choice of organodiphosphonic precursor, solvent, and amount of acid on the formation of hybrid titanium phosphonate is studied. The reaction between titanium alkoxides and diphosphonic acids result in rapid precipitation with limited control over the structure of the titanium phosphonate formed. In contrast, the reaction between titanium alkoxides and diphosphonic esters results in the formation of TiO2 clusters that are too weakly bonded with the phosphonic moiety. As phosphonic esters and acids present two extremes in reactivity, the influence of varying HCl content, enabling (partial) in-situ hydrolysis of the phosphonic ester precursor, was evaluated while all other ingredients were kept constant. The fate of the organodiphosphonic ester (TEPD) during the formation of hybrid titanium dioxide phosphonates was followed by 1H and 31P NMR spectroscopy. It revealed that varying the HCl amounts mainly affects the degree of hydrolysis of TEPD. The change in hydrolysis degree with HCl content leads to changes in the surface bonding dynamics resulting in the variation in particle aggregation. Moreover, when altering the carbon content of the alcohol used in the synthesis, the hydrolysis degree of TEPD could be broadly varied. As the carbon content of alcohol increases the degree of hydrolysis of TEPD increases in the order of n-butanol > isopropanol > ethanol. This adds knowledge to the field of hybrid titanium phosphonates synthesis, that in a next step will be used to systematically alter materials with the aim to tune their performance in applications such as catalysis, separation and proton conduction.

Philip Ley

• 02/04/2026
  
• 5 p.m.
• Venue: UCSIA - Manresazaal, Koningstraat 2, 2000 Antwerpen
• Supervisors: Filip Meysman & Jeanine Geelhoed
• Department of Biology

Abstract

In 2012, a novel group of long, filamentous bacteria was discovered in the seafloor. It was shown that these so-called “cable bacteria” facilitate electrical currents over centimeter distances. Somehow, living organisms are capable of efficiently conducting electricity. Later on, it was found that the cell envelope of these cable bacteria harbors specialized conductive wires. The conductivity of these wires is extremely high, and supersedes that of any other biomaterial. This discovery holds great promise for the development of new conductive bio-based electronic materials.

In this PhD project, we investigated different aspects of the phylogenetic and morphological diversity of cable bacteria. Currently, there are two established genera of cable bacteria: Candidatus Electrothrix, mainly inhabiting saltwater sediments, and Candidatus Electronema, predominantly living in freshwater sediments. However, the overall genetic diversity of cable bacteria is likely underestimated. We expanded the current phylogenetic tree of the cable bacteria clade by sequencing the 16S rRNA gene of cable bacteria from various unstudied locations and compiled public sequencing data. In total, we identified more than 90 potential species-level clades across six genus-level clusters, including a distinct cluster of potential deep sea-inhabiting species.

Besides the large phylogenetic diversity, different cable bacteria species are also highly variable in their morphological traits. We conducted a systematic morphological investigation of the cable bacteria clade using high-resolution microscopy images. While filaments of the same strain show little morphological variation, different species can vary significantly in their filament diameters and the number of ridges. Filaments with larger diameters show a larger capacity to transport electrons, as characterized by the larger number of conductive wires and the area of the wires.

Among the newly identified cable bacteria in this project, one species stood out due to its distinct phylogenetic placement between the two established cable bacteria genera. After developing a single-species culture, we characterized its morphology and genomic potential. It showed distinct, broad ridges and produced tube-shaped, extracellular sheaths. Interestingly, this species shares metabolic pathways and genes with both established genera. We proposed the name Candidatus Electrothrix yaqonensis YB6. As little is known about the formation of extracellular sheaths in cable bacteria, we selected sheath-forming cable bacteria and characterized the prevalence, detailed structure and chemical composition of these sheaths using different microscopy and spectroscopic methods. Sheaths consist of highly organized, parallel nanofibrils which are composed of anionic polysaccharides. Potential functions are the protection from grazers or aid in motility.

Lucilla Boito

• 02/04/2026
  
• 4 p.m.
• Venue: Stadscampus, SR 004
• ​ 
• Supervisor: Sara Vicca
• Department of Bioscience Engineering

Abstract

Climate change constitutes one of the most urgent global challenges, driven predominantly by rising atmospheric concentrations of greenhouse gases. To remain within the temperature limits stipulated by the Paris Agreement, scenario analyses increasingly indicate that substantial atmospheric carbon dioxide removal will be required alongside rapid emission reductions. Within this context, enhanced weathering (EW) of silicate minerals has emerged as a promising carbon dioxide removal (CDR) strategy. By accelerating natural silicate dissolution, EW removes atmospheric CO₂. However, despite accelerating interest in EW, significant uncertainties persist regarding its interactions with the complex biogeochemical processes of agricultural soils, most notably soil organic carbon (SOC) dynamics, nitrogen cycling (with particular attention to nitrous oxide (N₂O) emissions), and crop performance. ​ ​

This dissertation addresses these uncertainties through controlled mesocosm experiments and a field trial in Westmalle, Belgium, employing both natural silicates (basalt) and steel slags, derived from industrial steel production. Across all experiments, EW consistently elevated soil pH and base cation availability, confirming its capacity to counteract soil acidification. In contrast, crop biomass, nutrient uptake and broader agronomic performance exhibited only minimal responses, suggesting that agronomic co benefits are likely limited in already relatively fertile agricultural systems. This thesis also shows that EW affect SOC cycling, with responses strongly shaped by biotic interactions: basalt reduced soil organic matter decomposition only in plant free mesocosms, indicating SOC stabilization. In planted treatments, basalt increased rhizosphere activity, underscoring the central role of biological processes in governing EW outcomes. ​ ​

EW also influenced nitrogen cycling, with steel slags treatments increasing N₂O emissions, thereby revealing a risk associated with EW deployment. Heavy metal mobilisation due to silicate application remained generally low, yet measurable increases in nickel highlight the need for continued environmental monitoring of EW to ensure its safe and sustainable application. Across the thesis, mineralogical differences among feedstocks drove variability in agronomic responses and greenhouse gas fluxes, emphasizing the critical importance of feedstock selection. ​&�Բ�����;

Overall, the findings demonstrate that EW can contribute to SOC stabilization and mitigate soil acidification, yet its net climate effect depends on interactions among feedstock mineralogy, soil properties and biological processes. These results highlight the necessity for monitoring, reporting and verification frameworks to account not only for inorganic carbon sequestration but also for organic carbon cycling and emissions of non-CO2 greenhouse gases such as N2O. This dissertation provides a more comprehensive understanding of EW and a foundation for its responsible implementation in agricultural landscapes.

Koray L. Iroc

• 30/03/2026
  
• 4 p.m.
• Venue: Campus Groenenborger, US.025
• ​​
• Supervisors: Joke Hadermann & Nick Schryvers
• Department of Physics

Abstract

Nuclear fusion is regarded as a promising long-term solution for meeting the rising global energy demand, while offering a potential for abundant, intrinsically safe and low-carbon emission electricity. However, achieving fusion energy on industrial scale requires overcoming several critical engineering and scientific challenges, including development of radiation-resistant plasma-facing component (PFC) materials to withstand fusion conditions. Tungsten (W) has emerged as the leading candidate for divertor and first-wall components in next generation fusion reactors due to its exceptional thermal properties. Nevertheless, under fusion-relevant operational conditions, PFCs undergo significant microstructural degradation, including defect formation, accumulation and transmutation-induced precipitation, all of which impact the mechanical stability and eventually lifetime of the component. A comprehensive understanding of these irradiation-driven mechanisms is essential to design, simulate and optimize durable fusion materials.

Transmission electron microscopy (TEM) is a powerful technique to visualize the microstructure of the inspected material. For nuclear science, it enables to reveal microscopic features, their characteristics and morphologies, induced by irradiation. Furthermore, combining with in-situ heating enables information about high-temperature stability of the radiation-induced defects. Despite extensive research efforts, critical gaps remain in literature regarding the microstructural evolution of PFCs under different irradiation conditions. The response of the material strongly depends on irradiation type (neutron, ion, plasma etc.), dose (dpa), temperature, initial microstructural state and alloy composition, yet systematic comparisons across irradiation conditions are limited. This thesis aims to address some of these gaps by developing a comprehensive dataset describing the defect evolution of PFCs under both neutron and heavy-ion irradiation across a range of irradiation parameters. The work focuses on the experimental investigation of the fundamental microstructural mechanisms such as defect formation, aggregation and recovery as well as understanding radiation-induced hardening and the role of transmutation products.

Jonas Lescroart

• 30/03/2026
  
• 5.30 p.m.
• Venue: Stadscampus, Building R, room S.R.201
• Registration for physical access to public defence:
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• ​
• Supervisors: Hannes Svardal & Eduardo Eizirik
• Department of Biology

Abstract

This PhD project was a coordinated effort to scale up genomic research on the small cat species of the Neotropics (genus Leopardus). With the aid of many, we sourced DNA samples across 10 range countries in the Americas, sometimes resorting to museum specimens, and realized the novel sequencing of 45 complete genomes. For 12 taxa, whole-genome sequencing data simply did not exist at the time this project took off. This data is now accessible to the community in public repositories, hopefully to be reused many times over. ​ ​

Our time-scaled phylogenomic results reveal the sequence and timing of speciation events in genus Leopardus. Current diversity in the genus arose with two pulses of speciation, one in the Early Pliocene and another in the Early Pleistocene. We raise serious doubts regarding the margay-ocelot (L. wiedii/pardalis) sister species relationship and lay bare a deep paraphyly and multiple cryptic lineages within the tiger cat species complex. These results lead us to designate three subgenera, support species status for tiger cat populations in the northern Andes (L. pardinoides) and Caatinga ecoregion (L. emiliae) and suggest the existence of an additional species in the tiger cat complex. Furthermore, preliminary data supports multiple species-level taxa within the pampas cat species complex. ​ ​

We find that phylogenetic signal in Leopardus is highly discordant, that is, genetic similarities between species wax and wane depending on the precise location in the genome under investigation. We use this information to discern traces of introgressive hybridization from the echoes of incomplete lineage sorting. Hybridisation is detected between ocelot and the ancestral lineage of subgenus Oncifelis, between Geoffroy’s cat (L. geoffroyi) and the southern tiger cat (L. guttulus) and between Peruvian and Bolivian populations of tiger cat (L. tigrinus). Moreover, introgressed topologies are significantly associated with locally elevated rates of recombination across the genome, emphasizing the role of local recombination in modulating the effects of interspecific gene flow. ​ ​

Lastly, we present estimates of genomic diversity metrics, noticing that genetic diversity varies highly between species. Recomputing diversity across 39 felid species, we register the highest diversity (0.32%) in ocelot and the second to lowest (0.015%) in Andean cat (L. jacobita). Comparing current effective population sizes against historic sizes highlights sharp population declines especially in guigna (L. guigna) and clouded tiger cat (L. pardinoides), with alarming signs of a fragmented population in the eastern tiger cat (L. emiliae). ​ ​

Kumar Akhil

• 27/03/2026
  
• 2 p.m.
• ​
• Supervisor: Hans Van Haevermaet
• Department of Physics

Abstract

Since the first direct detection of gravitational waves in September 2015, gravitational wave astronomy has transformed from a theoretical pursuit into an observational science. Landmark events such as GW170817, the first binary neutron star merger observed in 2017, opened the era of multi-messenger astronomy and underscored the need for more sensitive detectors. To advance beyond the capabilities of Advanced LIGO and Virgo, third-generation observatories such as the Einstein Telescope (ET) are being designed. Achieving their target sensitivity requires extending the observation band to lower frequencies, which in turn demands unprecedented levels of seismic isolation, pushing residual ground motion at the mirrors to below the 10^{-20}m/sqrt{Hz} level. This is realised through multi-stage suspensions and it is the active control that ultimately stabilises the mirrors at the required level. At the heart of this controls are the precise sensors that measure vibrations, and actuators that apply corrective forces. The reliability of the entire isolation chain therefore depends critically on the design, performance, and integration of these sensing and actuation elements. This thesis focuses on these components in the context of the Einstein Telescope pathfinder (ETpf), a research facility in Maastricht to test the technologies for ET. A universal simulation pipeline was developed to model these sensors and actuators. Building on this modelling, complete production procedures for sensors were established. From design and winding to cleaning for ultra-high vacuum compatibility and final validation, a standard operating protocol was defined. A dedicated test bench was constructed at ��������Ƶ to characterise the transfer functions, noise spectra, and non-linear behaviour of the sensors, enabling direct comparison with model predictions. These studies demonstrate that the sensors achieve nanometre sensitivity at 10Hz. The validated sensors were subsequently integrated into the ETpf suspensions, where they form the backbone of the active control system. Complementary filter strategies were applied to blend the sensors, ensuring broadband control signals. Noise propagation studies confirmed that the intrinsic sensor noise is within the required limits. To push performance further, optimisation studies co-designed sensors and actuators, achieving improvements of more than 250\%. Novel concepts were explored, in particular a so-called reversed LVDT design. Both simulations and experimental measurements confirmed equivalent performance to the conventional design, with the potential advantage of simplified electronics through unified sensing and actuation.

Leonore Vander Donck

• 24/03/2026
  
• 5 p.m.
• Venue: Campus Drie Eiken, O.5
• ​​
• Supervisors: Sarah Lebeer & Veronique Verhoeven
• Department of Bioscience Engineering

Abstract

Women’s intimate health has long been constrained by scientific neglect, fragmented methodologies, and a limited understanding of the vaginal ecosystem. This dissertation addresses these gaps by reframing the vaginal microbiome as a dynamic, interconnected system shaped by both host biology and external factors. The vaginal microbiome, typically dominated by Lactobacillus species in health, plays a crucial role in preventing infections, supporting fertility, and promoting healthy pregnancies. Disturbances to this ecosystem remain an under‑recognized burden in women’s health worldwide. This PhD advances an ecological and integrative perspective on intimate microbial health. First, it demonstrates that the vaginal microbiome cannot be studied in isolation. Using cross‑body site analyses, the work reveals that vaginal lactobacilli disperse across the groin and breast skin in women, challenging traditional assumptions about body‑site specificity. This finding supports the existence of a connected female intimate microbiome shaped by hormones, hygiene practices, sexual activity, and physical proximity. Second, the dissertation shows that the stability of Lactobacillus crispatus‑dominated communities is driven primarily by ecological synergy and metabolic cooperation among microbes, rather than by host factors alone. Synthetic ecology approaches provided a powerful and reproducible platform to dissect these interactions and to evaluate how external influences, such as menstrual products, affect microbial dynamics. Finally, these ecological insights were translated into real‑world context through the Luna intervention study, one of the first to investigate how menstrual products shape vaginal microbiome trajectories. The study found that menstrual cups attenuate menstruation‑associated increases in microbial diversity, whereas external absorbent products are associated with shifts toward taxa linked to inflammation. These results highlight menstrual products as determinants of intimate microbial ecology rather than neutral consumer goods. Building on this, the dissertation proposes foundational steps toward standardized, microbiome‑relevant safety frameworks for menstrual products and intimate hygiene items. Together, this work contributes to a paradigm shift in women’s health research: from viewing the vaginal microbiome as a static niche to understanding it as a dynamic, interconnected ecosystem influenced by daily practices and structural inequities. The findings support more informed menstrual health guidance, the development of microbiome‑based interventions, and regulatory practices aligned with the Sustainable Development Goals for 2030, while helping to destigmatize menstruation and center women’s lived experiences in scientific inquiry.

Malaurie Hons

• 18/03/2026
  
• 4 p.m.
• Venue: Campus Drie Eiken, Q.002
• Supervisor: Jonas Schoelynck
• Department of Biology

Abstract

River restoration is a necessity for reversing biodiversity loss and securing flood safety in a climate with intensifying extremes. However, because ecological outcomes are often unpredictable and heavily influenced by catchment-scale stressors or extreme weather, simply altering a riverbank is rarely a guaranteed fix. Truly understanding the complex, non-linear responses of these ecosystems is the only way to move beyond localized "quick fixes" and build the resilient, self-sustaining river systems required for long-term environmental health. This thesis applied an integrative, process-based approach to evaluate short-term, multimetric ecological effects of reach-scale restoration in the Demer River (Belgium). Measures included meander reconnection, restoration of lateral connectivity, and in-stream structures such as bed sills and deadwood. A defining factor of the study was a severe summer flash flood that occurred shortly after restoration, serving as a critical "stress test" that influenced ecological trajectories and occasionally masked restoration-related signals.

Restoration rapidly altered flow and sediment dynamics, but outcomes were strongly design-dependent. Lateral connectivity proved critical: unfixed banks enabled sediment redistribution and habitat diversification, whereas bank fixation constrained channel development. Rather than resetting historical conditions, restoration initiated new, trajectory-dependent dynamics, also strongly impacted by hydrodynamics. Water quality improvements were modest and mostly transient. Aquatic communities showed no consistent river-wide recovery in multimetric indices, but did show local improvements based on age-class distributions and reproductive guilds, especially in the meander with additional in-stream measures. Fish recovered relatively quickly after flood-induced mortality, while macroinvertebrates and macrophytes exhibited prolonged declines. In contrast, riparian vegetation and carabid beetles responded more consistently, with increased species richness and shifts toward flood-tolerant specialists at sites were lateral connectivity was restored.

The findings demonstrate that restoration outcomes are shaped by design, hydrodynamic variability, and spatial scale, while an appropriate monitoring framework is essential for accurately interpreting ecological responses. Reach-scale interventions can effectively restore local hydro-geomorphic processes and habitat heterogeneity, particularly benefiting riparian biota. However, achieving sustained ecological recovery requires integration with catchment-scale management.

Caroline Dricot

• 12/03/2026
  
• 5 p.m.
• Venue: Campus Drie Eiken, O1
• ​​
• Supervisors: Sarah Lebeer & Irina Spacova
• Department of Bioscience Engineering

Abstract

Lactobacilli are well recognized as key members of healthy vaginal ecosystems, where they prevent pathogen colonization and engage in symbiotic interactions with both commensals and the host. Yet, the precise metabolic and immunological mechanisms underlying their beneficial functions remain incompletely understood. Traditionally, the protective effects of vaginal lactobacilli have been attributed to lactic acid-mediated pathogen exclusion; however, growing evidence suggests that many additional microbial metabolites play critical roles in shaping the vaginal niche. In other mucosal environments, including the gut and respiratory tract, microbial B vitamin metabolism, has emerged as an important mediator of microbiome stability, epithelial physiology, and immune signaling.

Riboflavin (vitamin B2) is of particular interest due to its antioxidative and anti inflammatory properties, its essential function as a metabolic cofactor in central metabolism, and the ability of its biosynthetic intermediates to activate mucosal associated invariant T (MAIT) cells via MR1. Moreover, (hidden) riboflavin deficiency disproportionately affects women due to fluctuating physiological demands linked to urogenital and reproductive health, the menstrual cycle, pregnancy, and breastfeeding. Despite of this, the role of microbial riboflavin metabolism in the vaginal environment had not been systematically explored. ​&�Բ�����;

This PhD thesis elucidates how riboflavin producing vaginal lactobacilli shape the immunometabolic landscape of the vagina, with emphasis on epithelial-immune crosstalk. Population scale multi omics analyses from the Isala citizen science project revealed that vaginal riboflavin levels are strongly associated with Lactobacillus crispatus abundance. Meta omic datasets further showed enriched expression of riboflavin biosynthesis genes in health associated, L. crispatus dominated profiles, with transcripts primarily derived from L. crispatus and L. jensenii. In vitro assays confirmed that riboflavin production is a widespread, ecologically relevant (though typically low level) trait among vaginal Lactobacillaceae. Genetic and metabolic characterization of Limosilactobacillus reuteri isolates identified a single nucleotide polymorphism in the RFN regulatory element that disrupts FMN mediated attenuation of the riboflavin operon, resulting in riboflavin overproduction and transient accumulation of MAIT antigens. Using a physiologically relevant 3D vaginal epithelial model we demonstrated that riboflavin is bioavailable to epithelial cells, basolaterally transported, and capable of modulating epithelial redox metabolism. Moreover, riboflavin producing lactobacilli seemed to affect epithelial MR1-MAIT signaling and trigger secretion of cytokines and tissue repair factors. Finally, the VIAB2L human intervention study-featuring oral and/or colon targeted delivery of riboflavin and riboflavin producing lactobacilli validates some of these in vitro findings.

Collectively, this work establishes microbial riboflavin metabolism as a conserved and functionally significant feature of vaginal lactobacilli, providing mechanistic insight into host-microbe symbiosis and laying the foundation for targeted, metabolism driven microbiome interventions.

Ben Huyge

• 26/02/2026
  
• 5 p.m.
• Venue: Campus Drie Eiken, O.01
• ​​
• Supervisors: Jan Sijbers & Jan De Beenhouwer
• Department of Physics

Abstract

Edge illumination (EI) is an X-ray phase contrast imaging technique with which three complementary image contrasts can be measured: attenuation, phase and dark field contrast. Unfortunately, extracting all three contrasts requires the acquisition of multiple images, causing EI scans to be several times slower compared to conventional X-ray imaging. The main goal of this thesis is to accelerate EI scanning via continuous acquisition, an approach where images are acquired continuously to avoid idle times of the scanning system.

To achieve this, a novel reconstruction technique for continuous acquisition is presented that can model any type of object motion. This technique is not immediately applicable to EI, because it only models object motion, while EI also has a moving mask. Next, continuous acquisition is introduced to EI, focusing on continuous mask motion with a stationary object. It is theoretically proven and experimentally validated that continuous mask motion does not influence the different contrasts. This allows for faster scanning without the need to change the model. Finally, the application potential of EI is demonstrated by extracting fiber orientations from fiber reinforced materials with spherical deconvolution. This allows to extract the orientations without needing to resolve the individual fibers. ​ ​

Miroslav Yosifov

• 23/02/2026
  
• 4 p.m.
• Venue: Campus Drie Eiken, N.008
• ​
• Supervisors: Jan Sijbers, Jan De Beenhouwer & Christoph Heinzl
• Department of Physics

Abstract

X-ray Computed Tomography (XCT) is an increasingly important tool for nondestructive testing, dimensional measurement, and inspection in industrial and scientific applications. Despite its widespread use, significant challenges remain in the reliable quantification of defect detectability and measurement uncertainty, particularly when inspections are complex, costly, or difficult to repeat experimentally. This thesis addresses these challenges by advancing probability of detection (POD) methodologies through simulation-driven approaches, enabling a more rigorous, systematic, and efficient framework for XCT-based inspection. The research is grounded in a detailed examination of X-ray imaging physics, system operation, and radiographic modeling using the X-ray simulation tool SimCT. By analyzing the influence of physical effects such as noise, acquisition geometry, focal spot blur, detector response, and modulation transfer function, the thesis establishes a comprehensive understanding of the factors that constrain image quality, defect detectability, and dimensional accuracy. This foundation is extended by a detailed consideration of POD theory, including POD curves, statistical modeling frameworks, and factors influencing detectability. Together, these methodologies enable an X-ray simulation-based approach to analyze both virtual and real XCT data.

The main contributions of the thesis focus on defect detectability, POD evaluation, measurement uncertainty, and XCT reliability. A simulation-based framework for applying POD to X-ray inspection is developed, demonstrating how virtual experiments can replace or complement costly test specimens. Using X-ray simulation, a controlled environment with artificial defects is generated, enabling systematic analysis of detectability under varied physical influences such as geometry, noise, and contrast. Building on this framework, traditional image segmentation approaches are compared with deep learning-based methods. Results obtained from simulated XCT datasets show that neural networks such as 3D U-Net and V-Net outperform classical algorithms in defect detectability and segmentation accuracy, supporting further automation in nondestructive testing.

Additional contributions include the application of simulation-empowered AI models to agricultural products, where XCT reconstructions of rice grains with controlled defects are analyzed through shape variation analysis to detect early degradation. Furthermore, a method for superimposing synthetic defects into measured XCT volumes is introduced, strengthening the link between simulated and real XCT data and enabling segmentation-based POD evaluation in a realistic yet controlled environment. Finally, the thesis investigates the influence of individual physical effects on dimensional measurement deviations, identifying dominant sources of dimensional error in XCT metrology.

Overall, the thesis demonstrates that X-ray simulations, deep learning, uncertainty analysis, and statistical methods can be integrated to advance POD methodologies for defect detection across multiple domains. ​ ​

Sarah Hautekiet

• 19/02/2026
  
• 4 p.m.
• Venue: Campus Drie Eiken, O.05
• ​​
• Supervisors: Stijn Temmerman & Maarten Kleinhans
• Department of Biology

Abstract

Tidal marshes are highly dynamic coastal ecosystems that provide essential ecosystem services such as shoreline protection and carbon sequestration. Hence, they belong among the most valuable ecosystems on our planet. However, centuries of anthropogenic disturbance, combined with ongoing climate change pressures, have led to widespread degradation of tidal marshes. This has created a growing interest for tidal marsh restoration. The aim of such practices is to create an intertidal ecosystem that displays equivalent characteristics and ecosystem functions as natural reference systems. However, restored tidal marshes often differ from their natural counterparts in this regard. As such, a thorough understanding of the mechanisms driving tidal marsh development is needed to improve outcomes in future restoration projects. The aim of this thesis was to advance our understanding of the abiotic and biotic mechanisms affecting tidal marsh development. Specifically, we focused on the development of two key features of tidal marshes (i.e. tidal creek networks and pioneer marsh vegetation) by investigating (1) the relative roles of abiotic and biotic processes in tidal creek network development, and (2) the mechanisms restricting or promoting pioneer vegetation establishment. In order to achieve this, we conducted scaled laboratory experiments in the Metronome tidal flume facility, a mesocosm experiment in the lab, field monitoring, field experiments and a field mesocosm experiment, and geospatial analyses of marsh development in restoration sites along the Scheldt estuary. Results demonstrate that while vegetation can influence tidal creek network density through bio-geomorphic feedbacks, abiotic drivers are the dominant controls on creek incision. Vegetation primarily modifies creek networks during the transition from bare mudflat to vegetated marsh, and the rate of plant colonization exerts a stronger influence on network development than spatial colonization patterns. Vegetation establishment on bare tidal mudflats was found to be strongly constrained by low intertidal elevation, rapid sediment accretion, and poor sediment drainage - conditions typical of recently restored sites. Well-drained areas, such as tidal creek edges, promote erosion-resistance, creating favorable conditions for seed retention and seedling survival. In addition, three-way interactions between filamentous algae, macrobenthos, and pioneer plants were shown to play a crucial role in plant establishment. Together, these findings highlight that tidal marsh development emerges from tightly coupled abiotic and biotic processes. Improving drainage, enhancing topographic diversity, and leveraging facilitative biotic interactions offer promising pathways to overcome key bottlenecks in marsh restoration and accelerate the transition from bare mudflat to resilient vegetated marsh.

Wouter Van Hoey

• 09/02/2026
  
• 5 p.m.
• Venue: Campus Middelheim, G.010
• ​
• Supervisor: Pegie Cool
• Department of Chemistry

Abstract

At present, the majority of all intricate global challenges are related to sustainability. Therefore, the urge for sustainable development is bigger than ever. To date, the chemical sector is already a major driving force for innovation and essential for sustainable growth across all sectors. However, much more scientific contributions will be needed to tackle the difficult task ahead, which is to create a sustainable world whereby the needs of the present are met without compromising the ability of future generations to meet their own needs. Hence, the aim of this thesis is to make a positive contribution to sustainability by applying heterogeneous catalysis in various green chemistry related applications. The research in this PhD thesis is mainly focussed on the development of innovative CeO2-based catalysts and can be divided into four different topics: catalytic combustion of VOCs, plasma-catalytic conversion of CO2, photo-induced reduction of nitrobenzene to aniline, and the selective hydrodeoxygenation of aromatic carbonates. In more detail, chapters 2, 3 and 4 focus on catalyst optimisation to improve the catalytic combustion of toluene, which is a model compound for aromatic VOCs. Based on the obtained results it can be stated that combining noble and transition metals offers a unique approach to limit the costs and consumption of noble metals for industrial large-scale combustion of VOCs. Unfortunately, while intriguing results are obtained regarding the catalytic combustion of VOCs, it cannot be ignored that combustion of carbonaceous compounds results in the production of CO2. Therefore, the fifth chapter tackles the plasma-catalytic conversion of CO2 using CeO2. More specifically, it is investigated if increasing the amount of oxygen vacancies at the surface of CeO2 enhances the dissociation of the stable CO2 molecule, which facilitates the conversion of CO2 into value-added products. Next, in chapter 6 the development of a more sustainable photochemical process to convert nitroarenes into anilines at room temperature and in open atmosphere without the addition of hydrogen is discussed. Additionally, it is examined if the presence of a heterogeneous catalyst can further improve the obtained results. Finally, in chapter 7 a thorough characterisation is provided to gain a better understanding on the selective hydrodeoxygenation of aromatic carbonates when commercially available heterogeneous Ni-SiO2 catalysts are applied. Unravelling the reason behind the remarkable activity is trivial as the successful hydrodeoxygenation provides a next step in the development of important bio-based compounds from wood.

Burak Karaduman

• 05/02/2026
  
• 10 a.m.
• Venue: Campus Middelheim, G.006
• Supervisor: Moharram Challenger
• Department of Computer Science

Abstract

As embedded system-originated paradigms grow in complexity and lack of autonomy, traditional control architectures struggle to manage the uncertainty and dynamism of real-world environments. Intelligent agent architectures, particularly those grounded in the Belief–Desire–Intention (BDI) model, offer promising cognitive capabilities for Cyber–Physical System (CPS). However, their integration into resource-constrained and (soft) real-time embedded platforms remains challenging. To address this, eventually, we propose a Model-driven Engineering (MDE) approach for deploying fuzzy-BDI agents into CPS. This approach combines fuzzy logic with BDI reasoning to manage imprecision and context shifts, while also enabling platform-independent system design through model-based abstraction. Despite the growing relevance of such hybrid architectures, existing literature lacks comprehensive methods that link cognitive agent behaviours to deployable embedded code in complex systems settings. This thesis fills this gap by introducing an integrated approach that bridges high-level agent modelling and low-level system implementation. The results demonstrate that fuzzy-BDI agents, supported by model-based analysis and engineering, can enhance adaptability, reduce development complexity, and improve robustness in uncertain environments. The central challenge addressed in this thesis lies in bridging the gap between traditional embedded control and intelligent decision-making within CPS. While intelligent agent architectures, particularly those grounded in the BDI model, provide a promising cognitive framework for autonomous reasoning, their practical integration with enhanced capabilities into resourceconstrained embedded platforms remains an unresolved problem. The BDI model enables agents to reason about their environment, goals, and actions proactively, maintaining beliefs about the world, forming desires representing objectives, and committing to intentions that guide action. However, classical BDI systems are based on crisp logic, which severely limits their applicability under uncertainty and continuous change. Without mechanisms to interpret ambiguous sensory data or adapt to fluctuating contexts, BDI agents fall short of the robustness required in CPS deployments. To address these shortcomings, this thesis introduces an integrated fuzzy-BDI and MDE framework for the development and deployment of intelligent CPS. The core idea is to combine fuzzy logic, which allows reasoning with degrees of truth, with the structured deliberation of BDI agents, thereby enabling systems to handle uncertainty natively at every stage from perception and planning to action execution. Complementing this reasoning innovation, the research employs MDE to elevate the abstraction level of system development. The proposed framework bridges high-level agent design models with low-level embedded implementations, achieving explainability, reusability, and platform-independent deployment. This dual integration of fuzzy and BDI reasoning with model-driven design forms the foundation of a new engineering methodology for building adaptive and smart CPS.

Arno Raes

• 02/02/2026
  
• 4 p.m.
• Venue: Stadscampus, Klooster van de Grauwzusters, Promotiezaal
• Supervisor: Sammy Verbruggen
• Department of Bioscience Engineering

Abstract

Titanium dioxide is widely used in photocatalysis because it is abundant, stable, and inexpensive, yet pristine anatase and rutile absorb mainly ultraviolet light. One strategy to extend activity into the visible region is the introduction of sub-gap states associated with oxygen vacancies and Ti³⁺ species. These are typically generated through hydrogenation, high-temperature reduction, plasma treatment, or strong chemical reductants, routes that often raise energy concerns. This thesis therefore addresses a practical question: can TiO2 be reduced by mild and potentially scalable methods that still deliver useful functionality under realistic conditions, and where do such approaches succeed or fail?

High-intensity ultrasound was first investigated as a potential reduction route. Using calorimetrically calibrated power delivery and systematic probe integrity checks, any apparent darkening of TiO2 during aqueous sonication was traced to metallic debris from horn erosion rather than defect formation. UV–Vis diffuse reflectance showed no band-edge shift or sub-gap absorption, and no evidence for stable oxygen vacancies or Ti3+ species was found. Under these conditions, cavitation hot spots do not provide a sustained reducing environment, and any transient defects are rapidly re-oxidised by radical species and dissolved oxygen. Ultrasound-induced “blackening” is therefore an artefact rather than a viable reduction strategy.

Despite this negative outcome, ultrasound proved valuable as a synthesis and processing tool. During sol–gel synthesis it yielded high-surface-area, open-mesoporous amorphous TiO2 with interconnected nanoparticulate networks. Controlled ultrasonic crystallisation produced predominantly anatase while retaining about 50% of the surface area, compared to the 10% of conventional calcination. These materials showed rapid uptake of volatile organic compounds, consistent with their preserved mesostructure.

Vacuum annealing was then explored as a hydrogen-free route to defective TiO2. Mild vacuum treatment of P25 darkened the powder, increased visible-light absorption without shifting the band edge, and shifted the Ti:O ratio away from perfect stoichiometry. Functionally, this resulted in an approximately 25% increase in methane formation during CO2 photoreduction compared with pristine P25. Applying the same treatment to Au@P25 preserved plasmonic properties but provided only marginal additional activity beyond that already conferred by gold.

Overall, this work shows that mild routes give mild but reliable outcomes. They are effective for preserving structure, adsorption capacity, and stability, and can deliver meaningful functional gains in selected cases.

Arthur Gestels

• 02/02/2026
  
• 2 p.m.
• Venue: Campus Middelheim, A.143
• ​
• Supervisors: Koen Janssens & Gunther Steenackers
• Department of Physics

Abstract

Scientific research in cultural heritage has increasingly shifted toward non-invasive imaging methods that can reveal material information across entire artworks. Because paintings often exhibit complex stratigraphies and heterogeneous compositions, traditional point-based techniques and small-scale sampling frequently fail to provide representative insight into the full material structure of an artwork. In this context, hyperspectral imaging techniques have become essential tools for conservators, researchers, and art historians.

Macroscopic X-ray powder diffraction (MAXRPD) is one such hyperspectral technique. It enables direct identification of crystalline compounds, making it highly valuable for the study of pigments and degradation products in painted artworks. Despite its high chemical specificity, MAXRPD is limited by low spatial resolution and long acquisition times, restricting its applicability for large-scale investigations. To address these limitations, this thesis investigates the combination of MAXRPD with faster hyperspectral imaging modalities, including reflectance imaging spectroscopy (RIS) and macroscopic X-ray fluorescence (MAXRF), using machine learning approaches to reduce acquisition time and improve efficiency. ​ ​

The research begins with a case study on Rembrandt’s The Night Watch, where MAXRPD was used to assess the material impact of an acid vandalism attack. The results revealed the formation of anglesite as a crystalline degradation product in the upper paint layers, demonstrating the value of compound-specific imaging for conservation assessment and treatment planning.

Next, a proof-of-concept study on corroded metal plates showed that quantitative MAXRPD data could be combined with RIS data to train machine learning models capable of predicting material distributions with reasonable accuracy. This demonstrated the feasibility of extrapolating material-specific information from faster imaging techniques.

The methodology was then adapted to cultural heritage objects, enabling the generation of high-resolution, material-specific images of artworks. Case studies on an illuminated manuscript and oil paintings showed that fusing MAXRPD with RIS or MAXRF data produces reliable predictions. Finally, the approach was applied to more complex historical paintings to assess its generalizability, demonstrating that accurate compound mapping is possible even in unscanned areas when representative training data are available. Together, these results establish a scalable framework for accelerating MA-XRPD-based imaging while preserving its analytical power.

Donja Baetens

• 30/01/2026
  
• 2 p.m.
• Venue: Campus Drie Eiken, O.5
• Supervisor: Siegfried Denys
• Department of Bioscience Engineering

Abstract

Indoor air quality (IAQ) is increasingly recognised as a major public health concern, as people spend most of their time indoors and are therefore continuously exposed to pollutants present in indoor environments. Among indoor contaminants, particulate matter (PM) and volatile organic compounds (VOCs) are of particular concern due to their health impacts and prevalence in buildings. While air purification technologies can reduce exposure, most systems target only one type of pollutant, i.e. particulate or gaseous. This dissertation explores the integration of electrostatic precipitation (ESP) and photocatalytic oxidation (PCO) into a single device to enable simultaneous removal of PM and VOCs. It is hypothesised that incorporating PCO into the collector section of a two-stage ESP reactor enables simultaneous removal and may reduce the need for cleaning the ESP by degrading deposited particles.

A combined ESP-PCO reactor was developed with a photocatalytic coating immobilised on the collector plates and UV lamps positioned between them, alongside multiphysics models describing the underlying processes. First, the standalone ESP functionality was investigated to determine total and fractional particle collection efficiencies, the influence of operating conditions, and ozone emission. Next, the photocatalytic coating was examined independently for photocatalytic VOC (acetaldehyde) removal, comparing metallic substrates and studying the effects of coating layers, inlet concentration, and relative humidity. Photocatalytic soot degradation experiments were also performed to evaluate removal of deposited PM.

Subsequently, acetaldehyde removal was studied in the full two-stage ESP-PCO reactor to assess PCO and ESP functionality in terms of clean air delivery rate (CADR) and single-pass removal efficiency (SPRE). Activation of the ionisation section resulted in acetaldehyde removal comparable to photocatalytic removal, while combining ESP and PCO yielded the highest removal observed, although synergistic effects were not clearly identified.

Finally, multiphysics models for ESP and PCO were developed to describe air flow, electric field, ion transport, particle charging and motion, acetaldehyde transport, radiation distribution, and photocatalytic kinetics. The models were verified against experiments and provide insight into the influence of plate spacing, irradiance distribution, and electric field effects while identifying opportunities for performance optimisation.

Overall, this dissertation demonstrates the feasibility of integrating photocatalytic oxidation into a two-stage electrostatic precipitator for indoor air purification, showing that the combined system can remove both PM and VOCs and has the potential to reduce particle accumulation. However, the results highlight design, material, and modelling challenges requiring further development.

Jan Morez

• 29/01/2026
  
• 4 p.m.
• Venue: Campus Drie Eiken, O.005
• ​​
• Supervisors: Jan Sijbers & Ben Jeurissen
• Department of Physics

Abstract

Diffusion magnetic resonance imaging (dMRI) offers a unique way of imaging the human brain noninvasively. By carefully controlling various acquisition parameters, the dMRI signal can be used to probe the movement of water molecules inside biological tissues, revealing important information about tissue structure. However, this signal is subject to noise and other undesired electromagnetic effects that reduce the signal-to-noise ratio. Efficient acquisition schemes and robust estimators are required to obtain accurate and precise tissue maps of the human brain. In this thesis, we have aimed our efforts towards improving the accuracy, precision, generalizability and applicability of several dMRI analysis methods. ​ ​

Constrained spherical deconvolution (CSD) is a popular dMRI technique that can be used to infer the local tissue densities and their orientations in the human brain, which consists of cerebrospinal fluid, anisotropic white matter and isotropic gray matter. This is achieved by densely sampling q-space, the space of q-vectors representing the direction and strength of diffusion weighting. After sampling q-space in multiple shells, tissue densities and orientations can be estimated by deconvolving this multi-shell dMRI signal with the respective response functions for white matter, gray matter and cerebrospinal fluid. These response functions are typically represented using spherical harmonics (SH) basis functions. However, when sampling is nonspherical, either due to inhomogeneous gradients or by design such as Cartesian sampling, the estimated tissue maps suffer from biases. To counter this issue, we adopted a compact response function model that accounts for nonspherical sampling. On multi-shell data, our approach provides fiber orientation density functions and tissue densities indistinguishable from those estimated using SH. On Cartesian data, estimates are on par with those obtained from shell-wise data, significantly broadening the range of data sets analyzable using CSD. In addition, inhomogeneous gradients can be accounted for, resulting in more accurate apparent tissue densities and connectivity metrics. ​ ​

Q-space trajectory imaging is a dMRI technique that uses time-varying q-vectors to sensitize the dMRI signal to microscopic variations in heterogeneous tissues. By modelling the dMRI signal with a diffusion tensor distribution (DTD), this approach allows teasing apart variations in diffusivity from microscopic anisotropy, orientation dispersion, and mixtures of multiple isotropic diffusivities. To improve the estimation of the DTD parameters, we propose an efficient acquisition scheme optimized for the most used QTI-derived microstructural parameters. A constrained iteratively reweighted least squares estimator is used to further improve the bias and precision of the DTD parameters.

Jonathan Sanctorum

• 13/01/2026
  
• 4 p.m.
• Venue: Campus Drie Eiken, Q.001
• ​
• Supervisors: Jan Sijbers & Jan De Beenhouwer
• Department of Physics

Abstract

X-ray phase contrast imaging is known for its potential to yield high contrast in soft tissue and light materials compared to conventional transmission contrast, making it a valuable tool for (bio)medical applications and non-destructive testing. In addition to transmission and phase contrast, dark field contrast is a third contrast type that has received increased interest recently due to its unique ability to detect the presence of unresolved microstructures. Phase and dark field contrast are both related to the material’s X-ray refractive index, but as opposed to phase contrast, dark field contrast results from refractive index fluctuations that cannot be resolved by the imaging system. Remarkably, all three contrast types can be measured in a single experiment using dedicated X-ray phase contrast imaging systems. The introduction of compact lab-based systems for phase sensitive X-ray imaging has resulted in a growing number of applications. Optimization of conventional X-ray imaging setups often relies on Monte Carlo simulations, where stochastic models are used to simulate the physics processes. In this work, a collection of tools is presented that aim to meet the requirements associated with Monte Carlo simulations for the design of X-ray phase contrast imaging systems. Here, two X-ray phase contrast imaging methods are mainly of interest: grating-based interferometry and edge illumination, each of which relies on the use of gratings. First, the realization of X-ray phase contrast simulations for grating-based interferometry is demonstrated using the GATE Monte Carlo framework, hereby relying on a hybrid simulation approach combining Monte Carlo simulations with wave optics calculations. Although this simulation framework is suitable for edge illumination simulations as well, Monte Carlo simulations are known to be very time consuming, certainly for parameter studies. To address this limitation, the concept of virtual gratings is introduced. By replacing the gratings in the simulation with virtual gratings, the parameters of the gratings can be changed after the simulation, thereby significantly reducing the overall simulation time. This concept is subsequently used for the design of edge illumination gratings for the augmentation of the FleXCT micro-CT scanner to a phase sensitive X-ray imaging system. Finally, the benchmarking of multi-contrast X-ray imaging simulations is addressed. The reference values required for benchmarking are particularly difficult to determine for the dark field contrast. Here, a practical method based on the virtual grating approach is presented to directly estimate reference values for all three contrast types from the simulated X-ray trajectories, allowing for efficient benchmarking.

Salma Haidar

• 13/01/2026
  
• 4 p.m.
• Venue: Campus Middelheim, A.143
• ​
• Supervisor: José Oramas Mogrovejo
• Department of Computer Science

Abstract

Hyperspectral imaging (HSI) combines digital imaging with spectroscopy, capturing hundreds of contiguous wavelength bands per pixel. This produces a three-dimensional “hypercube” that integrates spatial and spectral information, offering powerful opportunities for material analysis, environmental monitoring, and beyond. Yet, HSI also poses challenges: high dimensionality, limited annotated data, and heavy computational demands.

My thesis addresses these challenges through three complementary approaches. First, I develop a deep learning framework for multi-label classification that demonstrates strong performance in complex, mixed-material scenes and highlights limitations in common annotation practices. Second, I explore self-supervised contrastive learning to enhance classification accuracy under limited supervision, demonstrating substantial gains across diverse datasets. Third, I apply explainability-driven dimensionality reduction to identify the most informative spectral bands, reducing redundancy while maintaining or even improving accuracy.

Together, these contributions demonstrate that tailored representation learning strategies and explainability-driven dimensionality reduction can deliver hyperspectral classifiers that are accurate, computationally efficient, and adaptable to challenging data conditions. The results highlight practical pathways for improving hyperspectral image analysis and open opportunities for further exploration across diverse application domains.