Jan 

• 21/02/2025
  
• 10.00 uur
• Locatie: Campus Drie Eiken, R1
• ​Online Doctoraatsverdediging​
• Promotoren: Pegie Cool & Shoubhik Das
• Departement Chemie

Abstract

With the rapid development of the economy and society, the continuous increase in energy demands and CO2 emissions are becoming critical to global warming and posing severe challenges to human life and social development. Therefore, it is urgent to close the anthropogenic carbon cycle through a chemical conversion of CO2 into value-added products and fuels by renewable energy, which can not only alleviate ever-increasing environmental pollution but also store intermittent energies into chemical bonds. Of all technologies to convert CO2 into value-added products, the electrocatalytic route appears as one of the most sustainable techniques as it can be conducted under mild conditions such as ambient pressures and temperatures, in neutral pH, and powered by renewable electricity from wind, solar, or hydro powerplants. However, CO2 is chemically stable, exhibiting a significant energy gap between its highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO). Therefore, a high energy for the dissociation of the C=O bond (806 kJ mol−1) is required. Moreover, the competing hydrogen evolution reaction (HER) occurs alongside CO2 reduction on transition metal catalyst is detrimental to the product selectivity and reaction kinetics. How can we mitigate those challenges?

According to the Sabatier principle, the reaction rate is determined by the interaction strength with the intermediates that are produced during the CO2 reduction process. The interaction strength depends on the electronic structure of 3d orbitals of transition metal catalysts. Therefore, high product selectivity and fast reaction kinetics are achieved by introducing defects and alloying, which can optimize the interaction strength with the intermediates. Moreover, the underlying mechanisms of the interaction between active center and heteroatom atom, N doping, and alloying in facilitating CO2 reduction were explored, which provides more in-depth insight for designing catalysts and improving product selectivity.

Mattias Billast

• 20/02/2025
  
• 16.30 uur
• Locatie: Campus Middelheim, G.010
• Promotoren: Steven Latré & Kevin Mets
• Departement Informatica

Abstract

This dissertation explores the challenges and solutions of using domain adaptation in real-time computer vision applications with limited labeled data. Computer vision, initially based on traditional feature extraction methods, has progressed significantly with deep learning, achieving breakthroughs in areas like image classification and object detection. However, deep learning models often require large labeled datasets, which can be expensive and time-consuming to obtain, especially for custom applications. Domain adaptation offers a way to tackle this problem by using external data sources to improve model performance in a target domain. Its effectiveness depends on the domain gap—if the gap between source and target data is too large, adaptation becomes difficult. 

The dissertation focuses on two applications: maritime autonomous navigation and human motion prediction. In maritime navigation, the goal is to detect, track, and locate obstacles for autonomous vessels, but a lack of labeled data poses a challenge. By using domain adaptation techniques, data from external sources (such as public object detection datasets) is leveraged to improve model accuracy. The second application involves predicting the physical and cognitive ergonomics of operators performing repetitive tasks. This is done by analyzing human pose data and anticipating movements to prevent musculoskeletal issues. Data from a VR setup helps train the model, with domain adaptation used to improve its performance despite limited labeled data. 

Both applications require real-time performance with lightweight models. Domain adaptation techniques are used to enhance the models by incorporating external data, like maritime object detection datasets or VR controller data for human pose prediction. 

Overall, the thesis highlights the importance of domain adaptation in improving model accuracy with limited data, showing that external data sources can significantly enhance real-time computer vision applications, both in real-world and academic settings. The key contribution is that domain adaptation can utilize any useful external data to improve performance.

Yunmeng Li

• 13/02/2025
  
• 09.30 uur
• Locatie: Auditorium Asterias, InnovOcean Campus, Jacobsenstraat 1, 8400 Oostende
• ​Online Doctoraatsverdediging
• Promotoren: Sarah Lebeer, Jana Asselman & Maarten De Rijcke
• Departement Bio-ingenieurswetenschappen

Abstract

Epidemiological studies have shown that living near the ocean is associated with better self-reported health, though the underlying mechanisms remain unclear. This PhD research explores a less examined aspect: whether inhalation of sea spray aerosols (SSAs) confers potential immunological benefits, focusing on the effects of marine bacteria and endotoxins in SSAs on several immune receptors and transcription factors. To investigate this, we conducted studies focusing on two main areas: 1) analyzing bacterial communities in surf zone seawater and determining which bacteria are transferred to coastal air via SSAs, and 2) evaluating the immunological effects of bacteria and endotoxins in SSAs using human reporter cell models. These studies were carried out at the Marine Station Ostend (MSO) and a nearby recreational beach (within 1 km of MSO) in Ostend, Belgium. Our results demonstrated that bacterial communities in the surf zone seawater of the recreational beach under normal conditions (i.e., no distinct pollution and disease outbreaks) varied seasonally and were influenced by environmental factors such as chlorophyll a, net primary production, and seawater temperature. When wind speeds exceeded 4 m/s (indicating SSA production) and winds blew from the sea, aerosol samples collected on the rooftop of MSO contained more bacteria from local seawater. The composition of these bacterial communities also varied seasonally (spring and summer), influenced by temperature. Single, short-term exposure experiments using SSA-dominated samples revealed that total bacteria counts and endotoxins mildly activated Toll-like receptor 4 (TLR4), TLR2/6, and the transcription factors nuclear factor kappa B (NF-κB) and interferon regulatory factor (IRF) in a dose-dependent manner. Pre-exposure to these SSA samples modulated the activation of TLR4, NF-κB, and IRF by subsequent exposure to pro-inflammatory Escherichia coli lipopolysaccharide (LPS) in a dose-dependent manner for endotoxin concentrations and total bacterial counts. Specifically, low levels of endotoxin inhibited TLR4 activation following E. coli LPS exposure, while low levels of total bacterial count showed neutral effects on NF-κB activation and inhibited IRF activation. These findings deepen our understanding of bacteria in coastal air and suggest that inhalation of SSAs may modulate immune pathways, potentially offering immunological benefits. Further research is needed to evaluate SSA health effects across different particle properties, exposure modes, and immune responses, using primary human immune cells and cohorts to better understand their immunological impact and the mechanisms underlying SSA interactions with the human immune system.

Yuxiang Cai

• 07/02/2025
  
• 13.00 uur
• Locatie: Campus Drie Eiken, O1
• ​Online Doctoraatsverdediging
• Promotoren: Annemie Bogaerts & Xin Tu (University of Liverpool)
• Departement Chemie

Abstract

Controlling carbon dioxide (CO₂) emissions and effectively utilizing it through chemical processes is a challenging issue that chemists and environmental scientists urgently need to address. Although many methods have been proposed to tackle the CO₂ problem, there is still no particularly effective chemical utilization method for this vast carbon source. This is because carbon dioxide is very stable and requires high temperatures for thermal activation.

Therefore, actively seeking new methods or supplementing with other approaches represents a new direction in carbon dioxide conversion research. Plasma technology, with its powerful activation capabilities, offers a new technological route for CO₂ conversion. Using plasma technology to convert CO₂​ into fuels and chemicals has significant application prospects. However, although the technical route of using CO₂​ and hydrogen or methane to produce syngas is feasible, the energy efficiency is still low, making industrial application difficult. The use of plasma catalysis for CO₂ hydrogenation into methanol represents a novel technological route. However, the mechanism remains unclear, resulting in a lack of systematic guidance for the design of the process.

To fully utilize the CO₂​ and convert it into value-added chemicals, while also making full use of the hydrogen and carbon resources of the co-reactant, this thesis combines experiments and simulations at different spatiotemporal scales, and conducted research in:

(1)  Understanding the mechanism of plasma-catalytic reverse water-gas shift (RWGS) reactions is important for a deeper insight into plasma-catalytic CO₂ conversion. In this thesis, perovskite catalysts with various B-site elements were successfully synthesised and evaluated in plasma-catalytic RWGS reactions. Among the tested samples, Fe-based perovskite catalyst demonstrated the best  performance, achieving 22.7% CO₂ conversion and 94.3% CO selectivity. Further improvements were observed with partial substitution of the B-site on Fe-based perovskite catalyst. The optimal catalyst, La_0.5Sr_0.5Fe_0.9Cu_0.1O₃, yielded 25.9% CO₂ conversion and 94.3% CO selectivity. The perovskite catalyst enhanced the plasma discharge characteristics, facilitating CO₂ excitation and C=O bond activation. A 0D kinetics simulation indicated CO production mainly from CO₂ dissociation. Catalyst characterization revealed that Cu substitution increased the catalyst's surface area, redox capability, and oxygen vacancies, enhancing CO₂ and H₂ adsorption and decomposition.

(2)   Targeting value-added chemicals from plasma-catalytic CO₂ conversion, a comparative study focused on catalyst supports was conducted. The Si/Al ratio of ZSM-5 significantly altered the properties of the Cu/ZSM-5 catalyst, particularly its acidity and basicity. Among all the samples, Cu/ZSM-5 with a Si/Al ratio of 38 showed the largest strong basic site percentage, which enhanced the electron-donating ability of the catalyst, promoting CO₂ adsorption. This facilitated the dissociation and activation of CO₂ molecules on the active Cu sites, further improving the catalyst activity. By combining characterisation and in-situ diagnostics, the mechanism is revealed.

(3)  For predicting the comprehensive reaction networks of plasma-catalytic CO₂ hydrogenation on Cu, the performance of a meta-generalized gradient approximation (mGGA) level density functional, rMS-RPBEl-rVV10, was evaluated and utilized. The rMS-RPBEl-rVV10 density functional closely predicted metal description, thermal dynamics, and the adsorption process without empirical corrections and excelled in predicting dissociation barriers critical for reaction networks. Also the reaction pathways on Cu(111) and Cu(211) surfaces were studied. On Cu(111), the formate and CO₂ dissociation pathways were equally favourable, with identical highest barriers, while the carboxyl path had a higher barrier. On Cu(211), the CO₂ dissociation pathway was most favourable with the lowest rate-controlling barrier. Generally, intermediates were more stable and reaction barriers lower on Cu(211). The Eley-Rideal (E-R) mechanism is discussed, the participation of plasma species significantly reduces or even eliminates energy barriers, while also providing key intermediates for fundamental reactions, leading to high selectivity and yield of CH₃OH at low temperatures and atmospheric pressures discussed in Chapter 4. This study provided valuable insights for the understanding of a comprehensive plasma-catalytic CO₂ hydrogenation mechanism.

(4)  Developing a hybrid machine learning model using limited experimental data to predict and analyse plasma-catalytic dry reforming of methane (DRM). Combining artificial neural network (ANN), support vector regression (SVR), and regression tree (RT) with genetic algorithm (GA) for optimization, the model was trained on 100 data points with four reaction parameters and four performance indicators. It achieved high predictive accuracy. The model revealed significant interactions between discharge power and total flow rate, with optimal conditions identified for maximum energy yield (0.398 mmol/kJ) and fuel production efficiency (13.2%). Despite not providing mechanistic insights, the model provided an efficient way for predicting and optimizing plasma-catalytic DRM, and also shows potential of the application in other plasma catalysis system.

Sebastiaan Valkiers

• 06/02/2025
  
• 16.00 uur
• Locatie: Campus Middelheim, A.143
• ​Online Doctoraatsverdediging​
• Promotoren: Kris Laukens, Pieter Meysman & Nathalie Cools
• Departement Informatica

Abstract

The adaptive immune system's ability to recognize an immense array of antigens is enabled by the vast diversity of T cell receptors (TCRs) produced through the V(D)J recombination process. Analysis of an individual’s TCR repertoire is often limited to the most abundant T cell clones, based on the idea that clonal abundance serves as a metric for antigen-specific proliferation. While useful, this only takes into account a small fraction of the T cell response. This is particularly problematic in autoimmune disease, where autoreactive T cells that recognize self-antigens may exist at low frequencies. In contrast, this thesis explores the hypothesis that TCR sequence similarity can serve as a reliable proxy for antigen specificity, providing a refined framework to dissect TCR repertoires into its functional components To this end, we developed computational methods — ClusTCR and clustcrdist — designed to cluster TCR sequences based on their sequence similarity and to detect neighbor enrichment, identifying TCR clusters with higher-than-expected sequence convergence compared to background models accounting for V(D)J recombination statistics and repertoire properties. These tools offer scalable and flexible approaches to uncover antigen-driven patterns within TCR repertoires and highlight a dual model of antigen-specific T cell responses, involving both strong monoclonal expansion and polyclonal convergence through sequence similarity. We applied these principles on a curated set of public TCR repertoire from multiple sclerosis (MS) patients (n=129) and controls (n=94) to better understand the T cell responses underlying breakdown of the myelin sheath in this disease, which revealed primarily CMV-specific clusters negatively associated with MS that were restored after autologous hematopoietic stem cell transplantation. Conversely, enrichment of Epstein-Barr virus (EBV)-specific CD8+ T cells was observed in MS patients, aligning with hypotheses of viral cross-reactivity contributing to autoimmunity. In addition, myelin-stimulation experiments aimed at identifying myelin-reactive TCRs in MS patients, revealed notable heterogeneity and a lack of sequence convergence, contrasting with patterns observed in viral responses. This suggests that mechanisms driving T cell reactivity against autoantigens may fundamentally differ from those against viral epitopes.

Rani Vertongen

• 23/01/2025
  
• 17.00 uur
• Locatie: Campus Drie Eiken, Q0.02
• ​Online Doctoraatsverdediging
• Promotor: Annemie Bogaerts
• Departement Chemie

Abstract

CO2 is one of the main contributors to global warming. The best strategies to mitigate climate change are to electrify and decarbonize industry, but this cannot be achieved overnight. In the meantime, we need new technologies to deal with CO2: not only cut our carbon emissions, but also to lower the high levels of CO2 currently in the atmosphere. Carbon capture and utilization technologies are especially interesting, since they can produce value-added chemicals and fuels as new raw materials in industry to reduce our dependence on fossil sources and prevent more CO2 from entering the atmosphere. Plasma technology is especially promising thanks to its flexible and electric operation, coupling well with renewable energy sources, and its use of cheap and abundant materials in the reactor. However, the potential of plasma technology for CO2 conversion is not fully realized yet. Often, the conversion is limited, or high conversions can only be achieved at low energy efficiencies. How can we improve CO2 conversion in plasma technology? By investigating both reactor and process design, this thesis presents some encouraging answers to this question.

These experiments teach us some general insights on how to improve the conversion of CO2 in a plasma reactor. Good plasma stability can be achieved through proper reactor design, which will result in a higher energy input, yielding a higher conversion. Equally important is the design of the post-plasma zone, where effective quenching can help to improve the conversion. Furthermore, smart process design can modify the energy input by putting reactors in series and tune this technology for specific applications by adding hydrogen carriers or sorbent materials.

Overall, the reactor and process design in this thesis resulted in a higher CO2 conversion. The insights in the underlying mechanisms shine a light on future research paths, so that we can further develop plasma technology and contribute to a sustainable future. ​ ​

Leonid Kholkine

• 17/01/2025
  
• 15.00 uur
• Locatie: Stadscampus, Hof Van Liere, F. De Tassiszaal
• Promotoren: Tim Verdonck & Steven Latré
• Departement Informatica

Abstract

While much of Machine Learning research relies on controlled "toy datasets," real-world data and applications are significantly more unpredictable, introducing unique challenges in variability, scalability, and practical implementation. This thesis examines the practical applications of Machine Learning in real-life scenarios, focusing on personalized recipe recommendations and sports analytics. By addressing these complexities, it bridges the gap between theoretical advancements and practical deployment, providing insights and solutions for real-world use cases.

In the retail domain, a graph-based recommendation system models relationships between users, recipes, and ingredients to deliver personalized, actionable suggestions. The system addresses issues such as popularity bias and the cold-start problem, achieving a 40% improvement in accuracy over traditional methods. The framework also enhances diversity, reduces bias, and provides explainability by identifying key features driving recommendations, offering a practical solution for improving user engagement and sales.

In sports, Machine Learning techniques predict cycling race outcomes using a learn-to-rank approach. This novel application explains predictions using SHAP values, highlighting race-specific factors that influence performance. These insights provide a tool for pre-race analysis and data-driven strategy development. Future enhancements, including probabilistic models and simulations, could extend predictions across seasons and multi-day events.

A data-driven analysis of professional cycling identifies the age of peak performance across athlete specializations, including sprinters, all-rounders, and general classification riders. By combining classification and clustering techniques, the research offers actionable insights for athlete career planning. Personalized models could refine these findings further by tailoring predictions to individual trajectories.

The development of a low-cost, real-time player tracking system for field hockey demonstrates the potential of affordable technology to democratize sports analytics. Using a simple GoPro setup, the system achieves 93.8% accuracy in tracking players, providing underrepresented sports with access to data-driven insights. Further refinements could improve tracking performance, especially in high-traffic scenarios.

By addressing real-world challenges and proposing future advancements, this research highlights the transformative potential of Machine Learning in retail and sports. From improving recommendations to optimizing athletic performance and engagement, it offers a practical roadmap for deploying advanced algorithms in diverse, impactful applications.

Jonas Torfs

• 15/01/2025
  
• 17.00 uur
• Locatie: Campus Drie Eiken, O.05
• ​Online Doctoraatsverdediging​
• Promotoren: Marcel Eens & Nicky Staes
• Departement Biologie

Abstract

The animal gut houses a diverse community of bacteria, called the gut bacteriome, that performs essential functions in physiology, metabolism, and overall health. As such, it allows its host to respond to local environmental conditions, directly influencing host fitness. Despite the well-established role of the gut bacteriome in host biology, the forces shaping its composition within and between individuals of the same species remain poorly understood. 

In this thesis, I explored the gut bacteriome and its associated exposome and host-intrinsic factors in one of humans’ closest living relatives, the bonobo. In the first section of my thesis, I focus on addressing certain knowledge gaps in potential covariates of the bonobo gut bacteriome. First, I validated a method to assess body condition in bonobos and showed that it is heritable, and independent from dietary patterns. In another study, I examined the predictors of an individual’s position in the grooming network using a decade of data from zoo-housed bonobos. Age and rearing history impacted grooming behaviour, often in sex-specific manners, which fosters a better understanding of the link between grooming and the gut bacteriome. Finally, I showed that respiratory disease spreads following the proximity network in a zoo-housed group of bonobos.

The second section focused on mapping the bonobo gut bacteriome and its associated factors. I collected faecal samples with associated data from zoo-housed and wild bonobos and subjected them to 16S rRNA amplicon sequencing to determine gut bacterial profiles. Zoo-housed and wild bonobos differed remarkably in their gut bacteriome diversity and composition. Within zoo-housed individuals, lifestyle factors such as diet, early-life adversity, and medication use influenced bacteriome structure. On a dyadic level, social contact and environmental sharing were strongly correlated with gut bacteriome similarity, overshadowing maternal and genetic effects. 

Finally, I focused my attention on ageing patterns in the bonobo gut bacteriome, showing that across lifestyles (zoo vs wild) diversity increased with age while the core bacteriome shrank. Interestingly, ageing zoo-housed bonobos became more unique in their gut bacterial composition while this pattern was not observed in the wild, mirroring the human pattern. This points towards conserved aging processes in the hominoid gut bacteriome. In summary, this thesis offers key insights into the mechanisms through which the exposome influences gut bacterial communities in great ape species of which the gut bacteriome was not yet adequately characterized.

Adwine Vanslembrouck

• 13/01/2025
  
• 16.00 uur
• Locatie: ITM, Campus Rochus, Aula Janssens, Sint-Rochusstraat 43, 2000 Antwerpen
• ​Online Doctoraatsverdediging
• Promotoren: Herwig Leirs & Ruth Müller (ITM)
• Departement Biologie

Abstract

Invasive Aedes mosquitoes, such as the tiger mosquito (A. albopictus) and the Asian bush mosquito (A. japonicus), are known vectors for various viruses. One of the most significant consequences of these invasive mosquito infestations in Europe is the outbreak of mosquito-borne diseases such as dengue and chikungunya. These invasive Aedes mosquitoes are known to spread to new areas due to climate change and globalization. Additionally, anthropization to the environment, such as urbanization and deforestation, are important drivers of their dispersal. These human interventions lead to biotic homogenization, resulting in reduced biodiversity. This results in a reduced competition from native mosquitoes and a decrease in predators that could limit the spread and establishment of invasive mosquitoes. Furthermore, there is an increase in insecticide resistance, making it more difficult to control mosquito populations. As a result, there is a growing need for alternative control methods, such as biological control.

This thesis investigates the ecological interactions, competition dynamics, and implications for vector control strategies, with a particular focus on the invasive Aedes mosquitoes and native Culex species. The research covers interspecific larval competition, thermophilic preferences, and arboviral infection risks, demonstrating how larval competition among mosquito species affects their susceptibility for arboviruses. We found that Aedes albopictus larvae benefit from competing with Culex hortensis, potentially amplifying arbovirus transmission risks. Additionally, larval interactions among Aedes albopictus, Aedes japonicus, and Culex pipiens reveal metabolic and behavioral adaptations that increase susceptibility to arboviruses like chikungunya and West Nile virus.

The insecticide resistance status in European Culex pipiens populations highlights the limitations of conventional mosquito control methods. Resistance to multiple insecticides, also found in Belgium, points out the urgent need for innovative alternative approaches, including biological control. In this research, the role of native predatory diving beetles as natural regulators of mosquito populations in Belgium was investigated, identifying Agabus bipustulatus as a highly effective predator for Aedes albopictus.

In sum, this thesis emphasizes the importance of ecological interactions in understanding mosquito-borne disease risk and in developing sustainable mosquito control strategies. By integrating insights from larval competition, arboviral risks, and alternative control methods, it contributes to a broader understanding of vector ecology and offers novel approaches to mitigate the public health impacts of invasive mosquitoes in Europe. ​ ​

Ignace Pelckmans

• 09/01/2025
  
• 17.00 uur
• Locatie: Campus Middelheim, A.143
• Promotor: Stijn Temmerman
• Departement Biologie

Abstract

River deltas and estuaries are densely populated regions but face coastal flood risks due to extreme sea level events, particularly as climate change leads to rising sea levels and more instense storms. Nature-based strategies, such as the conservation and restoration of mangroves, are increasingly considered a cost-effective and sustainable addition to traditional flood defences. Cities such as Guayaquil (Ecuador), Ho Chi Minh (Vietnam) and Khulna (Bangladesh) all house millions of people but despite their growing exposure to coastal floods, the mangroves in between these cities and the sea have been largely lost to aquaculture. Yet, how the largescale conversion from mangrove to human land use affects the propagation of flood waves is still unknown. This thesis attempts to fill that knowledge gap by elaborating on how extreme sea levels are heightened or lowered when propagating through a tropical delta, and how this depends on the spatial properties of the complex network of mangroves, estuarine channels and human land use that is intrinsic to most tropical deltas. Hydrodynamic models allow the study of flood wave propagation over the tens to hundreds of kilometres that tropical river deltas typically cover. In this thesis, we present the setup, calibration and evaluation of a hydrodynamic model of the Guayas delta, Ecuador. The model demonstrated that the current mangroves in the delta effectively attenuate incoming extreme sea levels related to El Niño events and prevent the increase in sea level to amplify upstream. Furthermore, we show that the spatial locations of mangroves and aquaculture have a large impact on the attenuation of extreme sea levels with the strongest reduction in case mangroves are uniformly spread over the delta or concentrated upstream. During a field campaign, we measured high water level attenuation in a tropical Rhizophora forest, where aerial roots are several metres high, revealing attenuation rates up to 46 ± 9.8 cm/km, which are the highest attenuation rates ever recorded in a mangrove forest. With this thesis, we show how mangrove ecosystem properties and their spatial location within deltas affect the reduction of extreme water levels when propagating through the complex web of mangroves, channels and aquaculture, which is intrinsic to many tropical river deltas. Human infrastructure in the deltaic plain is often essential for the economic development of these regions, yet this thesis elaborates on where conversion from mangroves to human land use has minor effects on the reduction of high water levels.