Amin Jafarzadeh​

• 18/12/2020
  
• 3 p.m.
• ​Online PhD defence​
• Supervisors: Erik Neyts & Annemie Bogaerts
• Department of Chemistry

Abstract

The interest in utilizing plasma catalysis for environmental purposes continues to grow. Since a plasma-catalytic reaction can benefit both from the reactivity of the plasma and the high product selectivity induced by the catalyst, it is important to understand the plasma-catalyst interaction at a fundamental level in order to maximize the synergistic effects in practical experiments. Because of the highly complex network of processes happening simultaneously, revealing the entire mechanism is not straightforward. Utilizing computer simulations, we can study each factor separately and obtain detailed information about their effect on the whole reaction network. As part of an integrated multi-scale simulation scheme for understanding plasma catalysis, atomic scale calculations provide valuable insight into the interactions and subsequent changes in the catalyst electronic structure and the reactivity of atoms, molecules, and radical species present in the plasma. In that context, this thesis aims to provide answers to the following questions from the atomistic point of view: (1) How can we model plasma-induced surface charging and its impact on the adsorption of size selected catalytic nanoclusters on a support material? (2), What is the effect of “surface charging” on the activation of molecules, such as CO2 adsorbed on supported metal clusters? (3), How can we model the effect of an externally applied electric field in plasma catalysis? (4), How does the electric field change the activation and chemisorption properties of CO2 molecules adsorbed on Cu surfaces when combined with the effects of surface charging and the catalyst surface morphology? (5), How do plasma-generated radicals affect the plasma-catalytic reactions, especially for the case of ammonia reforming of methane for HCN production on Cu surfaces?, and (6) How can we simulate the reactivity of vibrationally excited species in a plasma-catalytic non-equilibrium environment?

The results of this thesis have demonstrated that the plasma-induced changes in the electronic structure of catalysts and reactants have great potential in steering the plasma-catalytic reactions and the answers to the abovementioned questions could help to adjust and optimize plasma-catalytic processes towards revamping renewable energy resources and mitigating environmental issues arising from greenhouse gas production. ​ ​

William Keustermans​

• 18/12/2020
  
• 4 p.m.
• ​Online PhD defence​
• Supervisor: Joris Dirckx
• Department of Physics

Abstract

Nasal airway obstruction (NAO) is a routinely encountered complaint by ear-nose-throat (ENT) physicians, affecting all age groups. NAOs yearly reduces the overall quality of life for millions of patients and, together with sinus diseases, cost the European healthcare systems billions of euros. The aetiology of NAO is determined by different conditions. If pharmacological treatment fails, as in many cases of anatomic abnormalities, surgery is often the treatment of choice. Nowadays, no proper gold standard currently exists for assessing nasal function impairment. Clinical examination is mainly used to make treatment decisions but frequently fails to pinpoint the cause of the patient’s perceived nasal obstruction. Current routine measurement techniques are found to correlate poorly with patients’ subjective feeling of nasal airway obstruction. However, these methods are used by rhinologists during decision making because no better objective measures are available. Therefore, the decision to proceed to surgery is generally based on the surgeon’s assessment, often without clear objective criteria. The reported surgical correction of nasal anatomic deformities with post-surgical symptom relief is inconsistent, and the range on reported failure rates inordinate. The introduction of a new standard for objective assessment of nasal airway obstruction could reduce the high failure rates by guiding surgical decision making.

Computational fluid dynamics (CFD) models have the potential to fill this gap by providing consistent and accurate information on nasal airflow and function. CFD can also be coupled to different physical laws, such as particle deposition and heat loss. Particle deposition in the nose depends on the airflow and is essential to understand nasal cleansing of airborne particles and for targeted drug delivery. Such physics-based models would enable an accurate diagnosis for each patient individually, together with an evidence-based selection of the most effective therapy while enabling postoperative evaluation. Ideally, and in combination with other techniques, allow taking into account the evolution of the internal nasal anatomy and its effect on airflow.

However, nowadays, some limitations inhibit such an approach becoming viable in the medical setting. Model-creation remains labour-intensive and time-consuming. The manual editing of X-ray tomographic cross-sections is not only tedious but also makes the model-creation prone to errors. On top of this, the numerical model design requires specific technical expertise that is not available for most physicians. In this dissertation, different machine learning and computer vision techniques were researched and combined with CFD to build proof-of-concept solutions to overcome existing limitations.

Charlotte Van Moorleghem​

• 15/12/2020
  
• 5 p.m.
• ​Online PhD defence​
• Supervisors: Katleen Huyghe & Raoul Van Damme
• Department of Biology

Abstract

For many animals, the ability to detect and recognise predators is crucial for their survival. Accordingly, species have evolved multiple sensory systems warning them of imminent dangers. One of these, the sense of smell, is probably the oldest and most widespread system, but likely also the least understood. This thesis explores the effects of predator type (mammal or snake), origin (native, invasive or allopatric) and insularity (mainland or island prey populations) on chemosensory abilities of lacertid lizards. Additionally, in order to lay bare the mechanics behind chemosensory predator detection in these lizards, I look into the chemical nature of predator-derived cues. Instigated by the alarming spread of the invasive mongoose (Herpestes auropunctatus, a notorious predator of reptiles) in the Balkan, I set out to test whether lacertids were able to interpret its odour. Surprisingly, individuals of the Asian grass lizard (Takydromus sexlineatus), a lacertid from the native range of the mongoose, exhibited no signs of stress when experimentally confronted with mongoose chemicals; chemicals of a sympatric snake predator elicited the behaviours typical for lacertids in dangerous situations. More puzzling, Dalmatian wall lizards (Podarcis melisellensis) from mainland Croatia did mount the typical anti-predatory response when brought into contact with mongoose scent. However, conspecific wall lizards living on islands failed to recognise mongoose chemicals, or ignored them. In fact, island lizards showed signs of chemosensory deprivation in general: they did not respond to a sympatric snake predator, and brain areas involved in the processing of chemical signals tended to be smaller than in mainland specimens. I hypothesise that insular conditions (limited resource availability and predator relaxation) select against chemosensory investment. In the second part of the thesis, I used a well-known study system (recognition of adder, Vipera berus, chemicals by common lizards, Zootoca vivipara) to test the usefulness of two techniques for the identification of kairomones. I found that neutral lipids, extracted with n-hexane from adder skin, provoked the typical fear-response in lizards. Gas Chromatography – Mass Spectrometry revealed a complex cocktail of 165 different molecules, several of which are likely candidate-kairomones. In a subsequent study, I tested a recently developed technique (Proton-Transfer Reaction Time-of-Flight Mass Spectrometry) that allows real-time capture and extremely accurate mass annotation of volatile molecules. Common lizards can detect the presence of adders based on such volatile molecules only. These results emphasise the complexity of the information prey animals can obtain from their predators’ scent. ​ ​

Maxim Pieters​

• ​14/12/2020
  
• 3.30 p.m.
• ​Online PhD defence​
  
• Supervisors: Pierre Van Mechelen & Hans Van Haevermaet
• Department of Physics

Abstract

A study of inclusive four jet production in proton-proton collisions at a centre-of-mass energy of 13 TeV is presented. The data sample was collected in 2016 with the CMS detector at the LHC during a low intensity run, with an integrated luminosity of 0.042 pb-1. Differential cross sections are measured as a function of the jet transverse momentum, pseudorapidity, and several other observables that exploit angular correlations. The lowest jet transverse momentum cuts required in this paper are 35, 30, 25, and 20 GeV for the first, second, third and fourth leading jet respectively within |η| < 4.7, leading to a fiducial cross section of σ = 2.77 ± 0.02 (stat.)+0.68-0.55(syst.) μb. It is found that the data are very sensitive to different aspects of the underlying event, parton showers, and matrix element calculations. In particular the interplay between the de-correlations caused by parton showers and double parton scattering contributions is shown to be important. Models employing angular ordered parton showers, off-shell initial kinematics, as well as models with higher order matrix element calculations provide a better description of the data in certain observables, compared to standard leading-order models. The ΔS observable, which characterizes the azimuthal angular difference between the hard and soft jet pair, is used to extract a double parton scattering contribution by means of a template fit method. Model dependent values of sigma effective are calculated and compared to previous measurements. While all extracted values of the effective cross section show agreement with measurements performed at lower centre-of-mass energies, a strong model dependence of the double parton scattering contribution is found.

Yannick Uytdenhouwen​

• 11/12/2020
  
• 2 p.m.
• ​Online PhD defence​
• Supervisors: Annemie Bogaerts & Pegie Cool
• Department of Chemistry

Abstract

Combatting the ever rising concentrations of greenhouse gases in the atmosphere, in particular CO2 and CH4, is one of the biggest challenges of peoplekind in this century. Reducing emissions and developing innovative solutions for capturing and reusing the gases that are inevitably produced, are the tasks at hand for the next decades. However, novel technologies are required in order to convert these greenhouse gases in a sustainable and efficient way. Plasma technology could offer a viable solution, by directly targeting the molecules in reacting into value-added chemicals. Their quick on-and-off-switching capabilities by electrical energy, in combination with intermittent renewable energy sources, makes them a promising technology to directly convert CO2 and CH4 in a sustainable way. Therefore, in this work, we studied the potential use of the DBD reactor for sustainable CO2 and CH4 conversion. We aimed to improve the reactor performance via different methods, and to develop a technique to gain more fundamental insight on how the kinetics in the reactor change on the macro scale when optimising the performance. First we investigated the influence of micrometre sized discharge gaps and packing materials to enhance CO2 dissociation conversions. The results show that smaller gap sizes are beneficial and that the performance of a packing material greatly depends on the specific combination of material composition, sphere size, and gap size. Further investigation with core-shell structured spheres showed that overall sphere properties can be optimised to a specific use. Next, an apparent first order reversible reaction fit was developed to retrieve more fundamental parameters, such as equilibrium conversion and reaction rate coefficients, on a macro level scale. By tracking the reactor conversion over a wide range of residence times for different cases and matching the results to our fit, we have elucidated how the applied power, reactor pressure, discharge gap size, and the addition of packing materials change the kinetics to influence the reactor performance in CO2 dissociation, CH4 reforming, and dry reforming of methane and their product distribution. Finally, we explored whether the reactor performance can be optimised for bi-component gas mixtures by altering the gas flow design in the reactor. The results assessed the potential of this method for dry reforming of methane and ammonia synthesis and showed room for improvement in conversion and product distribution.

Jonas Buys

• ​25/11/2020
  
• 10 a.m. 
• ​Online defence 
• Supervisors: Vincenzo De Florio and Chris Blondia
• Department of Computer Science
  

Business- and mission-critical distributed applications are increasingly expected to exhibit highly dependable characteristics, particularly in the areas of availability and QoS-related factors such as timeliness. For this type of applications, a complete cessation or a subnormal performance of the service they provide, as well as late or invalid results, are likely to result in significant monetary penalties, environmental disaster or human injury. However, software components deployed within distributed computing systems may inherently suffer from several types of impairments, such as long response times or temporary unavailability.

Adopting classic redundancy-based fault-tolerant design patterns, such as NVP, in highly dynamic distributed computing systems does not necessarily result in the anticipated improvement in dependability. This primarily stems from the statically predefined redundancy configurations hardwired within such dependability strategies, i.e. a fixed degree of redundancy and, accordingly, an immutable selection of functionally-equivalent software components, which may negatively impact the schemes’ overall effectiveness, at least from the following two angles. Firstly, a static, context-agnostic redundancy configuration may in time lead to a more rapid exhaustion of the available redundancy and, therefore, fail to properly counterbalance any disturbances possibly affecting the operational status (context) of any of the components integrated within the dependability scheme. Secondly, the amount of redundancy, in conjunction with the voting algorithm, determines how many simultaneously failing versions the NVP composite can tolerate. A predetermined degree of redundancy is, however, cost ineffective in that it inhibits to economise on resource consumption in case the actual number of disturbances could be successfully overcome by a lesser amount of redundancy.

In this thesis, a novel dependability strategy is introduced encompassing advanced redundancy management, aiming to autonomously tune its internal redundancy configuration in function of the observed disturbances. Designed to sustain high availability and reliability, this adaptive fault-tolerant strategy may dynamically alter the amount of redundancy and the selection of functionally-equivalent resources employed within the redundancy scheme. In doing so, the algorithm relies on a number of measures designed for approximating the operational status of the redundancy configuration in terms of availability, and of individual resources in terms of reliability. Discrete-event simulation is used to analyse the effectiveness and performance of the algorithm, and to illustrate how it addresses the shortcomings commonly observed in conventional NVP approach.

​Dajana Radujkovic​

• 25 November 2020
  
• Time: 10 a.m. 
• ​​Online defence​
• Department of Biology
• Supervisors: Erik Verbruggen and Sara Vicca
  

Abstract

Plants and soil microorganisms are the main components of every terrestrial ecosystem. They drive the cycle of carbon in nature and they form complex, often species-specific interactions with each other shaping both aboveground and belowground communities. Soil is a medium that connects these two worlds and mediates all interactions between them. This work explores the role of soil abiotic properties in predicting plant productivity and microbial (bacterial and fungal) community composition patterns in grassland soils worldwide and the role of plant-soil interactions on the development of soil fungal community composition in heathlands.

Our findings demonstrate that soil properties determining soil fertility and nutrient availability (soil organic matter, cation exchange capacity, percentage of sand, soil Zn concentrations) can explain a substantial amount of variation in global grassland productivity which surpasses the predictive power of commonly used climatic predictors. These same properties are also shown to be the best predictors of the local-scale variation in plant productivity across globally distributed grassland sites.

Regarding soil microbes, we show that there is generality in predictors of microbial community composition along globally replicated, local-scale grassland productivity gradients (where abiotic factors such as pH and base saturation best predict bacterial community composition and plant communities best predict fungal community composition). Furthermore, we find that that different plant productivity levels are consistently associated with distinct soil microbial communities across different grassland sites. Finally, we demonstrate that plant-soil interactions are important factors determining fungal community assembly in heathlands, which can override the importance of abiotic soil conditions.

These findings suggest that considering key soil physicochemical properties when predicting the effect of environmental changes on grassland productivity would not only improve grassland productivity models, but it could also help predict the shifts in soil microbial community composition. Moreover, given that some of the factors and interactions predicting both grassland plant productivity and microbial community composition were found to be universal across contrasting climates, similar general patterns might also hold in other systems. While this remains to be explored, the universal patterns observed in this study provide confidence that making global predictions regarding future changes in plant productivity, soil microbial community composition and, possibly resulting changes in ecosystem functioning, is a feasible task.

​Vincent Vermeiren​

• 23 November 2020
  
• Supervisor: Annemie Bogaerts
• Department of Chemistry 
• Venue: Online defence
• Time: 11:00 AM

Abstract

In our research, we aim to reveal the conditions at which the most energy efficient dissociation of CO2 takes place, for plasmas in which both vibrational induced dissociation and thermal dissociation become important.

First, a supersonic flow microwave plasma model is investigated. This model reveals the effect of the flow on the plasma performance. The results reveal that the time delay for vibrational induced dissociation to take place, as well as the maximum specific energy input that can be added before the flow is choked are the main limitations to reaching high energy efficiency.

Next, it is shown that pulsing the plasma can increase the vibrational-translational non-equilibrium, that is needed for efficient vibrational induced dissociation. The maximum improvement is reached when the plasma pulse time equals the time at which the vibrational temperature reaches a maximum value, and for long interpulse times, so that the gas can cool down before the next pulse starts.

Finally, the effect of thermal quenching on the plasma performance is investigated for warm and cold plasmas at different specific energy inputs. It is shown that quenching can increase the final CO2 conversion by reducing the recombination mechanisms, and that high efficiencies are reached for thermal plasmas in combination with quenching.
This PhD thesis increases our knowledge of the kinetics in CO2 plasmas, and gives valuable insight for experimentalists.

​Jennifer Jörissen​

• 20 November 2020
  
• Supervisors: Sarah Lebeer and Olivier Vanderveken
• Department of Bioscience Engineering

Abstract

Otitis media with effusion (OME) is a common childhood disease characterized by accumulation of fluid in the middle ear without symptoms of an acute infection. Chronic OME, lasting longer than three months, is often treated by placing a drainage tube into the ear drum, and there is an urgent need to develop non-invasive treatment and prevention methods. All bacteria traditionally associated with middle ear diseases are commensals also found in the upper respiratory tract (URT) of healthy individuals. Interaction with the host immune system and other bacteria present in the same niche determine their ability to expand into neighbouring anatomic locations and to express their virulence. Long-term perturbation of the microbiota has been associated with several chronic inflammatory diseases and is also hypothesized to underlie chronic OME. Preventing such perturbation or restoring a perturbed microbiota through addition of beneficial bacteria could therefore be a valuable method for OME prevention, reducing the need for surgical intervention.

This project aimed to identify bacteria associated with or protective against chronic OME. We first compared the microbiome of several URT and ear niches of 70 chronic OME patients and 63 healthy controls by sequencing the diverse V4 region of the bacterial 16S rRNA gene. This allowed us to identify bacterial taxa over-represented in healthy individuals or dominant in OME middle ear effusion. Next, we isolated health-associated bacteria and predicted their beneficial effect, ability to survive in the human URT and their safety through in vitro experimentation and genome analysis.

OME middle ear effusion frequently harboured one of several dominant (>50% relative abundance) taxa likely responsible for OME, which were traced to the URT and the ear canal. In contrast, most healthy middle ear rinses could not be sequenced, indicating very low biomass in or even sterility of this niche. Therefore, potentially health-associated bacteria were identified by comparing the nasopharynx microbiome between OME patients and healthy controls. Salivarius group streptococci and Acinetobacter lwoffi were significantly more abundant in the nasopharynx of healthy children compared to OME patients, but only the Streptococcus taxon was present in ≥50% of children and could be isolated. Seven Streptococcus salivarius isolates were characterized in detail and five were free of known virulence and transferable antibiotic resistance genes, inhibited all tested pathogens and adhered well to respiratory epithelial cells. This makes them promising candidates for further testing and development as potential probiotics or microbiome therapeutics to prevent or treat OME.

​Ignace Van Spilbeeck​

• 17 November 2020
  
• Supervisor: Benny Van Houdt
• Department of Computer Science

Abstract

Load balancing is one of the key components in many distributed systems as it heavily impacts performance and resource utilization.

This thesis considers heterogeneous systems where servers belong to one of multiple classes, with the speed of an individual server depending on its class.
Two categories of load balancing strategies are considered.

The first category consists of centralized load balancing strategies, where a dispatcher assigns incoming jobs to the servers. Both randomized dispatching and size-based dispatching strategies are considered.

For these strategies, we propose mathematical models to describe the queue lengths over time and validate them through discrete event simulation. We show that the dispatcher's optimal parameter can be determined via convex optimization, if it exists.

Furthermore we also investigate the effects of different system parameters on the achievable mean response time and propose several easier to compute schemes for determining the dispatcher's selection parameter.

Finally, we also discuss the impact of scheduling policies in the nodes in addition to the load balancing strategies.

The second category consists of decentralized load balancing strategies where no dispatcher is used, but servers can exchange jobs through communication by means of probing.
For these load balancing strategies, we use a mean field model to study the queue lengths and the required probe rate to achieve stability. We also develop an iterative algorithm to more easily compute the mean response time.

Finally we study and compare the mean response times for specific "pull" and "push" strategies for different parameter settings.

Nathalie Brenard​

• 21 October 2020
  
• Supervisors: Herwig Leirs, Rob Moerkens and Vincent Sluydts
• Department of Biology

Abstract

Biological pest management is already quite successful in European sweet pepper greenhouses and many pests can be controlled by releases of natural enemies. Aphids, however, are a major pest for which the currently used specialist natural enemies don’t provide sufficient control and chemical interventions are often applied. But due to their high reproductive rate and short generation time, aphids are able to quickly develop insecticide resistance.

Nowadays, there’s increased attention to generalist natural enemies in pest control since they can survive in crops when pests are absent. Macrolophus pygmaeus is a generalist predatory bug commonly used in European tomato greenhouses against a number of pest species.

In this PhD research, the use of M. pygmaeus in sweet pepper greenhouses was studied. First, release strategies were tested with regard to food supplementation. Not all generalist natural enemies require food supplementation and different food types or supplementation strategies can affect population growth and dispersal. Supplementing brine shrimp cysts biweekly in a full field fashion proved to be the best strategy for M. pygmaeus establishment.

Next, the bug’s potential as biological control agent against two sweet pepper key pests, aphids and thrips, was investigated. The effect of M. pygmaeus on aphid control was combined with leaf pruning at four different heights in order to decrease the foliage and bring pest and predator closer together. The bugs were found to successfully control aphid infestations in sweet pepper if vertical foliage length of the plants was kept no longer than 190cm.

Western flower thrips is currently managed in sweet pepper by releasing the predatory bug Orius laevigatus. However, M. pygmaeus also feeds on thrips and may dismiss the need for O. laevigatus releases. During the three seasons of experiments for this PhD, M. pygmaeus was always able to control thrips outbreaks on its own.
These thrips and M. pygmaeus population density data were consequently used to construct predator-prey models that use weekly monitoring data to predict chance of pest control in the next week.

With our findings, M. pygmaeus can be successfully released to control pests in sweet pepper greenhouses, which will bring growers a step closer to a pesticide-poor or even –free sweet pepper cultivation.

Mylène Hendrickx​

• 20 October 2020
  
• Supervisor: Joke Hadermann
• Department of Physics

Abstract

The type and arrangement of the atoms in a material, called the crystal structure, determine its physical properties. Thus to gain control over materials properties, the first step is to determine these crystal structures. During my PhD, I have investigated the crystal structures of two main groups of materials, perovskites and Li-ion battery cathode materials, to which cation substitution was applied in an attempt to induce and enhance specific properties. For the perovskites, which can have a wide variety of properties depending on small structural changes, we focussed on the magnetic properties, of great importance in nano-electronics. With the Li-ion batteries, we tried to find new cathode materials with enhanced electrochemical performance.

The crystal structures of these new materials were investigated using advanced transmission electron microscopy (TEM) in both reciprocal space (electron diffraction) and direct space (imaging techniques), combined with energy-dispersive X-ray spectroscopy (EDX). Based on these studies, we proposed and refined models for the new structures. With these models, we were able to explain the variations in the properties of these materials. The study of the relation between structure and properties has yielded fundamental knowledge, applicable for the optimization of the properties of the investigated materials as well as of related materials.

Myrthe Pareyn​

• 15 October 2020
  
• Supervisors: Herwig Leirs and Simon Shibru
• Department of Biology

Abstract

Cutaneous leishmaniasis (CL) is a neglected tropical disease characterized by nodular and crusty skin lesions, mainly on people’s face and extremities, resulting in disfiguring scars after healing. It is caused by Leishmania parasites, which are transmitted by female phlebotomine sand flies during blood feeding. The disease is a major public health problem in Ethiopia, where Leishmania aethiopica affects approximately 20,000 to 50,000 people annually and transmission is zoonotic, with hyraxes serving as reservoir hosts.

The first main objective of this thesis was to thoroughly explore the transmission cycle of CL in an endemic hotspot in southwestern Ethiopia and map the distribution of the vector and the infection in a larger surrounding area. We found that aside from hyraxes, humans also play an important role in transmission, hence early diagnosis and treatment of the human reservoir is pivotal for disease management. Furthermore, we show that the vector is mainly biting at night indoors. This information can be used to limit the human-vector contact by for instance the use of bed nets. The distribution of both the vector and the disease is much more widespread than reported, which is calling for better surveillance. The generated distribution map can be used for guidance of control interventions.

The second objective was to comparatively assess the performance of different molecular assays for detection of Leishmania aethiopica in clinical samples and sand flies. We show that the probe-based LC kDNA PCR, which was developed for L. aethiopica detection specifically, performs superior and should be implemented in routine practice for CL diagnosis in case microscopy results are negative. For large entomological or eco-epidemiological studies, however, we recommend the use of a crude high-salt extraction buffer with ethanol precipitation step for DNA isolation in combination with a SYBR Green qPCR assay targeting the spliced leader (SL-)RNA sequence.
For the third objective, we assessed that the recently introduced matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) protein profiling technique is a suitable alternative for unambiguous sand fly species determination in Ethiopia.

Collectively, this thesis lays groundwork for adequate CL detection and disease control and indicates the use of novel techniques for accurate, cost-effective future entomological surveys in Ethiopia.

Joren Vanherck

• 15 October 2020
• Supervisors: Wim Magnus and Bart Sorée
• Department of Physics

Abstract 

Two-dimensional (2D) materials have dominated the fields of solid-state physics and technology in recent years. With the experimental discovery of several atomically thin ferromagnets in 2017, magnetism has also properly entered this exciting domain. From a technological perspective, understanding the exact thin-film magnetisation behaviour is essential in the ever-continuing down-scaling of integrated circuit components, striving for cheaper, faster and more energy-efficient devices. From a scientific perspective, the origin of two-dimensional ferromagnetism is not well understood, as it seemingly contradicts the (in)famous Mermin-Wagner theorem — excluding the possibility of two-dimensional ferromagnetic ordering. To address this dissatisfying situation, we studied a quantum Heisenberg model with beyond-nearest-neighbour, anisotropic exchange interactions at non-zero temperatures. This model is believed to accurately describe some of the experimentally discovered 2D ferromagnets, such as CrI3, CrBr3 and MnSe2. We obtained results using Zubarev’s double-time temperature-dependent Green functions with Tyablikov’s decoupling approximation, which are known to give meaningful results for the entire temperature range. We compared results for two- and three-dimensional materials, investigated the effect of an applied, homogeneous field in arbitrary direction and extended the methodology to properly account for magnetic dipolar interactions.

We managed to reproduce the predictions of the Mermin-Wagner theorem and find that spontaneous magnetisation in 2D materials is still possible when an easy-axis exchange anisotropy is present. Combining our results with ab initio calculations allowed us to reproduce Curie temperatures in agreement with experiments. The behaviour of magnetism in easy-axis two-dimensional materials turns out to be similar to that of their bulk counterparts, while being distinct from easy-plane 2D materials. Overall, the magnetisation aligns mostly with the internal anisotropy at low temperatures and fields, while getting reoriented towards the homogeneous external field direction otherwise. Dipolar interactions might lead to an additional easy-plane anisotropy for thin films, but its corresponding long-range character might stabilise the magnetisation. In conclusion, we found an effective way to describe some recently discovered 2D ferromagnets, explaining their non-zero Curie temperatures by the presence of easy-axis anisotropy. In such materials, our results allow predictions on the magnetisation at non-zero temperatures and applied fields at arbitrary angles. To stimulate further computational discoveries of monolayer ferromagnets, we made a computer program publicly available to calculate Curie temperatures based on our formalism.

Jennifer Jörissen​

• 9 October 2020
• Supervisors: Sarah Lebeer and Olivier Vanderveken
• Department of Bioscience Engineering

Abstract 

Otitis media with effusion (OME) is a common childhood disease characterized by accumulation of fluid in the middle ear without symptoms of an acute infection. Chronic OME, lasting longer than three months, is often treated by placing a drainage tube into the ear drum, and there is an urgent need to develop non-invasive treatment and prevention methods. All bacteria traditionally associated with middle ear diseases are commensals also found in the upper respiratory tract (URT) of healthy individuals. Interaction with the host immune system and other bacteria present in the same niche determine their ability to expand into neighbouring anatomic locations and to express their virulence. Long-term perturbation of the microbiota has been associated with several chronic inflammatory diseases and is also hypothesized to underlie chronic OME. Preventing such perturbation or restoring a perturbed microbiota through addition of beneficial bacteria could therefore be a valuable method for OME prevention, reducing the need for surgical intervention.

This project aimed to identify bacteria associated with or protective against chronic OME. We first compared the microbiome of several URT and ear niches of 70 chronic OME patients and 63 healthy controls by sequencing the diverse V4 region of the bacterial 16S rRNA gene. This allowed us to identify bacterial taxa over-represented in healthy individuals or dominant in OME middle ear effusion. Next, we isolated health-associated bacteria and predicted their beneficial effect, ability to survive in the human URT and their safety through in vitro experimentation and genome analysis.

OME middle ear effusion frequently harboured one of several dominant (>50% relative abundance) taxa likely responsible for OME, which were traced to the URT and the ear canal. In contrast, most healthy middle ear rinses could not be sequenced, indicating very low biomass in or even sterility of this niche. Therefore, potentially health-associated bacteria were identified by comparing the nasopharynx microbiome between OME patients and healthy controls. Salivarius group streptococci and Acinetobacter lwoffi were significantly more abundant in the nasopharynx of healthy children compared to OME patients, but only the Streptococcus taxon was present in ≥50% of children and could be isolated. Seven Streptococcus salivarius isolates were characterized in detail and five were free of known virulence and transferable antibiotic resistance genes, inhibited all tested pathogens and adhered well to respiratory epithelial cells. This makes them promising candidates for further testing and development as potential probiotics or microbiome therapeutics to prevent or treat OME.

Kevin NYS

• 25 September 2020
  
• Supervisor: Sabine Van Doorslaer
• Department of Physics

Abstract

Heme-containing proteins are omnipresent in all kingdoms of life and display a striking versatility. While the function of heme proteins is largely determined by the protein fold dictating the active site architecture, accessibility and redox properties, there is no one-on-one relationship linking a particular protein structure with a specific function. In this work, I studied two types of heme proteins, observing how structural elements and subtle changes are able to tune the functional role. To this end, optical spectroscopy and electron paramagnetic resonance were employed as they are able to selectively probe the redox-active sites of heme proteins.

Dye-decolorizing peroxidases are heme-containing oxidoreductases of bacterial and fungal origin. They catalyze the hydrogen-peroxide-mediated oxidation of bulky textile dyes and lignin model compounds. A combined biochemical and biophysical approach was used here providing better understanding of the catalytic properties of this protein class, paving the way for applications in waste water treatment and biomass conversion. The roles of the distal amino acid residues in a subclass of dye-decolorizing peroxidases were elucidated and the accessibility of the active site was thoroughly examined. This also resulted in some hands-on advice for future EPR spectroscopic studies on heme proteins.

Moreover, a persistent organic radical in the resting state of a dye-decolorizing peroxidase was observed and characterized in this work, utilizing a highly fruitful strategy which combines computational predictions and spectroscopic experiments. The discovery of a dyad motif provided the first report of a stable radical in the resting state of a heme peroxidase.
Another heme protein concerned in this work is hemoglobin. While the latter is commonly known for its role in oxygen transport, it is intriguing that hemoglobin is also found in insects which breathe through a tracheal respiratory system. I performed a spectroscopic study on hemoglobin from the European honeybee and the malaria mosquito, showing some surprising differences in their heme cavity, ligand affinity and proposed function despite a similar living environment.

Daniel van den Akker​

• 16 September 2019
  
• Supervisor: Chris Blondia
• Department of Computer Science

Abstract

Leandro Van Langenhove ​

• 9 September 2020
  
• Supervisors: Ivan Janssens, Pascal Boeckx and James Weedon
• Department of Biology 

Abstract

Tropical forests are among the most productive ecosystems in the world and play a central role in global biogeochemical cycles due to the high exchange of carbon between biosphere and atmosphere. Nutrient cycles, especially of nitrogen and phosphorus, exert an important control on this exchange and by consequence also on forest growth and dynamics. How nutrients cycle between atmosphere, biosphere and pedosphere in tropical forests remains thus far poorly understood, mainly because a lack of data and a high diversity of tropical forest ecosystem functioning make generalizations difficult. To complicate matters, although tropical forests are very productive, they typically grow on impoverished soils with especially low mineral nutrient availabilities. This begs the question where the required nutrients are coming from. In this thesis, various sampling schemes are coupled with chemical measurements to evaluate how nutrient poor these soils are, and how much nutrients are coming into this system every year. For this, we focused on two topographic gradients with expected soil nutrient heterogeneity situated in two distinct tropical field sites in French Guiana. We measured soil nutrient distribution along the topographic gradients and found that soil phosphorus content differs with topography, a characteristic driven mainly by the soil texture. Additionally, we measured ecosystem nitrogen input through asymbiotic nitrogen fixation and found that these inputs are very low compared to other tropical forest regions, mainly because of the low phosphorus availability. Another nutrient input, namely atmospheric deposition, was also measured and results showed that organic nitrogen, an often-ignored part of the deposition, represented the main fraction of nitrogen deposition in our forest. Phosphorus deposition amounted to only 0.5 kg ha-1 y-1, and was most likely primarily derived from African dust transported across the Atlantic. This arguably low amount of phosphorus deposition, however, amounted to one third of the yearly amount of phosphorus recycled through litterfall, meaning that the amount deposited from the atmosphere is an important contribution to the ecosystem.

Finally, lab and field fertilization studies were conducted that showed that any P added to the soil is rapidly taken up by plant roots, and that plots receiving additional P showed a large increase in nitrogen fixation, indicating that phosphorus availability was severely limiting this process. From these results it becomes clear that phosphorus is in short supply in these forests and that its availability is severely limiting both microbial processes and plant growth.

Davide Di Croce​

• 3 September 2020
  
• Supervisor: Nick Van Remortel
• Department of Physics 
  

Abstract 

Measurements of the production of the standard model Higgs boson decaying to a W boson pair are presented in this thesis.

The W+W− candidates are selected in events with an oppositely charged and same-flavor lepton pair and large missing transverse momentum.

In order to constrain the Top quark production background, the events are classified in five different categories according to the jet multiplicity and the to their kinematical properties.
The dominant source of background arise from the Drell-Yan processes. Hence a multivariate analysis has been derived in order to recover as much signal as possible while rejecting the Drell-Yan events.

The main backgrounds of the analysis are data-driven, wherever possible about the shape and the normalisation.

The event sample of the analysis corresponds to an integrated luminosity of 137.1 fb−1, based on data collected in proton-proton collisions at a center-of-mass energy of 13 TeV by the CMS detector at the LHC during the Run II period spanning 2016, 2017 and 2018. The probability of observing a signal at least as large as the one seen, under the background-only hypothesis, corresponds to an expected significance of 3.1 standard deviations.

Since the HWW analysis with the whole Run II data is still pending approval for publication, the observed significance and signal strength modifiers from data are not shown.

Henceforth, the published analysis of the H->WW decay into same-flavor leptons performed by the author with the collected data by the CMS experiment in 2016 is presented. The data set corresponds to an integrated luminosity of 35.9 fb-1. The combination with others H->WW finals states resulted to an impressive observed significance of 9.1 standard deviation above the background-only hypothesis, corresponding to a measured signal strength of 1.28 times the standard model expectation. This is the first observation of the Higgs boson decays to a pair of W boson by the CMS Collaboration.

Mats de Jong

• 27 August 2020
  
• Supervisors: Karolien De Wael and Nele Samyn
• Department of Chemistry

Abstract 

Illicit drugs are everywhere in our society. The clandestine market is growing faster than ever before, with record breaking seizures in Europe (and particularly Belgium) concerning cocaine. Cocaine is singled out as a major substance of interest, in Europe and Belgium in particular. Currently used field tests for cocaine detection have several downfalls. They lack specificity, leading to false positive results, and are also easily bypassed by adding other (often colored) compounds, producing many false negative results. This lack of accuracy causes large costs for society: juridical, health-wise and economical. This, combined with the continuously growing drug retail market, presses the need for new and better portable detection devices for cocaine. This PhD thesis aimed at the development of a portable, reliable electrochemical sensor device for cocaine, allowing accurate analysis in field settings such as the Port of Antwerp. The potential of the electrochemical approach to replace other field techniques was tested and evaluated throughout the project. Electrochemical detection, square-wave voltammetry in particular, allows a fast (<40 s) analysis with high sensitivity, specificity and the possibility to detect multiple compounds (illicit drugs, adulterants and diluents) in one measurement scan, making it perfect for on-site screening purposes. In order to obtain this optimized electrochemical approach for cocaine, several fundamental and applied research steps were conducted of which the results are presented in this thesis: (1) determination of methodology and identification of interfering compounds, (2) defining the interfering mechanism of these compounds and provide solutions, (3) test possibility towards polydrug analysis (heroin), and (4) validation and field testing. This approach was followed to guide our fundamental research towards a highly relevant application. This PhD involved close collaboration with the National Institute for Criminalistics and Criminology, as well as with the Netherlands Forensic Institute, the Federal Judicial Police and Belgian Customs. All these agencies provided expert insights into the topic, as well as access to confiscated cocaine street and smuggle samples to help validating the developed technology. Lab validation on 374 samples delivered an accuracy of 98.4 % for the developed sensor, while the field measurements presented clear advantages over other screening tests, certainly concerning the detection of cocaine in colored and mixed smuggle samples. In conclusion, this work includes the research foundations to bring this cocaine detection technology to the market, where it has the potential to replace other commonly used screening techniques.

Eline Berghmans​

• 26 August 2020
  
• Supervisors: Geert Baggerman and Patrick Pauwels
• Department of Biology

Abstract 

Non-small cell lung cancer (NSCLC), accounting for 80-85% of all lung cancer cases, is generally linked with a poor prognosis and is one of the leading causes of cancer-related deaths worldwide for both women and men. Recently, immunotherapy has changed the landscape of NSCLC treatment. For those who respond, promising results, in terms of acceptable side-effects and antitumor activity by restoring host immunity, have been achieved in NSCLC patients by therapeutic blocking of immune checkpoint programmed cell death protein 1 (PD-1) and its ligand PD-L1. Unfortunately, to date, only a subgroup of patients experiences any long term benefit, while severe immune-related toxicities may occur in patients that do not clinically respond to the therapy. To avoid unnecessary costs and toxicities in patients who will not clinically respond, unique response patterns for anti-PD-(L)1 immunotherapy and additional biomarkers need to be discovered to overcome the limitations of PD-L1 as the sole predictive biomarker in clinical use.

​Proteomic approaches, in particular mass spectrometry, have already proven their relevance in discovery of new biomarkers, allowing early diagnosis or prediction of therapy response resulting in a better patient’s quality of life and improvement of health care. In this study, we demonstrated how the combinatorial use of mass spectrometry imaging (MSI) with top-down proteomics on NSCLC tissues was used to discover three candidate predictive biomarkers for anti-PD-(L)1 immunotherapy response in NSCLC patients. Evaluation of these three candidate predictive biomarkers (i.e. neutrophil defensin 1, neutrophil defensin 2 and neutrophil defensin 3) was performed with mass spectrometry imaging and verified with traditional immunohistochemistry and corresponding statistical analysis. The findings reported suggest the possible association between neutrophil defensin expression and response to anti-PD-(L)1 immunotherapy for a more accurate therapy decision for NSCLC patients. Finally, we demonstrated with in vitro physiological data that neutrophil defensin 1, 2 and 3 show an immune-stimulatory effect towards lung cancer.

​In conclusion, implementation of MSI and top-down proteomics in lung cancer research has led to the discovery of neutrophil defensins as biomarker to differentiate which NSCLC patients will benefit from anti-PD-(L)1 immunotherapy. MSI has further been used as a successful screening method to evaluate expression levels of these molecules in pretreatment biopsies, confirmed by immunohistochemical analysis. Lastly, the newly discovered neutrophil defensin biomarkers show potential as new molecular targets, essential for designing new effective therapeutic strategies.​

Piet Bladt​

• 9 July 2020
  
• Supervisors: Jan Sijbers, Arnold J. den Dekker and Eric Achten
• Department of Physics

Abstract ​

Perfusion is the delivery of blood to the capillary bed of the vascular system, where exchange of molecules between the blood and tissue compartment can occur. In the brain, perfusion is usually referred to as the cerebral blood flow (CBF). It is defined as the volume of blood (mL) delivered to a unit volume of brain tissue (100g) within a certain amount of time (min). The CBF can be affected in multiple brain disorders, such as in stroke, neurodegenerative diseases, cancer and epilepsy. It is therefore a potential biomarker for the diagnosis of such disorders or treatment follow-up. Arterial spin labeling (ASL) is a magnetic resonance imaging (MRI) technique that allows for absolute quantification of the CBF. It stands out from other imaging techniques capable of visualizing capillary perfusion, such as dynamic susceptibility contrast MRI and positron emission tomography, by its non-invasiveness, as no exogenous tracer is needed in ASL. In recent years, the position of ASL in the clinic has significantly matured with the help of the recommended clinical implementation put forth by the ASL research community.

​Despite the potential for absolute quantification, its non-invasiveness and the ease of the recommended implementation of ASL, significant issues remain. The inherently low signal-to-noise ratio of ASL data is at the origin of most of these issues. When estimating parameters (such as the CBF) from ASL data acquired in a clinical setting with a limited amount of scan time, the low SNR limits the amount of parameters that can be estimated with an acceptable precision. Adhering to this limit requires the use of simplified models with a small amount of parameters to be estimated, which often comes at a cost of accuracy (i.e., causes a bias). The balance between accuracy and precision of perfusion parameter estimation in ASL MRI is at the basis of each contribution of this work. Both the signal generation and imaging part on the one hand, and the quantification part of ASL, on the other hand, impact this balance. In this work, strategies are put forth to improve on the existing trade-off between accuracy and precision in ASL given certain acquisition settings.​

Aimeric Teyssier​

• 30 June 2020
  
• Supervisors: Erik Matthysen, Luc Lens and Joël White
• Department of Biology

Abstract 

The gut microbiota plays a fundamental role for host health and fitness. The gut microbiota is known to be shaped by host traits and environmental factors. One of the dominant causes of contemporary environmental change is the increase in human activity, with urbanisation representing one of the most radical forms of land use alterations in terrestrial ecosystems. These anthropogenic alterations are likely to alter host-associated microbiomes and the interactions between host and microbiota, resulting in adverse effects on hosts. The main aim of this thesis was to investigate the effect of anthropogenic alterations, on the characteristics of the gut microbiota of two passerine hosts, and to further examine the role of specific factors, notably diet and the rearing environment in shaping the gut microbiota.

By studying gut microbiota of free-ranging house sparrows and great tits in habitats with different levels of urbanisation I found that microbial diversity was reduced with urbanisation in house sparrows, but no effect was found in great tits. Urbanisation was also associated with modifications in taxonomic composition and community structure, and changes in functional composition. By exposing wild house sparrows from urban and rural populations to contrasting diets in an experimental set-up, with experimental diets based on a rural vs urban diet, I observed gut microbiota modifications with alterations of both α- and β-diversity and taxonomic composition, with the strongest shifts occurring in individuals exposed to contrasting diets. The influence of the nesting environment on the gut microbiota was investigated by performing a partial cross-fostering experiment in great tits halfway during nestling development. Results showed a significant decrease in microbial diversity between 8 and 15 days after hatching, as well as changes in community composition. In addition, fostered nestlings became more similar to their nest mates, providing evidence that the rearing environment plays a role in shaping the nestlings’ microbiota. Finally, gut microbiota characteristics were found to be related to host condition in both species with an effect of the microbial diversity, taxonomic and functional composition on the body mass and mass gain of the host.

To conclude, this thesis reports new insights on the effect of urbanisation on gut microbiota characteristics and provides experimental evidence for major factors that may induce microbiota changes, notably diet and rearing environment. It also highlights the potential impact of the gut microbiota and its disturbance on the performance of hosts living in cities.

Hans Vanrompay ​

• 26 June 2020
• Supervisor: Sara Bals
• Department of Physics

Abstract 

During the last decades, engineered nanoscale materials have become a common occurrence in available products and their functionalities are well spread across many topics. The specific properties of nanosystems are very often sensitive to their 3D structure. Therefore, for the development of nanomaterials geared towards specific applications there is an urgent need for fast and accurate 3D characterisation tools. One indispensable tool to study nanomaterials is transmission electron microscopy (TEM). State‐of‐the‐art TEM conventionally only results in 2D projections of 3D objects. Therefore, “electron tomography” was developed as a technique to investigate the 3D structure of nanomaterials. The approach is based on the acquisition of multiple 2D TEM images along different tilt angles. Next, these images are aligned and used as an input for a mathematical reconstruction algorithm that enables one to obtain the 3D structure of the original object.

​Although electron tomography yields very precise and local information on the 3D structure of nanoparticles, it is far from straightforward to obtain 3D information that can be considered as an averaged or statistically relevant result. This is a major drawback when trying to connect the properties of the nanoparticles to their 3D structure, which is crucial in order to obtain a general understanding concerning their structure‐activity relations. Currently, such studies cannot be performed due to the fact that both the acquisition of the tilt series and the 3D reconstruction are very time consuming. Obviously, one of the emerging challenges in the field of electron tomography is to increase the throughput of 3D reconstructions of nanoparticles.

​Here I will present novel techniques, aimed to reduce the run time of electron tomography experiments in order to enable high‐throughput and quasi real‐time characterisation of nanostructures. Such high‐throughput electron tomography experiments will yield statistically relevant 3D information concerning critical properties of nanomaterials. By developing acquisition methodologies that enable us to acquire a tomographic tilt series within several minutes, a plethora of new applications will become possible. Fast acquisition methodologies will also decrease the electron dose and/or dose rate, therefore lowering the harmful influence of the electron beam. In addition, by optimising the alignment and reconstruction processes, quasi real‐time 3D imaging at the electron microscope will come within reach. In this manner, the efficiency and applicability of 3D studies will improve and allow the user to dynamically steer ongoing (in-situ) tomographic experiments.​

Thais Milagres de Oliveira​

• 25 June 2020
  
• Supervisor: Sara Bals
• Department of Physics

Abstract

Shashi Kanth Reddy Singam​

• 25 June 2020
  
• Supervisors: Etienne Goovaerts and Michele Giugliano
• Department of Physics

Abstract 

In recent years the nitrogen-vacancy (NV) center in diamond has emerged as an atom-like system with many applications in precision measurements, quantum information processing and quantum fundamental research. In this thesis we focus on characterization of NV centers as a function of optical excitation as well as on a novel technique for local temperature sensing.

In this thesis several factors that affect the spin-dependent fluorescence of NV- centers such as the power of resonant microwave radiation, magnetic field strength and optical excitation intensity are explored. Fluorescence contrast is induced either by spin-resonant microwaves or by a static magnetic field, which can be applied for background free imaging of fluorescent nanodiamonds. Experiments are performed to measure the contrast characteristics of NV- centers under various optical excitation intensities on different types of samples, and the results are backed by an energy level simulation model for estimating population distribution between electronic and spin energy levels. These observations and the model provide good understanding of the contrast in imaging at various optical excitations and a basis for improving NV- detection. Later, by using the knowledge from the experiments, the method is applied to map nanodiamonds seeded in a neuron cell culture by means of the background free imaging technique using the static magnetic field contrast.

A new approach, called the frequency-jump method, is introduced to detect local temperature change on a surface of interest. The method, based on a specific frequency-modulation of the resonant microwaves, is tested first on a single crystal diamond sample and later on deposited nanodiamonds. Finally, this technique is applied on the real-world problem of measuring local temperature change in a GaAs/AlAs microelectronics circuit, by dropcasting nanodiamonds on the chip surface. The local diffusion of heat due to applied power to one of the devices is measured at different spots on the circuit resulting in local temperature detection at multiple points on the same device and in its surrounding. The measurement is time-dependent, which provides information about temperature change at the different spots as a function of time after sudden power switching.

Wouter Verstraelen​

• 24 June 2020
  
• Supervisor: Michiel Wouters
• Department of Physics

Abstract

Menelia Vasilopoulou-Kampitsi​

• 4 June 2020
  
• Supervisors: Jana Goyens and Peter Aerts
• Department of Biology 

Abstract

In this thesis, we studied the anatomy of the lacertid vestibular system as well as the manoeuvring capacities of lacertid lizards. When we investigated the interaction between vestibular system size, head size and microhabitat use, we found that lizards with smaller heads housed disproportionally large ears for their size. Since sensitivity of the vestibular system relies on the circulation of the endolymph inside the canals and is positively related to size, a large vestibular system in small animals would ensure the functioning of the ear. The microhabitat lizards occupy did not have an impact on this size relationship, because of spatial constraints of the skull. The shape analysis showed that species living in simple microhabitats possessed more anatomical adaptations linked to enhanced sensitivity than species in more complex habitats, suggesting that the former may benefit from increased sensitivity due to a higher visibility to predators.

Then, we tested the manoeuvrability, turning capacity and running performance of male and female Podarcis erhardi lizards, running on a zigzag and on a straight line racetrack. We used four P. erhardii populations situated at the Greek archipelago. In 2014 lizards were captured from the island of Naxos, Greece (site of Alyko) and were translocated to three experimental islets. The analyses on the microhabitat structure showed that the site of Alyko differed significantly in microhabitat structure from the three islets being covered more by sand rather than rocks and vegetation. We found that lizards from Alyko avoided areas lacking completely in shelters, a behaviour probably relating to thermoregulation and predator avoidance. The experiments performed for the population (males and females) of Alyko two years after the translocation (in 2016) showed that females turned more succesfully than males, however, a similar locomotor performance was observed for both sexes. We found that lizards on the experimental islands increased their size after their introduction, however their locomotor performance did not significantly differ from the performance of the individuals sampled in Alyko, 2016. We found that the average turning speed increased during the first two years of colonization, however two years later, it decreased. Islet lizards became better in avoiding collisions with the walls of the racetrack with time, and used more controlled manoeuvring strategies. We hypothesize that the complexity of the structural habitat on the experimental islets selects for improved manoeuvring capacities, and the lack of predation pressure selects for the use of slower manoeuvring in 2018.

Joy Eliaerts​

• 3 June 2020
• Supervisors: Karolien De Wael, Koen Janssens and Natalie Meert
• Department of Chemistry

Abstract

Worldwide, cocaine is commonly one of the most seized and used drugs. Currently, the screening of cocaine in seized powders is performed by means of colour tests. The major drawbacks of these tests are a lack of specificity and a subjective colour interpretation (‘50 shades of blue’). The high prevalence of cocaine and the limitations of colour tests have led to widespread interest in developing a fast method for identification and quantification of cocaine.

In this thesis, a new approach was established using Mid-InfraRed (MIR) spectroscopy in combination with Support Vector Machines (SVM). The SVM models resulted in a clear output (cocaine detected/not detected) and a reliable estimation of the purity of cocaine in a wide variety of street mixtures. Combined with SVM, the MIR technique is a simple, user-friendly and fast method to identify and quantify cocaine.

The developed chemometric models were tested in practice for the analysis of large cocaine seizures. A strategy was developed to obtain information about seizure homogeneity, the presence and concentration of cocaine and its most common adulterant, levamisole. Applying this method, the sample size as well as the number of confirmation analyses could be reduced.

It was also investigated whether the developed models could be applied to another MIR instrument of the same brand. Various strategies to perform a calibration transfer were compared. A mixed model, using data of both instruments, was the most successful and could be used on both instruments to detect cocaine.

A comparative study was conducted to determine if other spectroscopic techniques such as Raman and Near-InfraRed [NIR], in addition to MIR, could be used to classify and quantify cocaine. These techniques performed quite similar and could be considered as good alternatives for the MIR technique.

Finally, the current screening techniques (colour tests and MIR spectroscopy) were evaluated for the detection of cocaine in complex smuggling samples. Detection of cocaine was only possible after an extraction step prior to screening analysis.

It can be concluded that spectroscopic techniques combined with chemometric methods are an important added value for initial screening of cocaine. Moreover, an estimation of the purity is possible without wet chemistry. The obtained knowledge of this work can be applied for the detection of other illicit drugs, such as heroin and amphetamines.

Senne Van Loon​

• 29 May 2020
  
• Supervisors: Jacques Tempere and Hadrien Kurkjian
• Department of Physics

Abstract 

Ultracold Fermi gases offer a versatile system with which quantum mechanics can be studied on a macroscopic scale. Specifically, it allows to study superfluidity in different regimes, which are determined by the interaction between the fermions. This interaction can be tuned from ​​weak coupling -- where the fermions form weakly bound Cooper pairs according to the theory of Bardeen, Cooper, and Schrieffer (BCS) -- to strong coupling -- where fermions form strongly bound dimers that effectively behave like bosons and can form a Bose-Einstein condensate (BEC). The unitary Fermi gas is located between these two limits, where the interaction is resonant. This so-called BCS-BEC crossover is studied in this thesis, in particular the elementary excitations that occur in superfluid Fermi gases. Here two kinds of excitations can be recognized: a bosonic branch that represents the collective movement of the pairs and fermionic quasiparticles that depict their internal degrees of freedom. We investigate how the energy dispersion of the collective excitations influences the propagation of waves in the system, and how the energy and lifetime of the quasiparticles changes when they interact with the bosonic excitations.

Parisa Nematollahi​

• 27 May 2020
  
• Supervisor: Erik Neyts
• Department of Chemistry

Abstract 

Since the industrial revolution, the global air and sea temperature has increased significantly because of the rise in the concentration of greenhouse gases. Therefore, extensive research is carried out to both minimize the carbon emission from the exhaust of automobiles, petrochemical, agricultural and chemical industries, and reduce the current high levels of greenhouse gases by converting them into carbon-neutral fuels and other value-added industrial chemicals.

Graphene-based nanocatalysts are of great interest to the catalysis community due to their outstanding catalytic activity, surface properties, environmental friendliness, and cost-effectiveness. Surface modification of graphene-based materials is of great interest since it enhances the catalytic activity, electronic property, mechanical strength, and thermal conductivity of the nanocatalyst. The most commonly used surface modification methods are introducing defects, and doping with single metal atoms.

Two promising nanocatalysts are graphene and BC2N nano-flakes. The surface modification includes introducing defects or doping with single metal atoms. Depending on the type of nanocatalyst, the type of defects may change. The exact characteristics of the modified surfaces, the detailed reaction mechanisms, and the potential energy surface of direct conversion of methane to methanol, along with CO and NO oxidation to CO2 and NO2 on these surfaces at ambient conditions are unclear. Therefore, finding the corresponding reactions, detailed mechanisms, and characteristics of the tailored nano-surfaces was the main goal of this Ph.D. All the simulations were carried out using density functional theory (DFT) calculations. Our results reveal that the modified graphene and BC2N nanoflakes hold great promise toward gas conversion. Using these tuned nanostructures is energetically and thermodynamically interesting since they reduce the oxidation steps and their energy barriers, the formation of sub-chemicals, the possibility of surface poisoning with unwanted species, and make the reactions occur at ambient conditions. Our results may serve as guidance for fabricating a cost-effective graphene-based single-atom catalyst.

Wout Van Alphen​

• 25 May 2020
  
• Supervisor: Jacques Tempere
• Department of Physics

Abstract

Ultracold quantum gases consist of a collection of magnetically or optically trapped atoms cooled down to nanokelvin temperatures. At these ultralow temperatures, the laws of quantum mechanics, which are usually confined to the microscopic world of atoms and particles, now become apparent on the macroscopic scale of the entire cloud. This leads to remarkable behavior, such as the occurrence of flow without friction or “superfluidity”. In addition, these ultracold atomic gases possess a very high experimental tunability, thus making them the ideal systems for the study of quantum phenomena.

Non-linear excitations like dark solitons and quantized vortices play an important role in the dynamics of superfluid systems. A dark soliton is a solitary dip in the density of the fluid which does not change shape while propagating at a constant velocity, while a quantized vortex is the quantum equivalent of a whirlpool in classical hydrodynamics. Although several aspects of these solitary excitations have already been thoroughly examined in bosonic superfluids, much remains to be learned about their properties and dynamics in fermionic superfluids, which possess a more complex but also much richer physics than their bosonic counterparts. The aim of this thesis is to provide a detailed theoretical analysis of solitary excitations in fermionic quantum gases, through the use of a finite-temperature, low-energy effective field theory that was developed specifically for these systems. Our studies on the dynamics, stability and interactions of solitons and vortices throughout the various unique regimes of the superfluid Fermi gas demonstrate the emergence of new and interesting features that are clearly different from the established behavior of solitary excitations in bosonic quantum gases.

Marwa Kavelaars 

• 15 May 2020
  
• Supervisors: Wendt Müller and Luc Lens
• Department of Biology

Abstract 

In order to maximise fitness, parents need to be flexible and constantly adjust to changes in the environment. I focused on how a biparental seabird species, the Lesser black-backed gull (Larus fuscus), adapts its parental behaviour to the prevailing social and ecological conditions of its environment.
​
​I explored how parents could achieve efficient within-pair coordination and equality in reproductive investment, and whether that maximises reproductive success. I first investigated how partners divided a common parental task during the early phase of reproduction, i.e. incubating the eggs, and whether cooperation was facilitated by communication during nest relief. At this stage, partners may not yet fully trust each other, so negotiation over the amount of care to be provided might be particularly important. Subsequently, I investigated whether parents coordinated and co-adjusted their reproductive investment during the chick rearing phase, when parents have already invested substantially and built up trust with their partner. During both phases, pair members invested in parental care similarly, which suggests that parents co-adjusted their care behaviour. Parents also waited for their partner’s return to the nest before leaving the colony, resulting in a coordinated temporal spacing of foraging trips. Yet, I found no evidence that parents used the information they may have gained about their partner’s parental effort during changes of the parental shift to match their partner’s investment. A possible explanation could be that negotiation about the contribution to care may have already taken place during incubation.

​Additionally, I investigated how offspring provisioning may be affected by changes in their environment. The Lesser black-backed gulls of this study population breed and forage in an anthropogenic landscape, and are, therefore, continuously subjected to changes in their direct surroundings. After human-induced habitat loss, a part of the study population quickly settled in a neighbouring colony. However, the relocated gulls did not fully adjust their foraging behaviour, as they kept visiting distant, but familiar foraging sites, which entailed negative fitness consequences. This could relate to the high level of foraging specialisation, while with the current pace of human-induced environmental changes, opportunistic flexible foraging behaviour may become increasingly important for successful reproduction.

​During more demanding ecological circumstances, parental cooperation seemed to be especially important to successfully raise offspring. By incorporating the social and ecological environment, I emphasise how these aspects are intertwined and both influence the optimal parental strategy.​

Len Feremans​

• 14 May 2020
  
• Supervisor: Bart Goethals
• Department of Computer Science 

Abstract 

Finding patterns in long event sequences is an important data mining task. In the past, research focused on finding all frequent patterns, where the anti-monotonic property of frequency was used to design efficient algorithms. Recently, research focused on producing a smaller output containing only the most interesting patterns. In this thesis, we discover patterns using cohesion and quantile-based cohesion. Cohesion measures how close the items making up the pattern are on average. Quantile-based cohesion measures the proportion of pattern occurrences that are cohesive. We tackle the fact that both measures are not anti-monotonic by developing an upper bound to prune the search space. Experiments show that our method efficiently discovers important patterns that existing state-of-the-art methods fail to discover. In the second part of this thesis, we focus on multi-label classification which is important in different applications such as text categorisation, scene classification and bioinformatics. In machine learning, multi-label classification is the problem of identifying a set of labels for a new instance, based on a training database of labelled instances. Traditionally, methods learn a separate model for each label, however, this is not feasible for datasets with millions of labels. We propose a new algorithm that predicts labels using a linear ensemble of instance- and feature-based nearest neighbours. We tackle the problem of computing cosine similarity and similarity weighted predictions on large datasets using an inverted index and sparse optimisation. In addition, we propose a new top-k query with pruning based on a partition of the training database. Experiments show that our method is more accurate and orders of magnitude faster than state-of-the-art methods and requires less than 20 ms per instance to predict labels for extreme datasets consisting of hundreds of thousands of labels without the need for expensive hardware.

Roberta Sgammato

• 8 May 2020
  
• Supervisors: Wouter Herrebout and Christian Johannessen
• Department of Chemistry

Abstract

Globins are haem containing proteins ubiquitously expressed in all kingdoms of life, from bacteria to vertebrates. Thanks to the haem iron, globins can reversibly bind small ligands and can be involved in redox reactions. Nowadays more than 400 globins have been identified and classified into three main lineages and two structural families. Many of the recently discovered globins exhibit quite unusual structural architecture and a still unexplored mechanism of action. Revealing details about their structure-function paradigm can have an important biomedical relevance: a simple interaction between the globin haem iron with an exogenous ligand can eventually change the habits of the host organism and influence its adaptability to the environment, or in some cases its virulence. Moreover, some newly characterised globins are thought to have a neuroprotective function, hence a detailed knowledge of their mechanism of action could be beneficial for pharmacological purposes.

In the present work, we propose a spectroscopic approach to the study of haem-containing proteins, based on a combination of chiroptical techniques. In particular, we base our investigation on the use of resonance Raman optical activity (rROA) and electronic circular dichroism (ECD). While these techniques are routinely employed for the determination of the absolute configuration of natural compounds, their application to the study of the haem chromophore in globins, has been so far very poorly explored. The present thesis represents therefore a first, preliminary approach to relatively simple globins (or globin domains solely), using a non-classical spectroscopic approach. We have highlighted the capability of this methodology to detect conformational modifications of the achiral haem chromophore when placed in a protein matrix, and its fine sensitivity to small perturbations of the haem planarity induced by ligand binding. The early results show the potential of the chiroptical approach, and set the bases for a future investigation of more complex chimeric globins, via rROA and ECD.

Melissa Van Landeghem​

• 7 May 2020
  
• Supervisors: Etienne Goovaerts and Sabine Van Doorslaer
• Department of Physics

Abstract

The high potential of light-weight and flexible solar cells for future built-in and even wearable applications has driven research efforts towards thin-film photovoltaics. Such devices feature an active-layer thickness of only a few 100 nm, which is substantially less than the 200 μm thick wafers typically employed in Si-based solar cells. This thesis focuses on two emerging thin film technologies: organic solar cells (OSCs), which are based on molecular semiconductors, and perovskite solar cells (PeSCs) of which the active component is a lead halide with the perovskite crystal structure.

High-performance OSCs rely on a bulk heterojunction (BHJ) between an electron donor and acceptor for efficient photogeneration via charge transfer (CT). Hence, an important research objective is to identify the fundamental energy losses associated with the CT process and the main mechanisms governing charge recombination. This work contributes to this research area with an in-depth spectroscopic study of recombination via triplet excitons in two BHJ blends with a novel non-fullerene acceptor based on the 2,5-dithienylthiazolo[5,4-d]thiazole (DTTzTz) unit. Combining photo-induced absorption and luminescence spectroscopy, the photo-physical pathways of non-radiative recombination via triplets were determined, providing valuable insight into the possible suppression of triplet-related losses via molecular design.

Over the past decades, electron paramagnetic resonance (EPR) has played a crucial role in the study of OSCs because of its selectiveness in detecting the positive and negative charge carriers created in the BHJ blend under illumination. The spectral filtering methods developed in this work enable the unambiguous identification and characterization of these species when their spectra are strongly overlapping, which is often the case for fullerene-free blends.

The control of structural defects is essential for the further development of any semiconductor technology. Hence, one of the main objectives of this PhD research was to identify the dominant intrinsic defects in paradigm PeSC absorber methylammonium lead iodide. Hereto I have used a combination of continuous-wave and pulsed EPR and corresponding parameter computations in order to derive microscopic models for the relevant defects. While no signatures of paramagnetic defects could be observed by EPR in standard CH3NH3PbI3, this work presents the first experimental observation of light-induced polaronic states in related two-dimensional perovskites. As a first step towards future predictive computations of the magnetic resonance parameters of polaronic defects in perovskites using density functional theory (DFT), the thesis concludes with a case study of the well-known self-trapped electron in PbCl2, a closely-related lead halide material.

Jeroen Van Dijck​

• 4 May 2020
  
• Supervisors: Vera Meynen and Anita Buekenhoudt
• Department of Chemistry

Abstract

Stijn Heijkers​

• 27 April 2020
  
• Supervisor: Annemie Bogaerts
• Department of Chemistry

Abstract

Mariëlle Kaashoek​

• 24 April 2020
  
• Supervisors: Sandra Nauwelaerts, Peter Aerts and Friedl de Groote
• Department of Biology

Abstract

Members of the Equidae family, belonging to the Perissodactyla (odd-toed ungulates), underwent a strong digit reduction in both fore- and hindlimbs. The number of functional digits reduced to one (monodactyly). The driving forces behind the digit reduction within the Equidae are still unknown. Various hypotheses exist regarding possible selection pressures. It is currently believed that monodactyly is likely a result of multiple driving forces instead of just one main driving force. (Musculo)skeletal models of different representatives of the Equidae can be used to predict the locomotor performances (e.g. maximal speed and cost of transport) of (fossil) species in order to test hypotheses regarding the evolution of monodactyly from a locomotor perspective. Models are constructed using virtual representations of the skeletal system and biomechanical properties obtained from extant analogues.
The aim of this thesis was to obtain joint constraints and inertial properties of the forelimb of extant equids which could be implemented in to the (musculo)skeletal models. By including joint constraints, the locomotion simulations will be within a species natural movement range. In this thesis the studied joint constraints of the different equine forelimb joints were: the number of rotational degrees of freedom, the range of motion, the coupling between rotational degrees of freedom and the helical axis. Additionally, the inertial properties of the forelimb segments were also measured. They are needed in order for the model to accurately simulate the movement of the segments.

Our results showed that for the horse as a species, all forelimb joints displayed out of sagittal plane motion. For the elbow, fetlock and distal joints a clear coupling between rotational degrees of freedom was observed. For the helical axis, some properties of the different forelimb joints changed significantly with joint angle. Size only had a significant effect on a few of the helical axis properties of the different forelimb joints. The inertial properties did not differ between the three horses, donkeys and zebras for all forelimb segments except for the hoof. The three species used in this study did differ significantly in forelimb proportions. The results of the different joint constraints and inertia of the forelimb studied in this thesis should be taken into account when constructing (musculo)skeletal models of different monodactyl equids. This thesis provides the first step in testing hypotheses regarding the digit reduction within the Equidae using (musculo)skeletal models.

Daniel van den Akker​

• 25 March 2020
  
• Supervisor: Chris Blondia
• Department of Computer Science

Abstract 

Sensor networks consists of small, cheap, battery powered devices equipped with a number of sensors (temperature, noise, …) that communicate wirelessly with one another in order to provide a combined view of the area in which the sensors are deployed. Given the importance of extending the battery-lifetime as much as possible, it has long been considered standard practice to optimise or even custom-build the used communication protocols to the specific task for which the sensor network is being deployed. Because of this, there is generally no interoperability between sensor networks deployed in the same area.

Over the last decade however, the rise of the `Internet-of-Things’ has meant that sensor networks are increasingly expected to integrate seamlessly with external networks and infrastructure. This means that today interconnectivity and interoperability are more important than ever in the design of sensor networks.

​Therefore, the goal of this thesis to enable link-level interoperability between sensor networks using heterogeneous (incompatible) MAC protocols. The strategy for doing so is to use so-called virtual gateways: regular sensor nodes that have been configured to run multiple MAC protocols at the same time.

​To determine the feasibility of this approach, it is first investigated to
​what extent the performance of the MAC-heterogeneous sensor networks is affected by the interference that exists between them. It is shown that, except in extreme circumstances, the effect of this interference is small enough to allow these networks to co-exist without much issue.

​Next the feasibility of running multiple MAC protocols simultaneously on low-power sensor nodes is considered. To investigate this, the software architecture needed to do so is implemented for and evaluated using the extremely resource constrained Tmote Sky sensor node platform. It is shown that the proposed architecture is flexible and extensible enough to support a wide variety of MAC protocols and that the overhead of the Tmote Sky-implementation is minimal.

​Finally, the problem of virtual gateway selection is investigated. To this, end, the IRVG algorithm (Iterative Removal of Virtual Gateways) is introduced. This algorithm selects the virtual gateways to use by first configuring all sensor nodes as a virtual gateway and then iteratively disabling those virtual gateways that are unnecessary. It is shown that IRVG is able to both minimise the performance overhead of using virtual gateways and to balance between the possibly conflicting performance requirements of the individual networks.​

Marnik Bercx​

• 20 March 2020
  
• Supervisors: Dirk Lamoen and Bart Partoens
• Department of Physics

Abstract

Materials and their properties play a vital role in most applications we use on a daily basis. During the past few decades, computational materials science has started evolving more and more into a predictive tool instead of simply offering theoretical insight into the physical processes of materials of interest. In combination with increasingly available tools for automating the required calculations, this has led to the concept of in silico materials design, where large numbers of compounds are investigated using computer simulations in order to gauge their potential for a specific application.

Among the most successful theoretical frameworks for computational materials science is density functional theory, which can determine the electronic structure of many compounds with ever increasing accuracy using a reasonable amount of computational resources. However, the connection between the electronic structure of a material and the property of interest for a specific application is rarely trivial. The main goal of my work is to provide or improve this connection, by analyzing existing metrics for flaws or anomalies, and developing new descriptors of material properties as well as the tools for calculating them using automated workflows. These methods are then applied to a set of topics including solar cells, Li-ion batteries and ion-induced secondary electron emission.

Joy Eliaerts​

• 17 March 2020
  
• Supervisors: Karolien De Wael, Koen Janssens and Natalie Meert

Abstract

Worldwide, cocaine is commonly one of the most seized and used drugs. Currently, the screening of cocaine in seized powders is performed by means of colour tests. The major drawbacks of these tests are a lack of specificity and a subjective colour interpretation (‘50 shades of blue’). The high prevalence of cocaine and the limitations of colour tests have led to widespread interest in developing a fast method for identification and quantification of cocaine.

In this thesis, a new approach was established using Mid-InfraRed (MIR) spectroscopy in combination with Support Vector Machines (SVM). The SVM models resulted in a clear output (cocaine detected/not detected) and a reliable estimation of the purity of cocaine in a wide variety of street mixtures. Combined with SVM, the MIR technique is a simple, user-friendly and fast method to identify and quantify cocaine.

The developed chemometric models were tested in practice for the analysis of large cocaine seizures. A strategy was developed to obtain information about seizure homogeneity, the presence and concentration of cocaine and its most common adulterant, levamisole. Applying this method, the sample size as well as the number of confirmation analyses could be reduced.

It was also investigated whether the developed models could be applied to another MIR instrument of the same brand. Various strategies to perform a calibration transfer were compared. A mixed model, using data of both instruments, was the most successful and could be used on both instruments to detect cocaine.

A comparative study was conducted to determine if other spectroscopic techniques such as Raman and Near-InfraRed [NIR], in addition to MIR, could be used to classify and quantify cocaine. These techniques performed quite similar and could be considered as good alternatives for the MIR technique.

Finally, the current screening techniques (colour tests and MIR spectroscopy) were evaluated for the detection of cocaine in complex smuggling samples. Detection of cocaine was only possible after an extraction step prior to screening analysis.

It can be concluded that spectroscopic techniques combined with chemometric methods are an important added value for initial screening of cocaine. Moreover, an estimation of the purity is possible without wet chemistry. The obtained knowledge of this work can be applied for the detection of other illicit drugs, such as heroin and amphetamines.

Frederik Van de Perre​

• 9 March 2020
  
•  Supervisors: Herwig Leirs, Erik Verheyen and Steven Dessein
• Department of Biology

Abstract

Although the relationship between tree diversity and carbon stocks is generally positive, this relationship remains unclear for consumers or decomposers. We assessed this relationship for multiple trophic levels across the tree of life (10 organismal groups, 3 kingdoms) in lowland rainforests of the Congo Basin. Comparisons across regrowth and old-growth forests evinced the positive relationship that was already described for trees, but showed no such relationship for most other organismal groups. Moreover, our results demonstrated that differences in species composition between forests increases with difference in aboveground carbon stock. These variable associations across the tree of life contradict the implicitly accepted assumption that maximum co-benefits to biodiversity are associated with conservation of forests with the highest carbon storage. Moreover, we find that extent (both spatial and ecological) and grain are the most important determinants of the correlation between biodiversity and carbon stock.

The present Congo Basin forest fauna has shown to be resilient enough to recover from past climatic changes that dramatically affected the extent and characteristics of these lowland forests. However, the current most important threats for the Central African forest are rapidly advancing rates of anthropogenic deforestation, climate change, and defaunation. As the recovery of the diversity of disturbed areas (whether disturbed recently or in the past) depends on the influx from specialized forest species from undisturbed areas, protecting old-growth forests and forest refugia should be our highest priority.

Constance Fastré​

• 9 March 2020
  
• Supervisors: Erik Matthysen and Diederik Strubbe
• Department of Biology

Abstract

Effective management of protected areas is necessary to ensure they deliver socio-economic benefits to local communities while conserving the biodiversity they contain.
In this thesis, we use targeted monitoring and remote sensing data combined with modeling tools to generate scientifically-based management recommendations designed to ensure the conservation of biodiversity and the delivery of ecosystem services in the Tunari National Park (TNP) in Bolivia. More specifically, we aimed to (1) investigate which characteristics of the remaining Polylepis fragments of the Southern Slope of the TNP are associated with bird species richness and the presence of species of conservation concern, (2) study habitat selection patterns for the most common forest-dependent bird species occurring in a mosaic landscape made of Polylepis fragments, agricultural fields and exotic plantations on the Southern Slope of the TNP, (3) identify, using species distribution models, the areas of highest priority for the conservation of the avifauna occurring in Polylepis forests of the Southern Slope and especially for conservation concern species and (4) use the conservation planning software Marxan with Zones to generate optimal land use plans that maximize the conservation of several bird species, including species of conservation concern, while minimizing opportunity cost for the local communities on the Southern Slope of the TNP. We then use these plans to explore the potential trade-offs between biodiversity conservation and the delivery of water-related ecosystem services, a limited resource in the area and important source of conflicts. Finally, we formulate scientifically-based management recommendations for the conservation of the avifauna of the Polylepis remnants in the TNP and especially on the Southern Slope.

We confirm the importance of the Polylepis patches of the Tunari National Park and especially of its Southern Slope to support rare and/or threatened Andean species. We conclude that, while it is crucial to protect the biodiversity-rich Polylepis patches, managing the entire landscape in which these patches occur is necessary to meet conflicting conservation and socio-economic demands for the Southern Slope of the TNP. Therefore, conservation management should focus on (1) conserving and/or restoring existing Polylepis fragments, (2) establishing a reforestation scheme and (3) promoting agroforestry and silvopastoralism. We make spatially-explicit recommendations of where to prioritize conservation, restoration and reforestation and promote agroforestry and pastoralism to best support biodiversity, ecosystem services delivery and local livelihoods in a tropical protected area.

Ali Pilehvar​

• 28 February 2020
  
• Supervisors: Ronny Blust and Raewyn Town
• Department of Biology
  

Abstract

Freshwater ecosystems are under threat from the effects of multiple stressors. However, conventional aquatic toxicology studies on metal ions often utilize tests in which organisms are exposed to individual chemicals under constant and favourable experimental conditions. Therefore, the objective of the first part of this doctoral thesis was to evaluate the effect of metal ion toxicity and its interaction with environmental stressors (temperature and ionic strength of the aquatic medium) on the zebrafish (Danio rerio) as a prominent vertebrate model organism. To this end, adult zebrafish were exposed to copper (Cu(II)) and cadmium (Cd(II)) in single or binary exposures in different water hardness treatments or thermal regimes. Our results demonstrated the determinative impact of environmental stress on the toxicity of metal ions to zebrafish. The findings highlight the importance of considering the effect of environmental conditions in designing standard toxicology tests to derive water quality guidelines that are protective for environmentally realistic conditions. In addition, irrespective of the environmental conditions, zebrafish were found to be more sensitive to Cu(II) than to Cd(II) on the basis of single metal ion exposures. Furthermore, mixture exposures revealed a robust synergistic toxic effect of Cu(II) and Cd(II) on zebrafish at the mortality level. Finally, we have observed a significant decrease in whole body Na+ level of dead fish in comparison to surviving fish, irrespective of the exposure conditions; such an effect was not observed for the other major cations (K+, Mg2+, Ca2+). Accordingly, the ability to maintain Na+ homeostasis was identified as a crucial factor for survival under the applied multi-stress conditions.

In the second part of this thesis, we have evaluated some of the major behavioural traits of zebrafish and the effect of Cu(II) exposure on these responses by utilizing two behavioural assays, namely the novel tank diving test and T-maze assay. Moreover, we have also assessed the effect of Cu(II) exposure on chemical communication patterns, specifically the alarm substance response of zebrafish. Our findings from behavioural approaches, suggested that the behavioural toxicity tests, if properly designed, can be utilized in conjunction with classical toxicology endpoints (e.g. mortality, cellular and biochemical changes and growth rate) to add to a weight of evidence interpretation.. Overall, the present thesis provides an insight into the effect of metal ion toxicity on the behaviour of zebrafish in the context of multi-stressor scenarios.

Esteban Municio Hernández 

• 25 February 2020
  
• Supervisors: Steven Latré and Johann Márquez Barja
• Department of Computer Science

Abstract

Current Industrial Internet of Things requires high-reliability, guaranteed performance and low-power consumption in order to allow mission-critical infrastructures monitoring and control in harsh environments with a battery lifetime of years. 6TiSCH is a promising Industrial IoT technology that aims to provide these requirements by combining the TSCH mode of the IEEE 802.15.4e standard with an IPv6-enabled upper stack that connects the IoT network to the Internet.

However Industrial IoT networks and also 6TiSCH networks, are still facing major challenges. First, the current exponential growth of the IoT is also being experienced in the Industrial IoT. This calls for more scalable solutions that can cope with current and future demand. Second, Industrial IoT requires also to be flexible and programmable in order to be further tailored to the actual dynamic industrial automation needs.

These challenges currently remain as open questions. On one hand, intra-domain scalability largely depends on how network resources are shared and distributed. However, as we will later study in this book, limitations may appear when 6TiSCH networks scale up. On the other hand, flexibility and programmability are currently absent in 6TiSCH networks. In 6TiSCH, distributed protocols make static decisions over the routing and scheduling, and configuring the network dynamically is problematic. Alternatively, the proposed centralized SDN-based approaches provide operators with the required flexibility, but seem to be prone to high control overhead, and inherently, to be low scalable. Additionally, current Industrial IoT solutions are usually isolated in network silos, which hinders a complete end-to-end control in a multi-domain hierarchical architecture.

This PhD book addresses these research questions. It first studies intra-domain scalability in 6TiSCH networks, from a theoretical point of view and through extensive simulations. Then, it identifies the existing scalability problems and proposes a new scheduling mechanism in order to try to overcome them. Secondly, it proposes an innovative technique to flexibly control Industrial IoT networks (in our case 6TiSCH) in an efficient but yet scalable manner. Subsequently, we integrate it in a open-source SDN framework to support multi-domain scalable end-to-end control in Industrial IoT networks.

Finally, this PhD book also includes two complementary studies on 6TiSCH. First, we study the open-source simulator for 6TiSCH that allows benchmarking and fast-prototyping. Secondly, in order to further discuss the potential of Industrial IoT networks, this PhD book also includes a report on a cycling use case that leverages 6TiSCH to monitor athletes using a long-range multi-hop highly dynamic network.​

Nicolas De Neuter

• 21 February 2020
  
• Supervisors: Kris Laukens and Arvid Suls

Abstract 

Our body has developed several defense mechanisms to remain in a healthy state. Collectively, these mechanisms are referred to as our immune system. This system can be largely decomposed into two major components: the innate immune system and the adaptive immune system. The importance of understanding the immune system cannot be overstated with regards to human health as it is implicated in infectious diseases, in cancer and in autoimmune disease and understanding it is instrumental to many of our solutions to these health threats. However, due to the complexity of this system, it is far from trivial to understand its intricacies and to generate new insights. During the past decades, computer algorithms have been developed to allow computers to derive patterns and learn from data without any human assistance. Specifically, the field of machine learning aims to develop algorithms that can learn and generalize from data without human intervention. These algorithms are able to discern complex patterns from data at a level that is impossible for humans. In this thesis, we aimed to apply several of such machine learning methods to tackle immunological or immunology-related questions. We were able to demonstrate the possibility of predicting which T cell receptors are capable of binding an epitope within the context of two HIV-derived, HLA-B*08 restricted epitopes. In addition, we show that it is possible to predict the cytomegalovirus serostatus based on the presence of specific T cell receptor sequences present on CD4+ memory T cells. Also for the cytomegalovirus virus, we found several risk factors for the reactivation of the virus in kidney transplant patients and created a classification model to predict this reactivation. Finally, we were able to relate early changes in gene expression to long term vaccination outcomes for both a hepatitis B vaccine and a measles-mumps-rubella booster vaccine. Overall, we show that machine learning methods are able to generate new immunological insights and that it is possible to create sensible, high-performing models that can support both investigators and clinicians working in immunology-related fields.

Saeid Pourbabak​

• 10 February 2020
  
• Supervisors: Nick Schryvers and Bert Verlinden
• Department of Physics 

Abstract

The main properties of Ni–Ti alloys, i.e., shape memory and superelasticity, are used in engineering applications usually through a cyclic shape recovery during thermal or strain cycling, respectively. As these properties originate from the martensitic transformation, the functional stability of the material depends on the reversibility of this martensitic transformation. Therefore, the reversibility of martensitic transformation for Ni–Ti material under thermal cycling was investigated for bulk and micro–wire Ni–Ti.

In the first part of this work the effect of low temperature thermal cycling combined with room and elevated temperature aging on the martensitic transformation of some bulk and micro–wire Ni–Ti samples was studied. The cluster model was used to interpret strong structured diffuse intensities condensed in specific periodic loci in selected area electron diffraction patterns which revealed the formation of micro–domains in the shape of needle clusters of pure Ni atoms. Quantitative comparison between samples with and without a differential scanning calorimetry cycle revealed that the more DSC cycles a sample has received, the more condensed the diffuse intensity becomes which is expected to be caused by longer Ni clusters and enhancement of short–range ordering. A novel method to use a conventional twin–jet electropolishing apparatus for thin wires was also introduced.

In the second part of this work in–situ transmission electron microscopy tensile tests on nano–scale single crystals and polycrystalline Ni–Ti specimens was performed. The formation of stress–induced martensite was observed and stress–strain curves were plotted based on the obtained mechanical data. The stress plateau height shows an increase by decreasing specimen thickness but remains independent of the grain size since the latter is, on average, larger than the specimen thickness. Martensitic transformation starts at edges of the specimen for the single crystal and on the edges and grain boundaries for the polycrystalline specimen. When a martensite plate approaches a grain boundary in the polycrystalline specimen, it provokes the transformation in the neighboring grain at the other side of the grain boundary. After releasing the load, depending on the totally induced strain, some residual martensite remains in the specimen indicating the existence of induced plasticity in the martensite at large strains.

Vera Andrejchenko 

• 4 February 2020
  
• Supervisor: Paul Scheunders
• Department of Physics

Abstract

Hyperspectral images (HSI) contain rich spectral information, by capturing a wide range of the electromagnetic radiation. Even though this is advantageous and provides the potential of a very detailed characterization of materials, it comes with a number of challenges. Information in HSI is highly redundant due to the high number of spectral bands involved. Another source of redundancy is the large spectral variability between spectral reflectances of the same material. Spatial redundancy is introduced by mixed pixels, containing more than one materials and high correlations between neighboring spectra.

The general research objective in this work is to address these redundancies of the HSI, by employing spectral unmixing techniques. These methods have the capability of describing highly redundant spectra as (linear or nonlinear) mixtures of a very limited amount of pure materials. In this way, they capture the structure of the low-dimensional subspace in which this high redundant data lives.

In this work, we mainly address the redundancy in HSI by developing model-based techniques which employ prior information on the parameters, derived from the data. HSI are high-dimensional but intrinsically lie on a lower-dimensional subspace which will be unraveled by exploiting the proposed priors. Moreover, these low-dimensional representations are employed in a decision fusion framework to improve the classification performance of HSI.

The thesis is organized in such a way that there are three clear subdivisions. The first contains the developed spectral unmixing method with the local low rank and inter-group sparsity prior imposed on its abundance parameters simultaneously. The second embraces the model based method which considers nonlinearities, i.e., multiple reflections and the shadowing at the same time in addition to the estimation of the abundance parameters. And the last consists of two decision fusion frameworks that incorporate the sparse low dimensional feature sets to enhance the HSI classification when limited training data is available. These are naturally preceded by an a) introduction to the hyperspectral imaging area and its active research areas and b) prerequisite key elements required for the forthcoming chapters.

Ana Castanheiro​

• 21 January 2020
• Supervisors: Roeland Samson and Karolien De Wael
• Department of Bioscience Engineering

Abstract

Particulate matter (PM), the collection of fine respirable particles suspended in the air, is the greatest health-threatening pollutant. To properly monitor the high spatial variability of PM is not possible at the moment, particularly within dynamic, mixed-source urban environments, as conventional air quality monitoring networks have limited spatial resolution and often lack information on PM composition. To use urban vegetation as a bio-indicator for atmospheric PM (biomonitoring), as it provides a natural surface for deposition of particulates, may help filling out those gaps. Iron and other metals are of particular interest within PM. Therefore, biomagnetic monitoring of leaves has been extensively used as a rapid and cost-effective tool to assess urban PM exposure.

Leaves exposed to atmospheric conditions leads to the invariable accumulation of magnetic particles, which are ubiquitously present in PM but allow distinguishing between low and high pollution levels. Throughout this PhD research, the applicability of biomagnetic monitoring as a fingerprinting tool for atmospheric PM was investigated across different environments and source types, using a combination of analytical techniques. Overall this consisted in 1) characterizing major urban source types of PM by means of magnetic, chemical and microscopic techniques, to obtain source-specific magnetic and physicochemical PM fingerprints, 2) investigating potential associations source-specific magnetic and physicochemical PM fingerprints, and 3) evaluating leaf biomagnetic monitoring as a strategy to infer atmospheric PM levels and characteristics of major PM contributing sources.
Leaf-deposited PM from environments mainly exposed to different pollution sources were first investigated microscopically for their morphological characteristics and elemental composition. In a second stage, dedicated particle-analysis was combined with leaf magnetic investigation to discriminate between different source types (small scale) and between urban streets and parks across 20 European cities (large scale). To gain more insight on the leaf accumulation of PM, a multi-approach leaf monitoring campaign was also conducted using plant species with distinct macro- and micro-morphology.

The information gathered on the analytical techniques used and their potential for source characterization were finally applied in a large PM fingerprinting (PMF) project. The PMF project comprised the monitoring and fingerprinting of five different source locations (road, railway and shipping traffic, industry and a background site) based on air-pumped samples (conventional monitoring) and exposed plant leaves, to obtain source-specific signatures and evaluate the applicability of leaf magnetic monitoring of PM. The health-risk potential of the studied source types was also explored in terms of human lung pro-inflammatory response.

Carolien Callaert​

• 20 January 2020
  
• Supervisors: Joke Hadermann and Dirk Lamoen
• Department of Physics

Abstract

Fabio Bottari​

• 20 January 2020
  
• Supervisors: Karolien De Wael and Ronny Blust
• Department of Chemistry 

Abstract

In the broad context of food and environmental safety, the development of selective and sensitive analytical tools for the detection of β-lactam antibiotics in milk down to their Maximum Residues Limits (MRL), is still an open challenge. To address this need, the design of new bio(mimetic) electrochemical sensors was investigated in the present thesis. These sensors are based on the intrinsic electrochemistry of β-lactam antibiotics, taking advantages of the characteristic electrochemical fingerprints of the core structures and redox active side chain groups. Once verified the applicability of a direct electrochemical detection, different sensor configurations were tested mainly focusing on:

- the selection and validation of aptamers to be used as bioreceptors in the development of β-lactam biosensors;
- the design of biomimetic receptors, particularly molecularly imprinted polymers, and other synthetic electrode modifiers compatible with a direct detection strategy.

Lastly, the research activity was directed towards milk sample analysis following two parallel routes: the development of a pre-treatment protocol for raw milk, based on solvent addition and the study of β-lactam antibiotics electrochemistry in undiluted raw milk.

Jeroen van Dyck​

• 16 January 2020
  
• Supervisors: Frank Sobott and Dirk Snyders
• Department of Chemistry

Abstract

Native mass spectrometry has shown over the past years to be a very useful tool in the investigation of membrane proteins and pores, which provides additional information next to the conventional structural techniques. This information concerns for example the stoichiometry of complexes and structural information. In recent years native mass spectrometry has shown to be very useful for the investigation of large noncovalent, mainly globular structures. In this thesis different projects are presented and discussed showing the versatility of native mass spectrometry; including membrane associated proteins and nanopores build from custom designed DNA strands. To investigate each of the different projects ion mobility and mass spectrometric techniques were used. This also includes the methods of sample preparation required to transfer the samples into the gas phase without disturbing the complexes formed significantly.

The BAX protein revealed to behave differently, when different detergents were present in solution above the critical micelle concentration or when binding the directly activating molecule BAM-7. Ion mobility and mass spectrometry show that it forms oligomers and conformational changes.

Native mass spectrometry turned out to be very useful in the investigation of lipid interactions with membrane proteins. This was shown by MgtA membrane protein revealing a very specific binding of lipids. Native MS has shown to be very useful for observing of specifically bound lipids even when high concentrations of other lipid were present.

DNA origami is a term used for artificial designed nano-structures from DNA building blocks. Within this thesis the formation of such a DNA nano-structure was investigated concerning the formation of a DNA nanopore. This pore is formed from different DNA strands which should only fit together in one possible manner. Using ion mobility mass spectrometry it was shown that the salt concentration, in the form of ammonium acetate, has a profound influence on the actual form of the hexameric pore.

​Felipe Sempértegui Maldonado Pires

• 14 January 2020
  
• Supervisors: Joris Dirckx and Steve Vanlanduit
• Department of Physics

Abstract

The middle ear system functions as an acoustic impedance transformer, from which energy is transmitted from the air medium to the fluid present in the inner ear. The first transmission of mechanical energy is located between the eardrum and the first of the ossicles. Moreover, it was assumed that most of the energy is efficiently transmitted from the eardrum’s tissues to their attachment to the malleus. No more specific information could be found in literature and it was the aim of this Ph.D. research to quantify the energy flow on human eardrums.

The starting point of this work was based on a well-known technique called the energy flow analysis and it is based on a combination of measured deformations and the stiffness of a sample. These studies were mainly focused on analyzing plate-like structures in the past, which are by no means comparable to the complex morphology of eardrums. Furthermore, the stiffness of individual eardrums may also vary considerably. Therefore, the Ph.D. candidate had 2 challenges to face: expand the current energy flow analysis suited for simple geometries, in order to estimate results from complex ones & develop a method, from which stiffnesses could be identified via measurements of individual eardrums.

The first issue was addressed by developing a method that discretizes the continuous & irregular surface of the eardrum to a collection of individual elements. This choice has shown itself to be efficient and it could provide precise energy transmission data via measurements of an irregular sample’s deformation. Later, the second challenge was overcome by refining the so-called the Virtual Fields Method. The adapted algorithm was validated with synthetic data and the errors of the retrieved stiffnesses were below 3%. Since the stiffness and the energy flow assessments were completed for irregular geometries, it was decided to use the algorithms for the case of an eardrum. Thankfully, after recording some experimental data of the refereed membrane, reasonable stiffness values were estimated and were within the range of what researchers published in the past. By combining the stiffness with the displacement fields of a single eardrum, the energy flow could be finally visualized and the results were in accordance with our expectations. Due to this achievement, not just the main aim of this research was completed, but 2 well-established engineering techniques were refined, so they could be applied on more complex geometries.

Charlotte Vets​

• 10 January 2020
  
• Supervisor: Erik Neyts

Abstract

Carbon nanomaterials have shown tremendous promise for various types of electronic devices. In this work, we studied carbon nanotubes (CNTs) and carbyne. Both come in various geometries, on which their electric properties are dependent. The geometry is defined during the making, or growth, of the materials. To really use them in electronic devices, it is thus crucial to control their growth process. This is currently still a challenge. This work therefore tries to elucidate the influence of some of the mechanisms important during growth, with a view to gaining more insight into how to tune the growth towards materials with specific electric properties. Growth mechanisms of CNTs and carbyne were explored using computer simulations, more specifically density functional theory (DFT) and molecular dynamics (MD).

CNTs are hexagonal networks of carbon atoms, rolled up into a tube. Their growth requires a catalyst. The edge structure of the hexagonal network, i.e. its chirality, then defines the carbon nanotube’s electric properties. Most often used catalysts are Ni, Fe or Co nanoparticles. However according to experiments, bimetallic catalysts are promising for chirality control. Therefore, we did a combined DFT and Born-Oppenheimer MD study on stabilities of NiFe, NiGa and FeGa nanoparticles.

Both thermodynamic and kinetic mechanisms are at play in carbon nanotube growth. The thermodynamic mechanism was studied through the adhesion energy between carbon nanotubes with various chiralities and Ni, Fe, and FeNi nanoparticles. For this we employed DFT calculations. We investigated whether the carbon nanotube-catalyst adhesion energy can be tuned using bimetallic catalysts in various concentrations, in order to enable chirality selective growth.

Of the several kinetic mechanisms, defect healing was chosen, as it highly influences the chirality formation. Defect healing was studied using classical MD. We addressed the influence of the defect – metal catalyst contact on defect healing. We studied stabilities of CNTs with 5-7 defects, Stone-Wales defects and vacancies, and evaluated those results on Ni nanoparticles.

Next to CNTs, we also studied carbyne. Carbyne is a linear carbon chain. Despite its successful synthesis within double walled CNTs, the carbyne growth mechanism is still elusive. However, it is clear that the growth mechanism is dependent on the catalyst and the feedstock. We studied the nucleation and growth of various carbon chains in a Ni-containing double walled CNT, using carbon and hydrocarbon precursors.