Abstract: Nanoscience and nanotechnologies are of immense importance across many fields of science and for numerous practical applications. In this context, scanning transmission electron microscopy (STEM) and 4D-STEM are among the most powerful characterization methods at the atomic scale. Annular dark-field (ADF)-STEM can be used to quantify atomic structures in 3D by counting atoms based on a single projection image. In 4D-STEM a full diffraction pattern is recorded at each scan step, which enables more dose efficient imaging and the utilization of various advanced imaging modalities, which can however be complex and slow. Both, STEM and 4D-STEM suffer from noise and distortions. In the first section of this work the most important of these distortions are discussed and it is shown how image restoration with a dedicated convolutional neural network (CNN) can be beneficial for atomic structure quantifications in ADF-STEM. In the second part, a new 4D-STEM imaging method real-time-integrated-centre-of-mass (riCOM) is introduced, which is a very dose-efficient and fast algorithm that enables unprecedented live-imaging capabilities for 4D-STEM. It is based on the integrated centre-of-mass approach, but is reformulated with variable integration ranges and optional filters, which allows for a tunable contrast transfer function. This enables the imaging of light and heavy elements simultaneously at very low doses. In the third part another new 4D-STEM method, coined AIRPI (AI-assisted rapid phase imaging) is introduced, which uses a CNN to retrieve a patch of the specimen's phase image for each scan position, based on the diffraction patterns in the probe's immediate surroundings. This allows also live imaging in principle and surpasses comparable state-of-the-art algorithms in terms of resolution also at low doses. Different atomic columns can be reliably distinguished over a wide range of atomic numbers, enabling a very good image interpretability. Further, AIRPI can recover low frequency image components, which preserves thickness information. This is a unique and important feature which could make quantitative 4D-STEM feasible.

Abstract: This dissertation focuses on the materials and techniques used by the Brussels artists Michael Sweerts (1618-1664) and Michaelina Wautier (1614-1689). It tries to answer the following research questions: 1.Did 17th-century Brussels-based artists use idiosyncratic painting materials and techniques, and can their work be distinguished from paintings created in other artistic centers based on these materials and techniques? 2.How did Sweerts and Wautier influence each other’s work? In order to answer the research questions, a selection of relevant and representative paintings from Sweerts’ and Wautier’s oeuvre has been studied with MA-XRF scanning and microscopic examination. The results are discussed in volume 2 of this dissertation, and form the basis upon which the chapters in volume 1 are built. MA-XRF scanning is a non-destructive imaging technique based on the technique of X-ray fluorescence (XRF). The technique allows for the collection of elemental information about the materials present in a work of art. The work presented in this dissertation has shed more light on the working methods of Sweerts and Wautier. It has shown they both showed an openness and willingness to continue learning and developing as an artist. Wautier did not feel constrained to work only in one genre and apparently carried on developing her skills throughout her career, as evidenced by Flower Garland with Butterfly. She might have trained with a specialized artist to master their idiosyncratic working methods and this diverse genre. This suggests that Wautier had an interest in art education. Sweerts too had a passion for art education: he was involved in the drawing academy of Camillo Pamphilj in Rome, and later established his own academy for life drawing in Brussels. Sweerts’ work clearly shows the influence of his Italian contemporaries. Whilst living in Rome, he adapted his working methods to local customs such as working on brightly colored grounds. Wautier’s oeuvre shows that she was open to explore different genres and that she took inspiration from her contemporaries, including Theodoor van Loon and Daniël Seghers. In doing so, she furthered the development of different genres as she added original elements to her compositions. It is the conclusion of this dissertation that Michaelina Wautier can thus be considered one of the innovative artists of mid-17th-century Flemish painting, who inspired other artists in the Low Countries, including Michael Sweerts.

Abstract: Electromagnetic design problems involve optimizing multiple parameters that are nonlinearly related to objective functions. Traditional optimization techniques require significant computational resources that grow exponentially as the problem size increases. Therefore, a method that can produce good results with moderate memory and computational resources is desirable. Bioinspired optimization methods, such as particle swarm optimization (PSO), are known for their computational efficiency and are commonly used in various scientific and technological fields. In this article we explore the potential of advanced PSO-based algorithms to tackle challenging electromagnetic design and analysis problems faced in real-life applications. It provides a detailed comparison between conventional PSO and its quantum-inspired version regarding accuracy and computational costs. Additionally, theoretical insights on convergence issues and sensitivity analysis on parameters influencing the stochastic process are reported. The utilization of a novel quantum PSO-based algorithm in advanced scenarios, such as reconfigurable and shaped lens antenna synthesis, is illustrated. The hybrid modeling approach, based on the unified geometrical description enabled by the Gielis Transformation, is applied in combination with a suitable quantum PSO-based algorithm, along with a geometrical tube tracing and physical optics technique for solving the inverse problem aimed at identifying the geometrical parameters that yield optimal antenna performance.

Abstract: Our scientific and technological worldviews are largely dominated by the concepts of entropy and complexity. Originating in 19th-century thermodynamics, the concept of entropy merged with information in the last century, leading to definitions of entropy and complexity by Kolmogorov, Shannon and others. In its simplest form, this worldview is an application of the normal rules of arithmetic. In this worldview, when tossing a coin, a million heads or tails in a row is theoretically possible, but impossible in practice and in real life. On this basis, the impossible (in the binary case, the outermost entries of Pascal's triangle xn and yn for large values of n) can be safely neglected, and one can concentrate fully on what is common and what conforms to the law of large numbers, in fields ranging from physics to sociology and everything in between. However, in recent decades it has been shown that what is most improbable tends to be the rule in nature. Indeed, if one combines the outermost entries xn and yn with the normal rules of arithmetic, either addition or multiplication, one obtains Lamé curves and power laws respectively. In this article, some of these correspondences are highlighted, leading to a double conclusion. First, Gabriel Lamé's geometric footprint in mathematics and the sciences is enormous. Second, conic sections are at the core once more. Whereas mathematics so far has been exclusively the language of patterns in the sciences, the door is opened for mathematics to also become the language of the individual. The probabilistic worldview and Lamé's footprint can be seen as dual methods. In this context, it is to be expected that the notions of information, complexity, simplicity and redundancy benefit from this different viewpoint.

Abstract: The foodborne pathogens and microorganisms have played a prevalent role in the ebb and flow of the economy worldwide. The increasing population has strained the food processing industry to produce food in large quantity, which in turn has affected the quality of food. To curb this issue, there is immense pressure to produce and maintain quality food within a short time frame. Hence, high throughput technology is used to determine and timely assess the safety and hygiene of food. Further, the revolution of the food industry has also seen an upsurge of new pathogens and microorganisms, thereby increasing the risk of exposure towards rarest diseases to a larger population. This chapter sheds light on the different types of foodborne pathogens affecting the food industry and its social impact. It further emphasizes the safety measures to be taken on the prevention of the disease from the farm to the processing industries and in turn to the household.

Abstract: Illicit drugs are harmful substances, posing a threat to the health and safety of society. Policies, such as supply reduction and harm reduction, are in place to combat the illicit drug problem. Science can play a substantial role in this fight, by providing tools that enable these policies to be successfully enforced. One example are on-site detection tools, i.e. sensors that allow the on-site identification of an illicit drug in a sample of interest. Several technologies, such as color tests and portable spectroscopic techniques, are currently employed for this goal. Although these are valuable techniques, there is an opportunity for voltammetry, an electrochemical technique, to make an impactful addition to this repertoire of on-site detection tools. Despite its attractive features (low-cost, portable, short analysis time, indifference to color,...), voltammetric illicit drug sensor have failed to make an impact in real scenarios. The work outlined in this PhD thesis aims to change this by bringing the technology from the lab to the field. Strategic choices, fueled by feedback from end-users, were made to further develop those specific aspects of the technology that previously haltered the technology to fulfill its potential. A detection algorithm was introduced that converts the voltammetric output into a clear-cut interpretation thereof, opening up the technology to end-users without prior knowledge of the technology. A sensor that allows qualitative and quantitative detection of the psychoactive drug MDMA was introduced, and importantly, validated on a large set of 212 confiscated samples. A state-of-the art mobile application and adequate sampling methodology were developed, alongside other, often more practical studies and product developments, to evolve the technology into a product that truly creates value for end-users. Important steps towards multidrug detection were made with a festival sensor and a flowchart based on visual appearance that ties together a variety of voltammetric single sensors into a single multidrug sensing approach. Last but not least, multiple valorization aspects were researched, including a market study and an analysis to determine the optimal commercialization strategy. Overall, this PhD thesis has facilitated the transition of the voltammetric illicit drug sensing technology from lab to on-site application. The final application creates value for end-users, and is ready to make an impact in real on-site scenarios.

Abstract: Generalized Möbius-Listing surfaces and bodies generalize Möbius bands, and this research was motivated originally by solutions of boundary value problems. Analogous to cutting of the original Möbius band, for this class of surfaces and bodies, results have been obtained when cutting such bodies or surfaces. In general, cutting leads to interlinked and intertwined different surfaces or bodies, resulting in very complex systems. However, under certain conditions, the result of cutting can be a single surface or body, which reduces complexity considerably. These conditions are based on congruence and rotational symmetry of the resulting cross sections after cutting, and on the knife cutting the origin

Abstract: Delve into the diverse aspects of plant morphology, their responses to global climate change, and the spatiotemporal dynamics of forest productivity. Join us on a journey through the intricate web of plant characteristics and their impact on the environment.

Abstract: Nitrogen (N) is an indispensable building block for all living organisms as well as for pharmaceutical and chemical industry. In a nutshell, N is needed for plants to grow and beings to live and nitrogen fixation (NF) is the process that makes N available for plants as food by converting N2 into a reactive form, such as ammonia (NH3) or nitrogen oxides (NOx), upon reacting with O2 and H2. The aim of this thesis is to elucidate (wet) plasma-based nitrogen fixation with a focus on (1) the role of pulsing in achieving low energy consumption, (2) the role of H2O as a hydrogen source in nitrogen fixation and (3) elucidation of nitrogen fixation pathways in humid air and humid N2 plasma in a combined experimental and computational study. Furthermore, this thesis aims to take into account the knowledge-gaps and challenges identified in the discussion of the state of the art. Specifically, (1) we put our focus on branching out to another way of introducing water into the plasma system, i.e. H2O vapor, (2) we de-couple the problem for pathway elucidation by starting with characterization of the chosen plasma, next a simpler gas mixture and building up from there, (3) we include modelling, though not under wet conditions and (4) we focus on also analyzing species and performance outside liquid H2O. Firstly, based on the reaction analysis of a validated quasi-1D model, we can conclude that pulsing is indeed the key factor for energy-efficient NOx- formation, due to the strong temperature drop it causes. Secondly, the thesis shows that added H2O vapor, and not liquid H2O, is the main source of H for NH3 generation. Related to this, we discuss how the selectivity of plasma-based NF in humid air and humid N2 can be controlled by changing the humidity in the feed gas. Interestingly, NH3 production can be achieved in both N2 and air plasmas using H2O as a H source. Lastly, we identified a significant loss mechanism for NH3 and HNO2 that occurs in systems where these species are synthesized simultaneously, i.e. downstream from the plasma, HNO2 reacts with NH3 to form NH4NO2, which decomposes into N2 and H2O. This reduces the effective NF when not properly addressed, and should therefore be considered in future works aimed at optimizing plasma-based NF. In conclusion, this thesis adds further to the current state of the art of plasma-based NF both in the presence of H2O and in dry systems.

Abstract: As natural gas prices proved to be very volatile, sustainable alternatives are highly needed. Water-derived H2 was revealed as a promising substitute, allowing to produce a green energy carrier with a minimum of harmful emissions. Direct splitting of earth-abundant seawater provides an eco-friendly route for the production of clean H2, but is hampered by selectivity and stability issues due to the presence of salts.Photocatalytic seawater splitting is particularly promising for this purpose, as it seems less affected by adversary seawater effects and might rely on free and renewable solar power. Unfortunately, the benchmark photocatalyst, TiO2, still suffers from its low solar light activity. It is only actived upon illumination with energetic ultraviolet light (<5% of the sunlight). In order to broaden the activity window to the visible light, the concept of the 'plasmonic rainbow' was explored. Here, TiO2 is modified with various gold-silver composites that possess the unique optical phenomenon of Surface Plasmon Resonance (SPR). This phenomenon enables the absorption of light at very specific wavelengths, depending on the metal type, size, shape and dielectric environment. The light energy might then be converted into hot carriers, strong local electromagnetic fields and/or heat. By combining multiple composites with various sizes and compositions, a broadband absorption could be obtained, resulting in significantly enhanced activity in photocatalytic model reactions under simulated sunlight. The major disadvantage of these plasmonic nanoparticles is their tendency to oxidize and deactivate. To overcome this, polymer shell stabilization strategies were found to be effective to protect the metal cores. Both conductive and non-conductive polymers were studied. For the former, a mix-and-wait strategy generating polyaniline shells of 2-5 nm was used, whille the latter was based on a Layer-by-Layer approach, allowing (sub) nanometer thickness control. For the actual H2 production experiments, the plasmonic loading was optimized in a pure water:methanol scavenger (7:1) mixture and initially the stabilization strategies proofed to be effective for simulated seawater (0.5M NaCl), even after 2 years. However, in real seawater, the activity decreased drastically due to aggregation of the photocatalyst in the presence of multivalent cations. Finally, facile immobilization strategies using 3D printing showed to be able to yield stable, solar active photocatalyst for real seawater splitting.

Abstract: Plasma-based CO2 conversion is worldwide gaining increasing interest. The aim of this work is to find potential pathways to improve the energy efficiency of plasma-based CO2 conversion beyond what is feasible for thermal chemistry. To do so, we use a combination of modeling and experiments to better understand the underlying mechanisms of CO2 conversion, ranging from non-thermal to thermal equilibrium conditions. Zero-dimensional (0D) chemical kinetics modelling, describing the detailed plasma chemistry, is developed to explore the vibrational kinetics of CO2, as the latter is known to play a crucial role in the energy efficient CO2 conversion. The 0D model is successfully validated against pulsed CO2 glow discharge experiments, enabling the reconstruction of the complex dynamics underlying gas heating in a pure CO2 discharge, paving the way towards the study of gas heating in more complex gas mixtures, such as CO2 plasmas with high dissociation degrees. Energy-efficient, plasma-based CO2 conversion can also be obtained upon the addition of a reactive carbon bed in the post-discharge region. The reaction between solid carbon and O2 to form CO allows to both reduce the separation costs and increase the selectivity towards CO, thus, increasing the energy efficiency of the overall conversion process. In this regard, a novel 0D model to infer the mechanism underlying the performance of the carbon bed over time is developed. The model outcome indicates that gas temperature and oxygen complexes formed at the surface of solid carbon play a fundamental and interdependent role. These findings open the way towards further optimization of the coupling between plasma and carbon bed. Experimentally, it has been demonstrated that “warm” plasmas (e.g. microwave or gliding arc plasmas) can yield very high energy efficiency for CO2 conversion, but typically only at reduced pressure. For industrial application, it will be important to realize such good energy efficiency at atmospheric pressure as well. However, recent experiments illustrate that the microwave plasma at atmospheric pressure is too close to thermal conditions to achieve a high energy efficiency. Hence, we use a comprehensive set of advanced diagnostics to characterize the plasma and the reactor performance, focusing on CO2 and CO2/CH4 microwave discharges. The results lead to a deeper understanding of the mechanism of power concentration with increasing pressure, typical of plasmas in most gases, which is of great importance for model validation and understanding of reactor performance.

Abstract: Climate change is an ever-increasing threat to our planet, with the urbanisation of previously open spaces exacerbating the issue. The loss of open space leads to a decrease in ecosystem services, which negatively impacts the liveability in urbanised environments, and beyond. The incorporation of green infrastructure (GI) into the built environment is acknowledged as an effective and multi-functional measure to make our living environment resilient and future-proof. Notwithstanding that the local level is essential for effective GI implementation, research into local decision making is very limited. This thesis therefore aims to address two significant gaps in the implementation of GI. The first objective is to bridge the science-policy gap by integrating scientific knowledge on ES and GI into local decision-making processes. In this context, the policy dimension within this thesis involves the process of translating overarching policy objectives into practical actions within local decision-making practices, abstracting from the intricacies of higher-level policymaking itself. The second objective focuses on the people-policy gap, which involves understanding stakeholders' perspectives and priorities regarding GI and its associated ecosystem services. Through stated preference valuation techniques (discrete choice experiments and a best-worst scaling experiment), multi-stakeholder GI perceptions are gathered. Finally, a novel tool to GI valuation is developed, acknowledging the different stakeholder views and building on the insights of collaborative tool development and design. By gaining insights into the demand, prioritisation, and provision of GI from the two most important stakeholder groups at the local level (residents and local decision-makers), this research aims to foster effective communication and alignment between public preferences and policy implementation. Ultimately, addressing these gaps will contribute to more informed planning and decision-making practices related to green infrastructure. The overall goal of this thesis is to enhance the uptake of GI knowledge in the local decision-making context, proposing pathways to facilitate and optimise investments in public GI.

Abstract: Nanomaterials are materials that have at least one dimension in the nanometer length scale, which corresponds to a billionth of a meter. When three dimensions are confined to the nanometer scale, these materials are referred to as nanoparticles. These materials are of great interest since they exhibit unique physical and chemical properties that cannot be observed for bulk systems. Due to their unique and often superior properties, nanomaterials have become central in the field of electronics, catalysis, and medicine. Moreover, they are expected to be one of the most promising systems to tackle many challenges that our society is facing, such as reducing the emission of greenhouse gases and finding effective treatments for cancer. The unique properties of nanomaterials are linked to their size, shape, structure, and composition. If one is able to measure the positions of the atoms, their chemical nature, and the bonding between them, it becomes possible to predict the physicochemical properties of nanomaterials. In this manner, the development of novel nanostructures can be triggered. However, the morphology and structure of nanomaterials are highly sensitive to the conditions for relevant applications, such as elevated temperatures or intense light illumination. Furthermore, any small change in the local structure at higher temperatures or pressures may significantly modify their performance. Hence, three-dimensional (3D) characterization of nanomaterials under application-relevant conditions is important in designing them with desired functional properties for specific applications. Among different structural characterization approaches, transmission electron microscopy (TEM) is one of the most efficient and versatile tools to investigate the structure and composition of nanomaterials since it can provide atomically resolved images, which are sensitive to the local 3D structure of the investigated sample. However, TEM only provides two-dimensional (2D) images of the 3D nanoparticle, which may lead to an incomplete understanding of their structure-property relationship. The most known and powerful technique for the 3D characterization of nanomaterials is electron tomography, where the images of a nanostructured material taken from different directions are mathematically combined to retrieve its 3D structure. Although these experiments are already state-of-the-art, 3D characterization by TEM is typically performed under ultra-high vacuum conditions and at room temperature. Such conditions are unfortunately not sufficient to understand transformations during synthesis or applications of nanomaterials. This limitation can be overcome by in situ TEM where external stimuli, such as heat, gas, and liquids, can be controllably introduced inside the TEM using specialized holders. However, there are some technical limitations to successful perform 3D in situ electron tomography experiments. For example, the long acquisition time required to collect a tilt series limits this technique when one wants to observe 3D dynamic changes with atomic resolution. A solution for this problem is the estimation of the 3D structure of nanomaterials from 2D projection images acquired along a single viewing direction. For this purpose, annular dark field scanning TEM (ADF STEM) imaging mode provides a valuable tool for quantitative structural investigation of nanomaterials from single 2D images due to its thickness and mass sensitivity. For quantitative analysis, an ADF STEM image is considered as a 2D array of pixels where relative variation of pixel intensity values is proportional to the total number of atoms and the atomic number of the elements in the sample. By applying advanced statistical approaches to these images, structural information, such as the number or types of atoms, can be retrieved with high accuracy and precision. The outcome can then be used to build a 3D starting model for energy minimization by atomistic simulations, for example, molecular dynamics simulations or the Monte Carlo method. However, this methodology needs to be further evaluated for in situ experiments. This thesis is devoted to presenting robust approaches to accurately define the 3D atomic structure of nanoparticles under application-relevant conditions and understand the mechanism behind the atomic-scale dynamics in nanoparticles in response to environmental stimuli.