Abstract: Exposure to air pollution, such as particulate matter (PM), causes adverse health effects, particularly to the respiratory tract and cardiovascular system. PM is the collective name for all kinds of particles ranging from small particles and liquid droplets, which contain organic compounds, acids and metals, to soil or dust particles. One distinguishes PM10, PM2.5 and PM0.1, which have aerodynamic particle sizes smaller than 10, 2.5 and 0.1 µm, respectively. It is mainly the latter that is the most harmful, as PM0.1 penetrates deep into the respiratory system and carries relatively more toxic substances than the other PM fractions. Over a 15-year period, PM concentrations in European member states have fallen by about 30%. Nevertheless, the World Health Organisation (WHO) air quality guidelines, which became stricter in 2021, are exceeded in most places around the world. Particularly in cities, excessive levels of PM are measured and it is here that PM mitigation should be investigated. For this, the implementation of urban green infrastructure, including trees, shrubs, green roofs and green walls, is being looked at. Plants hinder airflow and remove PM from the air by deposition on their leaves and branches. This process is known as dry deposition. Plants can capture PM very efficiently, due to their complex structure of leaves and branches. Green walls offer significant advantages over other types of urban green infrastructure because they can grow on the huge available wall area and, because they do not hinder air circulation, as we sometimes see with trees. Green walls are believed to have a much greater, untapped potential to reduce PM pollution. However, a literature review showed that we do not know the quantitative impact of green walls and lack the tools and/or general methodology to do so. The objective of this thesis is therefore to develop a method for assessing PM removal by green walls, based on predictive models and based on relevant parameters that are experimentally determined. Computational fluid dynamics (CFD) is a numerical method to simulate airflow in complex environments such as cities. These models can also simulate the vegetation-wind interaction in detail and are interesting tools to assess the effect of green walls on PM concentrations in real environments. It is important to first study the aerodynamic effect of green walls and parameterise it correctly in CFD models. Plants decrease the wind speed and create turbulence through a combination of viscous and form drag, which are determined by the permeability (K) and drag coefficient (Cd), respectively. Wind tunnel experiments were conducted with three commonly found climbers (Hedera helix, Parthenocissus tricuspidata and Parthenocissus quinquefolia) and the variation of leaf area density was investigated for two of them. It was observed that the air resistance depended on plant species, leaf area density and wind speed. The difference between the plant species was assigned to the functional leaf size (FLS), the ratio of the largest circle within the boundaries of the leaf to the total leaf area. FLS is likely associated with other morphological characteristics of plants that, when considered collectively, provide a more comprehensive representation of leaf complexity. The pressure and velocity measurements obtained were used to optimise the permeability and drag coefficient in a CFD model. At wind speeds below 0.6 m s-1, the resistance was mainly determined by viscous drag and a larger leaf size resulted in a higher viscous drag. At wind speeds above 1.5 m s-1, form drag was dominant and the parameterised Cd decreased with increasing wind speed due to the sheltering effect of successive plant elements. The leaf area density had a significant effect on K and Cd and, is therefore an important plant parameters in CFD models. The main conclusion here is that the common practice of using a constant Cd to model the influence of plants on the air flow leads to deviations from reality. Wind tunnels are highly suitable to study the impact of green walls on PM concentration under controlled environmental conditions. For this purpose, a new wind tunnel setup was built and great attention was paid to obtaining a uniform air flow. Thus, based on CFD models, appropriate flow controllers were chosen, consisting of honeycombs and screens with different mesh sizes. New PM generation devices and measuring equipment were installed and set up appropriately. Devices were available for generating and measuring ultrafine dust (<0.1 µm, i.e. PM0.1) and fine dust (<0.3 µm, i.e. PM0.3) consisting of soot particles, and, on the other hand, fine dust with particle sizes smaller than 2.5 (PM2.5) and 10 µm (PM10) consisting of 'Arizona fine test dust'. With the new wind tunnel setup, it was possible to measure the influence of Hedera helix (common ivy), grown in a planter against a climbing aid, on the PM concentration and this was expressed by a collection efficiency, i.e. the difference in concentration in front and behind the plants normalised for the incoming concentration. The collection efficiency of H. helix depended on the particle size of the PM and wind speed. The collection efficiency decreased when the particle size increased from 0.02 to 0.2 µm and increased again for particle sizes above 0.3 µm. The collection efficiency also increased with increasing wind speed, especially for particle sizes > 0.03 µm. On the other hand, relative humidity and the type of PM (soot or dust) did not significantly affect the collection efficiency. The main objective of this study was to obtain an optimised size-resolved deposition model. Dry deposition occurs through several mechanisms, in particular gravity, diffusion, impaction and interception, and the subsequent resuspension of deposited PM back to the environment. The modelling of these mechanisms was described by \citet{Zhang2001} and \citet{Petroff2010}. The data obtained from the wind tunnel experiments allowed validating these deposition models. It was for the first time that deposition of real PM on green walls was studied. The different PM deposition mechanisms were found to be strongly dependent on particle size and wind speed. The models of \citet{Zhang2001} and \citet{Petroff2010} each matched PM concentration measurements for only certain particle sizes. Therefore, a combination of the two models was investigated and the root mean square error was lower by on average 3.5% (PM < 0.03 µm) and 46% (PM > 0.03 µm) compared to the original models at wind speeds greater than 1.5 m s-1. For wind speeds less than 1.5 m s-1, the optimised model did not differ from the original models. The optimised model was able to meet the imposed criteria for air quality models, where a correct model exhibits low deviation from measurements ('normalised mean square error' < 1.5), low bias ('fractional bias' between -0.3 and 0.3) and high R2. In comparison, the R$2$ of the optimised model was 0.57, while that of Zhang et al. (2001) and Petroff et al. (2010) was 0.23 and 0.31, respectively. The optimised model was however characterised by a high scatter, with the fraction of modeled results located within a factor of two of the measurements being lower than 50. A model study with a green façade oriented parallel to the incoming airflow showed that deposition by interception and impaction reduced remarkably, but that the orientation had no effect on deposition by Brownian diffusion. A promising green wall form for PM mitigation is the living wall system (LWS). LWS consist of supporting structures with substrate to grow plants in and can be planted with a variety of plant species. This allows to select plant species with optimal characteristics to achieve PM deposition. These characteristics refer to the macro- and microstructure of the leaves, and research has been conducted mainly on these. On the other hand, the influence of the supporting structure and substrate on PM concentrations has rarely been studied. With the new wind tunnel setup, LWS from different manufacturers were tested for their ability to capture PM. The setups were subjected for three hours to an air flow with a low PM concentration (resuspension phase) and then for three hours to an air flow to which additional PM was added (deposition phase). Some setups were able to decrease the PM concentration during both phases, while others just caused the concentration to increase. Some systems were able to reduce particulate matter concentration during both phases, namely LWS consisting of planters (-2% and -4% for PM0.1 and PM2.5, respectively) and textile cloths (-23% and -5% for PM0.1 and PM2.5, respectively). While other systems actually resulted in an increase in concentration especially LWS existing textile fabrics consisting of geotextiles (+11% for both PM fractions) and with moss as substrate (+2% and +5% for PM0.1 and PM2.5, respectively). This highlights the importance of careful selection of suspension systems to reduce particulate matter concentrations. Further research is therefore needed on the materials used in these systems in relation to their particulate content, as well as on plant development in these systems. In addition to air measurements, measurements were taken of the amount of PM deposited on the leaves and suspension system of LWS. This allowed the difference in PM resuspension and deposition between plant species to be investigated. The amount of deposited particulate matter was determined based on 'saturation isothermal remanent magnetisation' (SIRM), a measure of magnetisable particulate matter. This was possible because the added 'Arizona fine test dust' contained iron oxide. However, no significant difference was observed between the SIRM values measured before the wind tunnel experiment, after resuspension and after deposition. This suggested that the iron oxide content in the Arizona fine test dust was too low to measure a significant difference in the SIRM values on leaves after three hours. The plant species did give rise to different SIRM values ranging between 5 and 260 µ A. In particular, SIRM values above 26 µ A were observed for the plant species that were grouped due to their significantly higher accumulation of PM. 'Specific leaf area' (SLA), specifically the ratio of the one-sided 'fresh' leaf area to its dry mass, was the significant leaf characteristic. SLA correlated with leaf complexity. In particular, plant species with elongated leaves were characterized by low SLA, high FLS and high complexity and showed significantly higher SIRM values. Finally, the optimised size-resolved deposition model was also tested in an urban model to get an idea of the impact of a green wall on PM concentrations in a so-called 'street canyon'. These are narrow streets with high buildings on both sides, making air pollution more persistent. To this end, an ideal scenario was tested in which a green wall was introduced along both sides of the street over a length of about 270 m. The model result showed a decrease in PM2.5 and PM10 of 46 ± 12% and 52 ± 14%. This result is of course for a very optimal scenario where the green wall covers the entire building façades. Since this is not feasible in reality, other ways of promoting contact between green walls and polluted air can be explored. The insights obtained illustrate that the use of climbing plants can be a cost-effective and environmentally friendly solution to reduce PM concentrations. Moreover, the findings showed that models can be used to investigate the impact of green walls on PM levels. These findings fit within the broader context of designing healthy and sustainable urban environments and developing innovative solutions based on solid scientific knowledge.

Abstract: Grafting organophosphonic acids modification (PAs) on metal oxides has shown to be a flexible technology to tune the surface properties of metal oxides for various applications. Nevertheless, there are still puzzles that need to be addressed, such as the correlations between metal oxides properties (types of surface reactive sites) and the modification (modification degree), the correlations between metal oxides properties and the properties of modified surfaces. Moreover, the currently used liquid-phase method for the grafting has associated impeding effects of solvent on tailoring the modification degrees, and also causes the formation of metal phosphonate side products. The solid-phase method can induce the unwanted changes in crystal phase of supports. Based on these questions, the three titania supports with divergent surface properties were selected as the metal oxides supports investigated, propylphosphonic acid (3PA) modification was carried out under the same synthesis conditions: four different concentrations, two solvents (water or toluene), and one reaction time (4 h) and temperature (90 ). MeOH chemisorption was introduced to probe the surface (un)reactive sites for 3PA modification. On the other hand, MeOH chemisorption and inverse gas chromatography (IGC) were combined to characterize the changes in surface polarity and acidic properties induced by the modification. Next, a solid-phase method, manual grinding, was proposed to graft 3PA on titania, avoiding the impeding effects of solvent on improving modification degree and the formation of the titania phosphonate side products, as well as preserving the crystal phase. The results indicate that methanol chemisorption can qualitatively analyze the surface active sites that are consumed by 3PA modification, its chemisorption capacity shows the consistent trend with the maximum modification degree, hereby revealing the kinds of interactions that are important in controlling surface coverage. Titania supports is found to play an important role in changes in surface polarity and acidic properties by charactering the three modified titania samples at a similar modification degree using the methanol chemisorption coupled with in-situ infrared and thermogravimetric-mass spectrometer, and the IGC. Moreover, IGC provides additional information about the changes in binding modes. Furthermore, grafting 3PA modification on titania was achieved by manual grinding. Compared to the liquid-phase method, the maximum modification degree obtained by the manual grinding is 25 % higher while using 83.3 % or 75.0% lower amounts of expensive 3PA and limiting the formation of titania phosphonate side products. Compared to the reactive milling method, the proposed manual grinding method preserves the crystal phase(s) of titania.

Abstract: MicroRNAs (miRNAs) are small oligonucleotides (18-25 bases) that play a key role in epigenetic regulation. Since the discovery of miRNAs in 1993, their role in biological processes has been extensively investigated. By now, it has been evidenced that monitoring and detection of miRNAs can improve (early) disease diagnosis. The existing diagnostic approaches have limitations such as the need for complex multistep protocols for sample preparation, analysis, data interpretation, high cost of equipment, the need for highly qualified personnel, and high laboratory standards. As opposed to this, point-of-care biosensors and chips aim to facilitate the procedure and avoid sending samples into centralized laboratories, which saves time, reduces the chance of sample degradation, and enables analysis of patient samples in remote areas, directly at home or primary and secondary care facilities (i.e. general practitioners and specialists). The latter is essential for therapy assessment and follow-up monitoring of patients with chronic diseases and cancer. Nevertheless, the development of such sensors is lagging compared to the projections of 10 – 20 years ago, mainly due to insufficient sensitivity, poor reproducibility, and the complexity of the sensors’ design reported in the research literature. This motivates the development of new detection strategies and technologies such as photoelectrochemical sensors that combine the best features of different sensing approaches. The primary concern when developing detection technologies for miRNA is the need for a highly sensitive and selective platform. This thesis explores a novel photoelectrochemical (PEC) method that is distinctive owing to its sensitive nature and simple and robust design. Firstly, we focused on the usage of recently emerging commercial gold-sputtered electrode systems for the detection of short nucleic acid with enzymatic amplification. Importantly, cleaning such electrodes is a challenge since the standard procedures known for regular disk electrodes such as polishing cannot be employed here, since it will damage the protective layer on the electrode. However, the electrodes can be washed and pretreated chemically and/or electrochemically. Thus, a procedure to effectively clean and modify the gold-sputtered electrode has been developed, resulting in high-performance gold-sputtered nucleic acid sensors. Next, the usage of molecular photosensitizers as an alternative to enzymatic amplification has been evaluated. We took advantage of the singlet oxygen production by photosensitizers upon photoexcitation, leading to a photocurrent response due to the singlet oxygen-induced (electro)chemical conversions. Following the demonstration of the detection strategy, the analytical performance of the sensing system was evaluated using magnetic beads-based nucleic acid assay on disposable electrode platforms, with a focus to enhance the sensitivity and robustness of the technique in detecting complementary nucleic acid targets. Following the fundamental evaluation of the singlet oxygen-based PEC detection of nucleic acids, we further optimized the assay and measurement parameters and employed the sensing strategy for a polymerase chain reaction-free (PCR-free) quantification of miRNAs related to prostate cancer. By successfully detecting and quantifying low-picomolar range concentrations (< 10 pM) in plasma samples from prostate cancer patients, we successfully showed the applicability of the novel sensing strategy. We have also compared and positioned the performance of our developed PEC strategy with an existing state-of-art technique, i.e. electrochemiluminescence (ECL). Our PEC strategy performed on par with ECL, both yielding low-picomolar detection limits in serum matrices, however quicker and cheaper than ECL. Owing to the versatility of this PEC technique, the final study explored its multiplexing capability. As a starting point in this branch of the research, we have investigated two possible ways for multiplexing. To perform multiple measurements at the same time, constructing calibration plots and quantifying unknown miRNA concentrations in patient samples at the same time, we have developed a high-throughput detection with 96X multi-channel electrode systems and in-house designed and constructed 96XLED illumination sources. Secondly, to detect more than one target miRNA in a single measurement, intra-vial multiplexing where the samples were analyzed for different targets in one vial was also explored. Altogether, this thesis presents the fundamentals, development and application of a novel PEC strategy for detecting short (< 25 bases) nucleic acid sequences, in particular, miRNA. With an aim to serve as a distinctive technique to function as a clinical testing platform without any need for PCR, this work adds value to the development of nucleic acid-based sensors for miRNAs and other short-stranded nucleic acid biomarkers, and benefits in the early detection of diseases like cancer.

Abstract: Using state-of-the-art simulation methods, we optimized the performance of nanoscale superconducting scanning probe tips for advanced spatial imaging of magnetic fields. The systematic studies of the tips’ static properties as a function of the tilted magnetic field, geometric parameters, and material parameters were carried out. The sensitivity of different superconducting quantum interference devices (SQUIDs) to the magnetic field emanating from the magnetic nanoparticle, where the location of a magnetic nanoparticle is considered below the primary loop's center, was examined as a function of the primary and secondary loop dimensions. The main objective of the research was to characterize and optimize the performance of a nano-sized SQUID-on-tip (SOT) device. Optimal SOT sensitivity was sought, for different loop sizes, arm linewidth, and lead dimensions. Moreover, we revealed that a constriction in the loop arms of the SOT can substantially improve the sensitivity of the device. Finally, the properties of the theta-SOT device were examined in the presence of in-plane and out-of-plane magnetic field components, enabling nanoscale imaging of 3D distributions of the magnetic field. Altogether, the obtained results deliver an engineering solution for the optimum performance of the SOT device in desired conditions.

Abstract: During the past decades, metallic nanoparticles (NPs) have attracted great attention in materials science due to their specific optical properties based on surface plasmon resonances. Because of these phenomena, plasmonic NPs (or nanoplasmonics) are very promising for application in biosensing, photocatalysts, medicine, data storage, solar energy conversion, etc. Currently, colloidal synthesis techniques enable scientists to routinely produce mono and bimetallic NPs of various shapes, sizes, composition, and elemental distribution, with superior properties for plasmonic applications. Two primary directions for further advancing nanoplasmonic-based technologies include synthesizing novel morphologies, such as highly asymmetric chiral NPs, and gaining deeper insights into the factors affecting the stability of produced nanoplasmonics. With the increasing complexity of nanoplasmonics morphologies and higher stability requirements, there is a pressing need for thorough investigations into their 3D structures and their evolution under different conditions, with high resolution. Electron tomography (ET) emerges as an ideal tool to retrieve shape and element-sensitive information about individual nanoparticles in 3D, achieving resolutions down to the atomic level. Moreover, ET techniques can be combined with in situ holders, enabling detailed studies of processes mimicking real applications of nanoplasmonic-based devices. The first part of this thesis will focus on detailed studies of chiral Au NPs, promising for spectroscopy techniques based on the differential absorption of left- and right-handed circularly polarized light. Specifically, I will discuss the primary strategies for wet-colloidal growth of the various types of intrinsically chiral Au NPs. Advanced ET methods will be demonstrated as powerful tools for characterizing the final helical morphologies of the produced Au NPs and for studying the chiral growth mechanisms by examining intermediate structures obtained during chiral growth. The second part will focus on the heat-induced stability of various Au@Ag core-shell NPs. Operating in real conditions, such as elevated temperatures, may cause particle reshaping and redistribution of metals between the core and shell, gradually altering nanoplasmonics properties. Hence, a thorough understanding of the influence of size, shape, and defects on these processes is crucial for further developments. Recently developed techniques, combining fast ET with in-situ heating holders, have allowed me to evaluate the influence of various parameters (size, shape, defect structure) on heat-induced elemental redistribution in Au@Ag core-shell nanoparticles qualitatively and quantitatively. Additionally, I will discuss the prospects of high-resolution ET for visualizing the diffusion of individual atoms within complex nanostructures.

Abstract: Carbon dioxide electrochemical reduction (CO2ER) has attracted considerable attention as a technology to recycle CO2 into raw materials for chemicals using renewable energies. We recently found that Zn-Al layered double hydroxides (Zn-Al LDH) have the CO-forming CO2ER activity. However, the activity was only evaluated by using the liquid-phase CO2ER. In this study, Ni-Al and Ni-Fe LDHs as well as Zn-Al LDH were synthesized using a facile coprecipitation process and the gas-phase CO2ER with the LDH-loaded gas-diffusion electrode (GDE) was examined. The products were characterized by XRD, STEM-EDX, BF-TEM and ATR-IR spectroscopy. In the ATR-IR results, the interaction of CO2 with Zn-Al LDH showed a different carbonates evolution with respect to other LDHs, suggesting a different electrocatalytic activity. The LDH-loaded GDE was prepared by simple drop-casting of a catalyst ink onto carbon paper. For gas-phase CO2ER, only Zn-Al LDH exhibited the CO2ER activity for carbon monoxide (CO) formation. By using different potassium salt electrolytes affording neutral to strongly basic conditions, such as KCl, KHCO3 and KOH, the gas-phase CO2ER with Zn-Al LDH-loaded GDE showed 1.3 to 2.1 times higher partial current density for CO formation than the liquid-phase CO2ER.

Abstract: Today, illicit drugs are omnipresent in society. Clandestine markets are growing faster than ever before, record amounts of cocaine are seized in seaports and airports, while the associated violence is spiralling out of control. In addition, drug monitoring centres worldwide are warning for the increasing complexity of the drug markets, as the traditionally popular drugs are joined by countless new synthetic variants, while medical drugs are also increasingly being abused. In order to provide services confronted with illicit drug samples (police, customs, forensic scientists, first responders, …) with important information on the identity of an unknown sample, suitable analytical tests are required. While these exist for laboratory environments, on-site applicable tests are important to accelerate the decision-making process. Electrochemical sensors have all the advantages required for such on-site tests: they are fast, portable, easy-to-use and reliable. Furthermore, they are not influenced by colours, which are frequently added to drug samples to deceive the existing tests. Previous work has mainly focussed on the detection of a single drug per analysis. However, many drugs could be encountered due to the diversity of the drug markets. Therefore, this project developed electrochemical strategies for the detection of multiple drugs simultaneously. First, the electrochemical behaviour of the individual drugs was studied in different measuring conditions (assessing the influence of pH, concentration and temperature). Then, all findings and strategies were combined to detect multiple targets simultaneously. An electrochemical sensor was developed for the four most popular drugs at music festivals: cocaine, MDMA, amphetamine and ketamine. This sensor generates a so-called ‘superfingerprint’ of the sample, which is then automatically interpreted by a developed algorithm in order to produce a straightforward output. Finally, a pill analysis sensor was developed in the context of drug checking services, where a consumer can anonymously have a sample chemically analysed to obtain information on the composition, dose and potentially harmful additives. The sensor achieved an outstanding accuracy in identifying the main component and provided the option to quantify, as well as an indication on the presence of other substances in the sample. The project’s findings demonstrate the potential for electrochemistry in illicit drug detection and provide a basis for the development of new sensors, targeting other drug combinations.

Abstract: Inelastic excitation as exploited in Electron Energy Loss Spectroscopy (EELS) contains a rich source of information that is revealed in the scattering process. To accurately quantify core-loss EELS, it is common practice to fit the observed spectrum with scattering cross-sections calculated using experimental parameters and a Generalized Oscillator Strength (GOS) database [1].   The GOS is computed using Fermi’s Golden Rule and orbitals of bound and excited states. Previously, the GOS was based on Hartree-Fock solutions [2], but more recently Density Functional Theory (DFT) has been used [3]. In this work, we have chosen to use the Dirac equation to incorporate relativistic effects and have performed calculations using Flexible Atomic Code (FAC) [4]. This repository contains a tabulated GOS database based on Dirac solutions for computing double differential cross-sections under experimental conditions.   We hope the Dirac-based GOS database can benefit the EELS community for both academic use and industry integration.   Database Details: – Covers all elements (Z: 1-108) and all edges – Large energy range: 0.01 – 4000 eV – Large momentum range: 0.05 -50 Å-1 – Fine log sampling: 128 points for energy and 256 points for momentum – Data format: GOSH [3]   Calculation Details: – Single atoms only; solid-state effects are not considered – Unoccupied states before continuum states of ionization are not considered; no fine structure – Plane Wave Born Approximation – Frozen Core Approximation is employed; electrostatic potential remains unchanged for orthogonal states when – core-shell electron is excited – Self-consistent Dirac–Fock–Slater iteration is used for Dirac calculations; Local Density Approximation is assumed for electron exchange interactions; continuum states are normalized against asymptotic form at large distances – Both large and small component contributions of Dirac solutions are included in GOS – Final state contributions are included until the contribution of the previous three states falls below 0.1%. A convergence log is provided for reference.   Version 1.1 release note: – Update to be consistent with GOSH data format [3], all the edges are now within a single hdf5 file. A notable change in particular, the sampling in momentum is in 1/m, instead of previously in 1/Å. Great thanks to Gulio Guzzinati for his suggestions and sending conversion script.  Version 1.2 release note: – Add “File Type / File version” information [1] Verbeeck, J., and S. Van Aert. Ultramicroscopy 101.2-4 (2004): 207-224. [2] Leapman, R. D., P. Rez, and D. F. Mayers. The Journal of Chemical Physics 72.2 (1980): 1232-1243. [3] Segger, L, Guzzinati, G, & Kohl, H. Zenodo (2023). doi:10.5281/zenodo.7645765 [4] Gu, M. F. Canadian Journal of Physics 86(5) (2008): 675-689.

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: Transmission electron microscopy (TEM) is a technique that uses an electron beam to analyze materials. This analysis is based on the interaction between the electron beam and the sample, such as photon emission and electron diffraction pattern, to name a few. Sample damage, however, also occurs when such interaction alters the structure of the sample. To ensure information from the undamaged material can be acquired, the electron expense to probe the material is thus limited. In this work, we propose efficient methods for acquiring and processing the information originating from the electron-sample interaction so that the study of the material and the conducting of the TEM experiment can be less hindered by the limited dose usage. In the first part of the work, the relationship between the scattering of the electron and the local physical property of the sample is studied. Based on this relationship, two reconstruction schemes are proposed capable of producing high-resolution images at low-dose conditions. Besides, the proposed reconstructions are not restricted to complete datasets but instead work on pieces of data, therefore allowing live feedback during data acquisition. Such feature of the methods allows the whole TEM experiment to be carried out under low dose conditions and thus further reduces possible beam damage on the studied material. In the second part of the work, we discuss our approach to modulating the electron beam and its benefits. An electrostatic device that can alter the wavefront of the passing electron wave is introduced and characterized. The beam-modulation ability is demonstrated by creating orthogonal beam sets, and applications that exploit the adaptability of the wave modulator are demonstrated with both simulation and experiments.

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: Supplementary information for the article “In-situ Plasma Studies using a Direct Current Microplasma in a Scanning Electron Microscope” containing the videos of in-situ SEM imaging (mp4 files), raw data/images, and Jupyter notebooks (ipynb files) for data treatment and plots. Link to the preprint: https://doi.org/10.48550/arXiv.2308.15123 Explanation of the data files can be found in the Information.pdf file. The Videos folder contains the in-situ SEM image series mentioned in the paper. If there are any questions/bugs, feel free to contact me at lukas.grunewaldatuantwerpen.be

Abstract: Even at low temperatures, metal nanoparticles (NPs) possess atomic dynamics that are key for their properties but challenging to elucidate. Recent experimental advances allow obtaining atomic‐resolution snapshots of the NPs in realistic regimes, but data acquisition limitations hinder the experimental reconstruction of the atomic dynamics present within them. Molecular simulations have the advantage that these allow directly tracking the motion of atoms over time. However, these typically start from ideal/perfect NP structures and, suffering from sampling limits, provide results that are often dependent on the initial/putative structure and remain purely indicative. Here, by combining state‐of‐the‐art experimental and computational approaches, how it is possible to tackle the limitations of both approaches and resolve the atomistic dynamics present in metal NPs in realistic conditions is demonstrated. Annular dark‐field scanning transmission electron microscopy enables the acquisition of ten high‐resolution images of an Au NP at intervals of 0.6 s. These are used to reconstruct atomistic 3D models of the real NP used to run ten independent molecular dynamics simulations. Machine learning analyses of the simulation trajectories allows resolving the real‐time atomic dynamics present within the NP. This provides a robust combined experimental/computational approach to characterize the structural dynamics of metal NPs in realistic conditions.