Abstract: abstract not available

Abstract: The miniaturization of transistors for application in new processors and logic devices poses a significant challenge in the field of materials. Spintronics, which relies on controlled movement of magnetic nanostructures, offers a promising solution. Among the candidates, magnetic skyrmions are considered one of the most promising. These chiral spin structures, characterized by topological protection and enhanced stability compared to vortices or magnetic bubbles, have been extensively studied. To advance in the control of skyrmion motion, essential for practical applications, we investigated their dynamic behavior in a two-dimensional chiral magnet at zero temperature. Our study focused on the influence of periodic arrays of pinning centers. The simulations considered skyrmions as point-like particles considering the following interactions: skyrmion-skyrmion interactions, interactions with pinning center arrays, a current of polarized spins, and the Magnus force. We conducted calculations for scenarios involving a single skyrmion as well as different skyrmion density values in the material. The aim was to explore possibilities for controlled skyrmion motion, investigate different dynamic regimes, and examine collective effects. The results demonstrate that by adjusting the size, strength, and density of the pinning centers, we can effectively control the motion of individual skyrmions and manage the flow of multiple skyrmions. Furthermore, we discovered that periodic arrays of pinning centers can facilitate topological selection when different species of skyrmions with distinct Magnus components are present. Employing alternating currents, we observed the significant role of the ratchet effect in the skyrmion dynamics. By fine-tuning the amplitudes of the alternating currents, we achieved direct and controlled motion of skyrmions in specific directions. These findings hold potential for advancing our understanding of skyrmion dynamics and can inspire future technological applications involving these quasi-particles. Overall, we anticipate that our results will be valuable to the scientific community, contributing to a deeper comprehension of skyrmion dynamics and paving the way for future technological applications.

Abstract: Pretreatment is a critical step in the conversion of lignocellulose into biofuels and biochemicals. During pretreatment, the recalcitrance of lignocellulose is reduced, e.g. by removing lignin, thereby making the carbohydrates more accessible for enzymatic saccharification. Fungal delignification by white-rot fungi is a biotechnological alternative to chemical/physicochemical methods, which is carried out in solid-state fermentation with mild reaction conditions and without the formation of microbial inhibitors. However, fungal pretreatment presents some challenges, such as long pretreatment time, non-selective and low delignification, low enzymatic digestibility and feedstock sterilisation requirement, making its commercial implementation challenging compared to conventional methods. This study investigates the possibility of improving and characterising the solid-state fungal pretreatment of poplar wood by Phanerochaete chrysosporium. The individual and combined effects of MnSO4 and CuSO4 supplements on the delignification of sterilised wood are investigated using response surface methodology to improve the degree and selectivity of fungal delignification. Spore-inoculated solid-state fermentations are carried out for 4 weeks in sterile vented bottles. The mechanism of the concerted action of the metal ions on lignin degradation is then elucidated by relating fungal growth and ligninolytic enzyme activities to lignocellulose degradation as a function of pretreatment time. The optimised metal-supplemented system is then applied to the pretreatment of non-sterilised wood using different inoculation techniques (spores and pre-colonised substrate), nutrients (metal ions with or without glucose and sodium nitrate) and cultivation environments (sterile aerated bottles and open trays). The fermentations are then characterised using infrared spectroscopy, in particular NIR and ATR-FTIR spectroscopy, with the aim of developing rapid lignin quantification methods as an alternative to conventional wet chemical methods. Finally, the feasibility of producing fermentable sugars from sterilised and non-sterilised poplar wood using fungal pretreatment is evaluated through a techno-economic analysis. Supplementing the pretreatment system with 2.01 µmol CuSO4 and 0.77 µmol MnSO4 g-1 wood resulted in 1.9-fold higher lignin degradation, 2.3-fold higher delignification selectivity value and 2.9-fold higher glucose yield. The improved delignification could be explained by the concerted action of Mn2+ and Cu2+ ions, with Mn2+ ions inducing and Cu2+ prolonging manganese peroxidase production responsible for delignification. Fungal pretreatment at non-sterile conditions was obtained using trays in a simple solid-state fermentation set-up without sterile aeration. A 1:3 ratio of pre-colonised and untreated wood was applied for inoculation and only Cu2+, Mn2+ and sodium nitrate as supplements. Remarkably, this technology resulted in a comparably high glucose yield (28.51 ± 0.28%) to the traditional method using sterilised wood, sterile aeration and spores as inoculum, while reducing the amount of wood to be sterilised by 71.2%. Infrared spectroscopy-based methods with high coefficients of determination (R_CV

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: 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: 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: Electrons, X-rays and neutrons that pass through a thin crystalline sample will be diffracted. Diffraction patterns of crystalline materials contain Bragg reflections (sharp discrete intensity maxima) and diffuse scattering (a weak continuous background). The Bragg reflections contain information about the average crystal structure (the type of atoms and the average atomic positions), whereas the diffuse scattering contains information about the short-range order (deviations from the average crystal structure that are ordered on a local scale). Because the properties of many materials depend on the short-range order, refining short-range order parameters is essential for understanding and optimizing material properties. The refinement of short-range order parameters has previously been applied to the diffuse scattering in single-crystal X-ray and single-crystal neutron diffraction data but not yet to the diffuse scattering in single-crystal electron diffraction data. In this work, we will verify the possibility to refine short-range order parameters from the diffuse scattering in single-crystal electron diffraction data. Electron diffraction allows to acquire data on submicron-sized crystals, which are too small to be investigated with single-crystal X-ray and single-crystal neutron diffraction. In the first part of this work, we will refine short-range order parameters from the one-dimensional diffuse scattering in electron diffraction data acquired on the lithium-ion battery cathode material Li1.2Ni0.13Mn0.54Co0.13O2. The number of stacking faults and the twin percentages will be refined from the diffuse scattering using a Monte Carlo refinement. We will also describe a method to determine the spinel/layered phase ratio from the intensities of the Bragg reflections in electron diffraction data. In the second part of this work, we will refine short-range order parameters from the three-dimensional diffuse scattering in both single-crystal electron and single-crystal X-ray diffraction data acquired on Nb0.84CoSb. The correlations between neighbouring vacancies and the displacements of Sb and Co atoms will be refined from the diffuse scattering using a Monte Carlo refinement and a three-dimensional difference pair distribution function refinement. The effect of different experimental parameters on the spatial resolution of the observed diffuse scattering will also be investigated. Finally, the model of the short-range Nb-vacancy order in Nb0.84CoSb will also be applied to LiNi0.5Sn0.3Co0.2O2.

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: Nowadays, the burning of fossil fuels, particularly petroleum, natural gas, and coal, meets the rising need for power and fuels for automobiles and industries. This has given rise to ecological and climate challenges. This thesis explores these issues from three distinct perspectives: (i) experimental, (ii) computational, and (iii) process simulation, with a focus on studying CO2 as an alternative and economically viable raw material. Firstly, the experimental study is focused on the synthesis, characterization, and testing of novel catalysts for electroreduction of CO2 and oxalic acid, an intermediate product of CO2. Electrocatalysts based on Cu supported by citrus (orange and lemon) peel biomass are prepared. These catalysts exhibit activity in the electrochemical reduction of CO2, emphasizing the effectiveness of biomasses, particularly orange peels, as environmentally friendly precursors for sustainable and efficient electrocatalysts. In addition, graphitic carbon nitrides/TiO2 nanotubes (g-C3N4/TiNT) composites are prepared for the electrocatalytic reduction of oxalic acid to glycolic acid, revealing superior electrocatalytic properties compared to pristine TiNT. Characterization by X-ray diffraction, X-ray photoelectron spectroscopy, and scanning electronic microscopy were performed for all the prepared electrocatalysts. Delving into the reduction of CO2 on Cu catalysts, a computational study about the synthesis of methanol on Cu(111) surface is performed by using the Vienna Ab initio Simulation Package. A systematic study is carried out to define the activation energies of the elementary reactions by using mGGA DF. Consequently, it is shown that the rate-controlling step is CH3O* hydrogenation and the formate pathway on Cu(111) proceeds through the HCOOH* intermediate. Finally, the process simulation, performed by using the software Aspen Plus 11 from AspenTech Inc., is based on the comparison of a catalytic (oxidation of ethylene glycol) and an electrocatalytic process (CO2 electroreduction chain) to synthesize glycolic acid. An economic analysis of the operational and investment costs reveals that the catalytic process is more cost-effective due to the current instability of electrocatalysts and proton exchange membranes, resulting in increased maintenance costs and, consequently, higher prices for the product.