Abstract: Plasma reactors are interesting for gas-based chemical conversion but the fundamental relation between the plasma chemistry and selected conditions remains poorly understood. Apparent kinetic parameters for the loss and formation processes of individual components of gas conversion processes, can however be extracted by performing experiments in an extended residence time range (2–75 s) and fitting the gas composition to a firstorder kinetic model of the evolution towards partial chemical equilibrium (PCE). We specifically investigated the differences in kinetic characteristics and PCE state of the CO2 dissociation and CH4 reforming reactions in a dielectric barrier discharge reactor (DBD), how these are mutually affected when combining both gases in the dry reforming of methane (DRM) reaction, and how they change when a packing material (non-porous SiO2) is added to the reactor. We find that CO2 dissociation is characterized by a comparatively high reaction rate of 0.120 s−1 compared to CH4 reforming at 0.041 s−1; whereas CH4 reforming reaches higher equilibrium conversions, 82% compared to 53.6% for CO2 dissociation. Combining both feed gases makes the DRM reaction to proceed at a relatively high rate (0.088 s−1), and high conversion (75.4%) compared to CO2 dissociation, through accessing new chemical pathways between the products of CO2 and CH4. The addition of the packing material can also distinctly influence the conversion rate and position of the equilibrium, but its precise effect depends strongly on the gas composition. Comparing different CO2:CH4 ratios reveals the delicate balance of the combined chemistry. CO2 drives the loss reactions in DRM, whereas CH4 in the mixture suppresses back reactions. As a result, our methodology provides some of the insight necessary to systematically tune the conversion process.

Abstract: Research in plasma reactor designs is developing rapidly as plasma technology is gaining increasing interest for sustainable gas conversion applications, like the conversion of greenhouse gases into value-added chemicals and renewable fuels, and fixation of N₂from air into precursors of mineral fertilizer. As plasma is generated by electric power and can easily be switched on/off, these applications allows for efficient conversion and energy storage of intermittent renewable electricity. In this paper, we present a new comprehensive modelling approach for the design and development of gliding arc plasma reactors, which reveals the fluid dynamics, the arc behaviour and the plasma chemistry by solving a unique combination of five complementary models. This results in a complete description of the plasma process, which allows one to efficiently evaluate the performance of a reactor and indicate possible design improvements before actually building it. We demonstrate the capabilities of this method for an experimentally validated study of plasma-based NO_xformation in a rotating gliding arc reactor, which is gaining increasing interest as a flexible, electricity-driven alternative for the Haber–Bosch process. The model demonstrates the importance of the vortex flow and the presence of a recirculation zone in the reactor, as well as the formation of hot spots in the plasma near the cathode pin and the anode wall that are responsible for most of the NO_xformation. The model also reveals the underlying plasma chemistry and the vibrational non-equilibrium that exists due to the fast cooling during each arc rotation. Good agreement with experimental measurements on the studied reactor design proves the predictive capabilities of our modelling approach.

Abstract: Tailoring heat treatments for Laser Powder Bed Fusion (LPBF) processed materials is critical to ensure superior and repeatable material properties for high-end applications. This tailoring requires in-depth understanding of the LPBF-processed material. Therefore, the current study aims at unravelling the threefold interrelationship between the process (LPBF and heat treatment), the microstructure at different scales (macro-, meso-, micro-, and nano-scale), and the macroscopic material properties of AlSi10Mg. A similar solidification trajectory applies at different length scales when comparing the solidification of AlSi10Mg, ranging from mould-casting to rapid solidification (LPBF). The similarity in solidification trajectories triggers the reason why the Brody-Flemings cellular microsegregation solidification model could predict the cellular morphology of the LPBF as-printed microstructure. Where rapid solidification occurs at a much finer scale, the LPBF microstructure exhibits a significant grain refinement and a high degree of silicon (Si) supersaturation. This study has identified the grain refinement and Si supersaturation as critical assets of the as-printed microstructure, playing a vital role in achieving superior mechanical and thermal properties during heat treatment. Next, an electrical conductivity model could accurately predict the Si solute concentration in LPBF-processed and heat-treated AlSi10Mg and allows understanding the microstructural evolution during heat treatment. The LPBF-processed and heat-treated AlSi10Mg conditions (as-built (AB), direct-aged (DA), stress-relieved (SR), preheated (PH)) show an interesting range of superior mechanical properties (tensile strength: 300-450 MPa, elongation: 4-13%) compared to the mould-cast T6 reference condition.

Abstract: Single-shot structured light profilometry (SLP) aims at reconstructing the 3D height map of an object from a single deformed fringe pattern and has long been the ultimate goal in fringe projection profilometry. Recently, deep learning was introduced into SLP setups to replace the task-specific algorithm of fringe demodulation with a dedicated neural network. Research on deep learning-based profilometry has made considerable progress in a short amount of time due to the rapid development of general neural network strategies and to the transferrable nature of deep learning techniques to a wide array of application fields. The selection of the employed loss function has received very little to no attention in the recently reported deep learning-based SLP setups. In this paper, we demonstrate the significant impact of loss function selection on height map prediction accuracy, we evaluate the performance of a range of commonly used loss functions and we propose a new mixed gradient loss function that yields a higher 3D surface reconstruction accuracy than any previously used loss functions.

Abstract: Bridging the gap between complex signal data output and clear interpretation by non-expert end-users is a major challenge many scientists face when converting their scientific technology into a real-life application. Currently, pattern recognition algorithms are the most frequently encountered signal data interpretation algorithms to close this gap, not in the least because of their straight-forward implementation via convenient software packages. Paradoxically, just because their implementation is so straight-forward, it becomes cumbersome to integrate the expert's domain-specific knowledge. In this work, a novel signal data interpretation approach is presented that uses this domain-specific knowledge as its fundament, thereby fully exploiting the unique expertise of the scientist. The new approach applies data preprocessing in an innovative way that transcends its usual purpose and is easy to translate into a software application. Multiple case studies illustrate the straight-forward application of the novel approach. Ultimately, the approach is highly suited for integration in various (bio)analytical applications that require interpretation of signal data.

Abstract: Research has shown the potential of photofuel cells (PFCs) for waste water treatment, enabling the (partial) recovery of the energy released from the degraded compounds as electricity. Literature on PFCs targeting air pollution on the other hand is extremely scarce. In this work an autonomously operating air purification device targeting sustainable electricity generation is presented. Knowledge on gas phase operation of PFCs was gathered by combining photocatalytic and photoelectrochemical measurements, both for TiO2 and WO3-based photocatalysts. While TiO2-based photocatalysts performed better in direct photocatalytic experiments, they were outperformed by WO3-based photoanodes in all-gas-phase PFC operation. Not only do WO3-based photocatalysts generate the highest steady state photocurrent, they also achieved the highest fuel-to-electricity conversion (>65 %). The discrepancies between gas phase photocatalytic and photoelectrochemical processes highlight the difference in driving material properties. This study serves as a proof-of-concept towards development of an autonomous, low-cost and widely applicable waste gas-to-electricity PFC device.

Abstract: Pancreatic ductal adenocarcinoma (PDAC) and glioblastoma multiforme (GBM) are two of the most malignant solid tumor types with poor survival rates, which underscore the urgency of novel and efficacious treatment strategies. Within the last decade, immunotherapy has been established as a breakthrough in cancer therapy. This mainly has been driven by the clinical data and approval associated with several immune checkpoint inhibitors (e.g. anti-CTLA-4 and anti-PD-1/L1). Despite the clinical benefit in specific tumor types, these inhibitors have not yet fulfilled their promise in low immunogenic tumors such as PDAC and GBM. Oxidative stress in cancer cells due to elevated reactive oxygen species (ROS) and an inability to balance intracellular redox state has recently been highlighted as promising target for anticancer treatment strategies with possible immunogenic effects. In this PhD dissertation, I investigated novel oxidative stress-mediated treatment approaches to target PDAC and GBM and to enhance immunogenicity by inducing immunogenic cell death (ICD). In the first part of this thesis (chapter 2), I reviewed the mechanistic responses of cancer cells towards different oxidative stress-inducing treatment strategies and their immunomodulating effects. The resulting literature demonstrated that different exogenous and endogenous ROS-inducing therapies show direct and indirect immunomodulating effects, which can be either immunostimulatory or immunosuppressive. One of the indirect immunostimulatory effects of the ROS-mediating therapies is the capacity of inducing immunogenic cell death (ICD) in tumor cells, which can increase the immunogenicity and consequently can trigger an antitumoral immune response. In chapter 3, I investigated a novel exogenous ROS-inducing treatment method, namely cold atmospheric plasma, to determine the therapeutic and ICD-inducing effects in PDAC, in vitro. I revealed that plasma-treated PBS (pPBS) has the potential to induce ICD in pancreatic cancer cells (PCCs) and to reduce the immunosuppressive tumor microenvironment (TME) by attacking the tumor supportive pancreatic stellate cells (PSCs). Although the cell death induced in PSCs was non-immunogenic as seen by the lack of danger-associated molecular patterns (DAMPs) emission and DC activation, I showed that pPBS could disrupt the physical barrier and lower the immunosuppressive secretion profile (lower TGF-β) of PSCs. In contrast, DAMPs were released by PCCs after treatment with pPBS which resulted in activation and maturation of DCs and a more immunostimulatory secretion profile (higher TNF-α, IFN-γ). Hence, indirect plasma treatment via pPBS has the potential to enhance immunogenicity in PDAC by triggering ICD and by attacking the immunosuppressive PSCs. Tumor cells can evolve adaptation mechanisms to protect themselves against intrinsic oxidative stress by upregulation of pro-survival molecules and their antioxidant defense system to maintain the redox balance. As such, tumor cells can become resistant towards exogenous ROS-inducing therapies, like plasma. Dual targeting of the redox balance of tumor cells by increasing exogenous levels of ROS and inhibiting the antioxidant defense system can maximally exploit ROS-mediated cell death mechanisms as therapeutic anticancer strategy. In this regard, cold atmospheric plasma was combined with auranofin, a thioredoxin reductase inhibitor, in GBM (chapter 4). A synergistic effect was shown after this combination treatment in 2D and 3D, however, in 3D only high concentrations of auranofin synergized with plasma treatment. I confirmed a ROS-mediated response after combination treatment, which was able to induce distinct cell death mechanisms, specifically apoptosis and ferroptosis. Additionally, the auranofin and plasma combined treatment strategy induced cell death, which resulted in an increased release of DAMPs. Together with the observed DC maturation, these results indicates the potential increase in immunogenicity, though, the phagocytotic capacity of DCs was inhibited by auranofin. In chapter 5, I evaluated this promising oxidative stress combination therapy in GBM, in vivo. A decrease in tumor kinetics and an increased survival in GBM-bearing mice was observed when auranofin was sequentially combined with direct plasma treatment. No T cell infiltration was observed after auranofin monotherapy. However, further characterization of the TME after the combination therapy is necessary to provide more insight in the immunogenic effects in vivo. In conclusion, this PhD dissertation comprises novel and important therapeutic and immunogenic insights in cold atmospheric plasma and auranofin as promising oxidative stress-mediated treatment strategies for low immunogenic tumors, like PDAC and GBM. These preclinical results provide a solid basis for future research towards combinations with immunotherapeutic approaches.

Abstract: A better understanding of the drivers of the economic, environmental, and social sustainability of emerging (biobased) technologies and products in early development phases can help decision-makers to identify sustainability hurdles and opportunities. Furthermore, it guides additional research and development efforts and investment decisions, that will, ultimately, lead to more sustainable products and technologies entering a market. To this end, this study developed a novel techno-sustainability assessment (TSA) framework with a demonstration on a biobased chemical application. The integrated TSA compares the potential sustainability performance of different (technology) scenarios and helps to make better-informed decisions by evaluating and trading-off sustainability impacts in one holistic framework. The TSA combines methods for comprehensive indicator selection and integration of technological and country-specific data with environmental, economic, and social data. Multi-criteria decision analysis (MCDA) is used to address data uncertainty and to enable scenario comparison if indicators are expressed in different units. A hierarchical, stochastic outranking approach is followed that compares different weighting schemes and preference structures to check for the robustness of the results. The integrated TSA framework is demonstrated on an application for which the sustainability of a production and harvesting plant of microalgae-based food colorants is assessed. For a set of scenarios that vary with regard to the algae feedstock, production technology, and location, the sustainability performance is quantified and compared, and the underlying reasons for this performance are explored.

Abstract: Mainstream nitrogen removal by partial nitritation/anammox (PN/A) can realize energy and cost savings for sewage treatment. Selective suppression of nitrite oxidizing bacteria (NOB) remains a key bottleneck for PN/A implementation. A rotating biological contactor was studied with an overhead cover and controlled air/N2 inflow to regulate oxygen availability at 20 °C. Biofilm exposure to dissolved oxygen concentrations < 0.51 ± 0.04 mg O2 L-1 when submerged in the water and < 1.41 ± 0.31 mg O2 L-1 when emerged in the headspace (estimated), resulted in complete and long-term NOB suppression with a low relative nitrate production ratio of 10 ± 4%. Additionally, weekly biofilm stressor treatments with free ammonia (FA) (29 ± 1 mg NH3-N L-1 for 3 h) could improve the NOB suppression while free nitrous acid treatments had insufficient effect. This study demonstrated the potential of managing NOB suppression in biofilm-based systems by oxygen control and recurrent FA exposure, opening opportunities for resource efficient nitrogen removal.

Abstract: Water quality standards (WQS) set the legal definition for safe and desirable water. WQS impose regulatory concentration limits to act as a jurisdiction-specific legislative risk-management tool. Despite its importance in shaping a universal definition of safe, clean water, little information exists with respect to (dis)similarity of chemical WQS worldwide. Therefore, this paper compares chemical WQS for drinking and surface water matrices in eight jurisdictions representing a global geographic distribution: Australia, Brazil, Canada, China, the European Union, the region of Flanders in Belgium, the United States of America, and South Africa. The World Health Organization's list is used as a reference for drinking water standards. Sørensen–Dice indices (SDI) showed little qualitative similarity in the compounds that are regulated in drinking water (median SDI = 40%) and surface water (median SDI = 33%), indicating that the heterogeneity within a matrix is substantial at the level of the standard. Quantitative similarity for matching standards was higher than the qualitative per Kendall correlation (median = 0.73 and 0.58 for drinking water and surface water respectively), yet variance observed within standards remained inexplicably high for organic compounds. Variations in WQS were more pronounced for organic compounds. Most differences cannot be easily explained from a toxicological or risk-based point-of-view. Historical development, ease of measurement, and (toxicological) knowledge gaps on the risk of a vast number of organic compounds are theorized to be the drivers. Therefore, this study argues for a more tailored, risk-based approach in which standards incorporated into water safety plans are dynamically set for compounds that are persistent and could pose a risk for human health and/or aquatic ecosystems. Global variations in WQS should therefore not necessarily be avoided but rather globally harmonized with enough flexibility to ensure a global, up-to-date definition of safe and desirable water everywhere.

Abstract: The characteristics of confined magnetoelastic waves in nanoscale ferromagnetic magnetostrictive waveguides have been investigated by a combination of analytical and numerical calculations. The presence of both magnetostriction and inverse magnetostriction leads to the coupling between confined spin waves and elastic Lamb waves. Numerical simulations of the coupled system have been used to extract the dispersion relations of the magnetoelastic waves as well as their mode profiles.

Abstract: Based on the current knowledge, a plasma–liquid interface looks and behaves very differently from its counterpart at a solid surface. Local processes characteristic to most liquids include a stronger evaporation, surface deformations, droplet ejection, possibly distinct mechanisms behind secondary electron emission, the formation of an electric double layer, and an ion drift-mediated liquid resistivity. All of them can strongly influence the interfacial charge distribution. Accordingly, the plasma sheath at a liquid surface is most likely unique in its own way, both with respect to its structure and behavior. However, insights into these properties are still rather scarce or uncertain, and more studies are required to further disclose them. In this Perspective, we argue why more research on the plasma sheath is not only recommended but also crucial to an accurate understanding of the plasma–liquid interaction. First, we analyze how the sheath regulates various elementary processes at the plasma–liquid interface, in terms of the electrical coupling, the bidirectional mass transport, and the chemistry between plasma and liquid phase. Next, these three regulatory functions of the sheath are illustrated for concrete applications. Regarding the electrical coupling, a great deal of attention is paid to the penetration of fields into biological systems due to their relevance for plasma medicine, plasma agriculture, and food processing. Furthermore, we illuminate the role of the sheath in nuclear fusion, nanomaterial synthesis, and chemical applications. As such, we hope to motivate the plasma community for more fundamental research on plasma sheaths at liquid surfaces.

Abstract: HAADF-STEM tomography is a widely used experimental technique for analyzing nanometer-scale crystalline structures of a large variety of materials in three dimensions. Unfortunately, the acquisition of conventional HAADF-STEM tilt series can easily take up one hour or more, depending on the complexity of the experiment. It is therefore far from straightforward to investigate samples that do not withstand long acquisition or to acquire large amounts of tilt series during a single TEM experiment. The latter would lead to the ability to obtain statistically meaningful 3D data, or to perform in situ 3D characterizations with a much shorter time resolution. Various HAADF-STEM acquisition strategies have been proposed to accelerate the tomographic acquisition and reduce the required electron dose. These methods include tilting the holder continuously while acquiring a projection “movie” and a hybrid, incremental, methodology which combines the benefits of the conventional and continuous technique. However, until now an experimental evaluation has been lacking. In this paper, the different acquisition strategies will be experimentally compared in terms of speed, resolution and electron dose. This evaluation will be performed based on experimental tilt series acquired for various metallic nanoparticles with different shapes and sizes. We discuss the data processing involved with the fast HAADF-STEM tilt series and provide a general guideline when which acquisition strategy should be preferentially used.

Abstract: Currently, a large variety of materials are studied by transmission electron microscopy (TEM) as it offers the possibility to perform structural and elemental analysis at a local scale. Relatively recent advances in aberration correctors and electron sources allow the instrument to achieve atomic resolution. Along with these advances, a state-of-the-art technology has been reached in TEM. However, the instrument is far from being perfect and imperfections or external sources can make the interpretation of information troublesome. Environmental factors such as acoustic and mechanical vibrations, temperature fluctuations, etc., can induce sample drift and create image distortions. These distortions are enhanced in scanning operation because of the serial acquisition of the information, which are more apparent at atomic resolution as small field of views are imaged. In addition, scanning distortions are induced due to the finite time response of the scan coils. These types of distortions would reduce precision in atomic-scale strain analysis, for instance, in semiconductors. Most of the efforts to correct these distortions are focused on data processing techniques post-acquisition. Another limitation in TEM is beam damage effects. Beam damage arises because of the energy transferred to the sample in electron-sample interactions. In scanning TEM, at atomic resolution, the increased electron charge density (electron dose) carried on a sub-Å size electron probe may aggravate beam damage effects. Soft materials such as zeolites, organic, biological materials, etc., can be destroyed under irradiation limiting the amount of information that can be acquired. Current efforts to circumvent beam damage are mostly based on low electron dose acquisitions and data processing methods to maximize the signal at low dose conditions. In this thesis, a different approach is given to address drift and scanning distortions, as well as beam damage effects. Novel scan strategies are proposed for that purpose, which are shown to substantially overcome these issues compared to the standard scan method in TEM.

Abstract: The added value of supervised Machine Learning (ML) methods to determine the Absolute Configuration (AC) of compounds from their Vibrational Circular Dichroism (VCD) spectra was explored. Among all ML methods considered, Random Forest (RF) and Feedforward Neural Network (FNN) yield the best performance for identification of the AC. At its best, FNN allows near-perfect AC determination, with accuracy of prediction up to 0.995, while RF combines good predictive accuracy (up to 0.940) with the ability to identify the spectral areas important for the identification of the AC. No loss in performance of either model is observed as long as the spectral sampling interval used does not exceed the spectral bandwidth. Increasing the sampling interval proves to be the best method to lower the dimensionality of the input data, thereby decreasing the computational cost associated with the training of the models.