Abstract: ​Food production is a cornerstone in contemporary industrial societies. Its production requires land, water and enormous amounts of fertilizers. These precious fertilizers enter the linear food chain and suffer from a cascade of inefficiencies, resulting in detrimental effects to the environment. A radical transforming of the current food production chain is, therefore, essential to guarantee a sustainable future for humanity. ​This thesis has studied the production of microbial protein (i.e. single-cell protein), which is the use of microorganisms such as yeast, fungi, algae and bacteria as protein ingredient for animal feed. The type of microorganisms targeted in this thesis were purple non-sulfur bacteria (PNSB). These bacteria are an extremely heterogenic group that contain photosynthetic pigments and are able to perform anoxygenic photosynthesis. The core focus of the thesis was technology development for the production of PNSB as a source of microbial protein on wastewater and fresh fertilizers. In the final stage of this research, it was the objective to explore the potential of PNSB as a nutritious feed ingredient for shrimp. ​Overall, this work has provided the building blocks to transform the conventional food production chain. The findings show that PNSB production and biomass valorization is within reach. Further pilot implementation and cost reduction will facilitate the introduction of PNSB production in future’s wastewater treatment plants and the valorization of the biomass as nutritious animal feed ingredient.​

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: 200 years ago, humanity started the industrial revolution by discovering fossil fuels, which lead to unprecedented technological advancements. However it has become alarmingly clear that the major environmental concerns associated with fossil fuels require a short-term transition from a carbon-based energy economy to a sustainable one based on green electricity. A key step concerning this transition exists in developing electricity-driven alternatives for chemical processes that rely on fossil fuels as a raw material. A technology that is gaining increasing interest to achieve this, is plasma technology. Using plasmas to induce chemical reactions by selectively heating electrons in a gas has already delivered promising results for gas conversion applications like CO2 conversion and N2 fixation, but plasma reactors still require optimization to be considered industrially competitive to existing fossil fuel-based processes and emerging other electricity-based technologies. In this thesis I develop computational models to describe plasma reactors and identify key mechanisms in three different plasma reactors for three different gas conversion applications, i.e. N2 fixation, combined CO2-CH4 conversion and CO2 splitting. I first developed models to describe a new rotating gliding arc (GA) reactor operating in two arc modes, which, as revealed by my model, are characterized by distinct plasma chemistry pathways. Subsequently, my colleague and I study the quenching effect of an effusion nozzle to this rotating GA reactor, reaching the best results to date for N2 fixation into NOx at atmospheric pressure, i.e., NOx concentrations up to 5.9%, at an energy cost down to 2.1 MJ/mol. Afterwards, I investigate the possible improvement of N2 admixtures in plasma-based CO2 and CH4 conversion, as significant amounts of N2 are often found in industrial CO2 waste streams, and gas separations are financially costly. Through combining my models with the experiment from a fellow PhD student, we reveal that moderate amounts of N2 (i.e. around 20%) increase both the electron density and the gas temperature to yield an overall energy cost reduction of 21%. Finally, I model quenching nozzles for plasma-based CO2 conversion in a microwave reactor, to explain the enhancements in CO2 conversion that were demonstrated in experiments. Through computational modelling I reveal that the nozzle introduces fast gas quenching resulting in the suppression of recombination reactions, which have more impact at low flow rates, where recombination is the most limiting factor in the conversion process.

Abstract: The plasma-based dry reforming in a dielectric barrier discharge (DBD) reactor is important to achieve sustainable goals, but many challenges remain. For example, the conversion and energy yield of DBD reactors are relatively low, and the catalysts or packing materials used in existing studies cannot improve them, possibly due to the unsuitable properties and structures of catalysts or packing materials for plasma processes. In order to study the effect of catalyst structure on plasma-based dry reforming, a controllable synthesis of the catalyst supports or templates was explored. In Chapter 2, an initially immiscible synthesis method was proposed to synthesize uniform silica spheres, which can replace the organic solvent-based Stöber method to successfully synthesize silica particles with the same size ranges as the original Stöber process without addition of organic solvents. Using the silica spheres as templates, 3D porous Cu and CuO catalysts with different pore sizes were synthesized in Chapter 3 to study the effect of catalyst pore size on the plasma-catalytic dry reforming. In most cases, the smaller the pore size, the higher the conversion of CH4 and CO2 due to the reaction of radicals and ions formed in the plasma. An exception are the samples synthesized from 1 μm silica, which show better performance due to the electric field enhancement for pore sizes close to the Debye length. Besides the pore size, the particle diameter of the catalyst or packing is also one of the important factors affecting the interaction between plasma and catalyst. In Chapter 4, SiO2 spheres (with or without supported metal) were used to study the effect of different support particle sizes on plasma-based dry reforming. We found that a uniform SiO2 packing improves the conversion of plasma-based dry reforming. The conversion of plasma-based dry reforming first increases and then decreases with increasing particle size, due to the balance between the promoting and hindering effect of the particle filling on the plasma discharge. Chapter 5 is to improve the design of the DBD reactor itself, in order to try to increase its low energy yield. Some stainless steel rings were put over the inner electrode rod of the DBD reactor. The presence of rings increases the local electric field, the displaced charges and the discharge fraction, and also makes the discharge more stable and with more uniform intensity. The placement of the rings improves the performance of the reactor at 30 W supplied power.

Abstract: Novel emerging biorefinery technologies have gained interest and have the potential to tackle several sustainability challenges in our society. A lignin-first biorefinery process – reductive catalytic fractionation (RCF) – is currently under development with the aim to process wood into high-value end-products that replace highly polluting fossil oil-based products. However, such emerging technologies are not matured yet, holding a certain degree of technological, economic, and environmental uncertainty. Hence, an appropriate assessment method is required to assess techno-economic feasibility and environmental impacts of emerging uncertain technologies (e.g lignin-first RCF process). This dissertation aims to develop an integrated techno-environmental economic assessment framework to assess emerging technologies dynamically and prospectively from economic and environmental points of view. First, a techno-economic assessment (TEA) is performed to assess the economic feasibility and the most influential economic and technological parameters of the lignin-first RCF biorefinery taking the whole wood value chain into account. By making the relations across the wood value chain, the scale of the biorefinery, wood species, and output prices highly determine the economic feasibility. The economic feasibility can be reached by a sufficient capacity level which depends on wood species-specific conditions. Also, waste wood proves to be the most profitable feedstock in comparison to virgin wood. Second, an analytical real options analysis (ROA) is performed taking two correlated market uncertainties and the value of flexibility into account to identify the optimal investment decision in an RCF biorefinery. Two different investment options, separated and united investments in harvesting equipment and RCF biorefinery, are analyzed. In both scenarios, market uncertainty postpones the investment. When both investment decisions are united, the probability of investment increases in comparison to separated investments. The study reveals that RCF has the potential to stimulate investments within the wood value chain. Third, a consequential life cycle assessment (LCA) is performed to assess the carbon emissions and the environmental consequences of the lignin-first RCF process and its products. The study reveals that at the current stage RCF products have higher carbon emissions than their alternative counterparts. Several options to improve the environmental performance are discussed such as different RCF technology configurations, targeting different RCF products with the ability to replace higher polluting alternative counterparts on the markets. Other discussion points such as transportation type and the distance, (in-)direct land-use change, the use stage and disposal stages implications, and a more comprehensive environmental view of the RCF products, show the potential to improve the environmental performance of the RCF technology. Overall, the study shows that the RCF process can be environmentally desirable if the appropriate RCF configuration and products are chosen. Finally, the above-mentioned methods – techno-economic assessment, analytical real options analysis, and consequential life cycle assessment – are uniquely integrated within the newly developed integrated assessment framework. The framework has the aim to complement the shortcomings and combine the advantages of all three methods. The framework assesses emerging technologies to give predictive insights about the time-specific economic and environmental performance under the newly developed three threshold conditions: technological readiness, economic feasibility, and environmental desirability. The developed integrated assessment framework assesses dynamically and prospectively the RCF biorefinery implementation under Belgian conditions. It reveals that the economic feasibility increases and carbon emissions decrease over time. The RCF biorefinery fulfills all three threshold conditions – technological readiness, economic feasibility, and environmental desirability – consecutively. The newly developed integrated assessment framework offers decision support to several stakeholders of emerging technologies starting from low technology readiness level (TRL). Practitioners such as the technology developers, researchers, and policymakers can use the framework to evaluate emerging technologies that deal with high levels of technological, economic, and environmental uncertainties. The framework assesses emerging technologies on a detailed level to give decision-makers in-depth insights into the intertwined nature of the technological, economic, and environmental dimensions. It offers insights into the expected time-specific economic and environmental performances, potential, and challenges of the emerging technology to further improve the technology and direct R&Ds along the right path.

Abstract: Ever since the industrial revolution, the emission of greenhouse gasses dramatically increased, resulting in high CO2 concentration in the atmosphere. The electrochemical conversion of CO2 to value added products, such as carbon monoxide, formic acid, methane, ethylene and ethanol is a very promising strategy to inhibit CO2 emissions. Nevertheless, at the moment, the electrochemical CO2 reduction (eCO2R) is not yet industrially viable, mainly due to the lack of good electrocatalysts. On the other hand, core-shell nanoparticles (NPs) have emerged over the last couple of years as promising candidates. It is believed that bimetallic enhancement effects are behind the improved performance of these core-shell NPs when compared to the individual metals. Although widely investigated, there are still some remaining issues and/or open questions. Indeed, the development of a robust and straightforward synthesis method along with fundamental insight into their resistance towards electrochemical stress remains absent. A good control over morphology, size and composition is key in determining which properties are beneficial for the eCO2R. Since these catalysts are designed to be implemented in electrolyzers, they have to maintain long-term performance. This makes the design of a reproducible method, unveiling structure-performance relationships the effect of electrochemical stress, a crucial aspect. Exploring and modifying existing synthesis methods, have led to the acquisition of a robust and reproducible synthesis method where thermal decomposition of the Cu core is combined with the galvanic replacement of Ag in organic solvents. The implementation of this method has led to the design of a wide variety of Cu-Ag bimetallic NPs and enabled to investigate their composition-selectivity profile. Introducing Ag on Cu suppressed hydrogen and increased the CO formation. CO production was boosted by using Cu@Ag core-shells and was promoted even more by changing the type of electrolyte. As these nanoparticles suffered from degradation, the 3D mapping of the structural changes of Cu@Ag core-shells under operating conditions led to the hypothesis of a two-step degradation mechanism where initially Cu leaching was observed with the subsequent sintering of the Ag shells. One approach to avoid this electrochemical degradation, investigated in this research, was the application of an ultrathin carbon layer to protect the active layer. This ultrathin carbon layer operated as a protective layer, suppressing hydrogen production and increasing the stability of the electrocatalyst. In conclusion, the product selectivity can be tuned by using different Cu-Ag bimetallic nanoparticles synthesized through a robust method. Their unique degradation pathway of Cu@Ag core-shell nanoparticles has led to the proposition of a more accurate stabilization strategy. These findings can contribute significantly in the quest for improved electrocatalysts for the eCO2R.

Abstract: Scaling down the size of transistors has been a trend for several decades which has led to improved transistor performance, increased transistor density and hence the overall computation power of IC chips. The trend slowed in recent years due to reliability and power consumption issues at the nanoscale. Hence strain is introduced into transistor channels that has beneficial effects on improving the mobility of charge carriers, providing an alternative pathway for enhancing transistor performance. Therefore, monitoring strain is vital for the semiconductor industry. With the recent trend of decreasing device dimensions (FinFETS ~ 10-20nm) and strain modulation being used throughout, industry needs a reliable and fast method as quality control or defect characterisation. Such a universal strain measurement method does not exist, and one relies on a combination of quantitative in-line methods and complex off-line approaches. In this thesis, I investigated TEM and Raman spectroscopy-based methodologies for strain measurement. In terms of TEM methodologies, advancements are made for the STEM moiré imaging, targeting strain spatial resolution enhancement. I introduce advanced quadrature demodulation and phase stepping interferometry applied to STEM moiré that greatly enhances the spatial resolution while providing enhanced field of view and sensitivity for strain measurement. We introduce ways to reduce scan distortions in strain maps using an alternative scan strategy called “Block scanning” and the non-linear regression applied for strain extraction. Prospects for 3D strain analysis using high-resolution tomography is also investigated which gives direct access for the full second order strain tensors calculation. Finally, we compare strain measurements from TEM techniques with inline techniques like Raman spectroscopy. Raman stress measurement involves sensitive identification of the TO and LO phonon peaks. Raman spectrum of strained Ge transistor channel consists of strongly overlapping peaks within the spectral resolution of the spectrometer. Hence, the process of deconvolution of the two peaks is rather challenging. Hence, we explore new polarisation geometries like radially polarised incoming light which was shown to ease the deconvolution problem resulting in improved precision for Raman stress–strain measurements.

Abstract: Irradiation assisted stress corrosion cracking (IASCC) is an intergranular cracking effect which can occur in heavily irradiated internal structural components of nuclear reactor cores. It is a complex phenomenon which is not yet fully understood because it occurs through an interplay of several material degradation processes. The factors that influence IASCC susceptibility include irradiation damage (neutrons and other irradiation particles stemming from the nuclear fission reaction), the operating temperature of the nuclear reactor, water corrosion, operating stresses, and the composition of materials susceptible to IASCC. Such materials are typically fabricated from austenitic stainless steels because of their relatively high strength, ductility, and fracture toughness. However, besides excellent metallurgical and corrosion resistant qualities, the operating conditions may still cause severe material degradation and component failure, which is extremely important for nuclear power plant safety and lifetime managements. Despite much accumulated data in the literature, both crack initiation and crack propagation mechanisms still need to be further elucidated. To that end, a probabilistic fracture model entitled the subcritical crack propagation (SCP) was recently developed, which assumes that the oxidized part of stainless steel in front of the crack plays an essential role in the crack initiation and crack propagation in sample failures. Still, despite a very good agreement with experimental observations, the SCP model but also other contemporary models favoured within the literature, require further experimental verification to what concerns the investigation of (IA)SCC. To that end, the main objective of this doctorate was to utilize experimental instrumentations like SEM, FIB-SEM and (S)TEM to conduct the investigation of the crack initiation and propagation processes in both tested and industrial specimens. Some of the investigated materials were retrieved within a nuclear reactor and are thus considered as unique test material to investigate the material degradation processes relevant for cracking. Other specimens were tailor-made to simulate the cracking processes of irradiated materials in otherwise un-irradiated materials. The newly acquired experimental results in this doctorate help rationalize existing models and methodologies used in the literature to analyse the IASCC failures of structural materials of reactor components. These results also facilitate in the development of predictive methodologies and mitigation strategies towards IASCC cracking and provide more information on IASCC from a microstructural perspective.

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.