Abstract: Even though nitrification/denitrification is a robust technology to remove nitrogen from sewage, economic incentives drive its future replacement by shortcut nitrogen removal processes. The latter necessitates high potential activity ratios of ammonia oxidizing to nitrite oxidizing bacteria (rAOB/rNOB). The goal of this study was to identify which wastewater and process parameters can govern this in reality. Two sewage treatment plants (STP) were chosen based on their inverse rAOB/rNOB values (at 20 °C): 0.6 for Blue Plains (BP, Washington DC, US) and 1.6 for Nieuwveer (NV, Breda, NL). Disproportional and dissimilar relationships between AOB or NOB relative abundances and respective activities pointed towards differences in community and growth/activity limiting parameters. The AOB communities showed to be particularly different. Temperature had no discriminatory effect on the nitrifiers' activities, with similar Arrhenius temperature dependences (ΘAOB = 1.10, ΘNOB = 1.061.07). To uncouple the temperature effect from potential limitations like inorganic carbon, phosphorus and nitrogen, an add-on mechanistic methodology based on kinetic modelling was developed. Results suggest that BP's AOB activity was limited by the concentration of inorganic carbon (not by residual N and P), while NOB experienced less limitation from this. For NV, the sludge-specific nitrogen loading rate seemed to be the most prevalent factor limiting AOB and NOB activities. Altogether, this study shows that bottom-up mechanistic modelling can identify parameters that influence the nitrification performance. Increasing inorganic carbon in BP could invert its rAOB/rNOB value, facilitating its transition to shortcut nitrogen removal.

Abstract: Artificial photosynthesis of H2O2, an environmentally friendly oxidant and a clean fuel, holds great promise. However, improving its efficiency and stability for industrial implementation remains highly challenging. Here, we report the visible-light H2O2 artificial photosynthesis by digging pro-superoxide radical carbon vacancies in three-dimensional hierarchical porous g-C3N4 through a simple hydrolysis-freeze-drying-thermal treatment. A significant electronic structure change is revealed upon the implantation of carbon vacancies, broadening visible-light absorption and facilitating the photogenerated charge separation. The strong electron affinity of the carbon vacancies promotes superoxide radical (O-center dot(2)-) formation, significantly boosting the H2O2 photocatalytic production. The developed photocatalyst shows an H2O2 evolution rate of 6287.5 mM g(-1) h(-1) under visible-light irradiation with a long cycling stability being the best-performing photocatalyst among all reported g-C3N4-based systems. Our work provides fundamental insight into highly active and stable photocatalysts with great potential for safe industrial H2O2 production.

Abstract: Photoactive nanoparticles and their light-driven applications have gained tremendous scientific attention towards remediation of the global environmental problems, meeting alternative energy demands, and other new technological discoveries. The research work presented in this dissertation includes a fundamental investigation of such nanoparticles to gain deeper insights that will ultimately benefit their application. In particular, the study of plasmonic metal nanoparticles and metal oxide nanoparticles for light driven applications is the major theme of this work. The investigation begins with isolated plasmonic Au and Ag nanoparticles, followed by a natural extension to nanoparticle clusters, and then further to nanoparticle films. Next, the application of such plasmonic nanoparticle films for gaseous phase sensing of volatile organic compounds is explored. Finally, the film formation of metal-oxide nanoparticles by self-assembly is investigated for the fabrication of photoactive functional interfaces. The fundamental theoretical investigation of the isolated plasmonic nanoparticles encompasses alloy and core-shell nanostructures of Au-Ag bimetallic compositions. First, the optical properties of bimetallic alloy and core-shell nanoparticles are compared for different structures such as nanospheres, nanotriangles and nanorods. Based on the optical properties, the photothermal properties of these nanostructures are also evaluated for relevant light-driven applications. Further, to bridge the gap between the theoretical and experimental optical properties of colloidal plasmonic nanoparticles, the effect of different statistical parameters pertaining to the particle size distribution is studied. Going from isolated nanoparticles to nanoparticle clusters, the changes in the optical properties of plasmonic nanoparticles when they form finite clusters is investigated. A strong effect of clustering on the absorption intensities of the nanoparticles and hence, on the photothermal properties is found. Next, for the study of plasmonic nanoparticle infinite arrays, Au and Ag nanoparticles films are experimentally obtained by the self-assembly at the air-ethylene glycol interface. Upon further validation of the computational models with the experimental optical properties of these films, the near-field and far-field optical response of the plasmonic nanoparticle arrays is investigated. An application of the self-assembled Au nanoparticle film is then demonstrated in the sensing of volatile organic compounds (VOCs). Finally, the focus is shifted from plasmonic nanoparticles to metal oxide nanoparticles for their self-assembly at the air-water interface to obtain self-assembled films. For this, the hydrophobic functionalization of four metal oxides nanoparticles namely, TiO2, ZnO, WO3 and CuO is investigated. The insights from this work is useful for the design and fabrication of functional nanoparticles and interfaces for light driven applications.

Abstract: Today’s society is increasingly challenged by a range of urgent environmental problems. Air pollution is one of these pressing topics. This thesis will mainly focus on the degradation of volatile organic compounds (VOCs) and particulate matter (PM) – more specifically soot. A second globally urging topic is the quest for sustainable energy production. To simultaneously target both environmental problems, a photoelectrochemical (PEC) cell will be studied in this thesis, combining air purification and sustainable energy production in a single device. Photocatalysis is used at the anode of the PEC cell to drive the air purification process, while the energy contained in the degraded compounds is (partially) recovered at the cathode, either as H2 gas or electricity. The first two experimental chapters focus on the proof of concept of such an unbiased all-gas phase PEC cell targeting VOC degradation, using both TiO2- and WO3-based photocatalysts. In the two following experimental chapters the photocatalytic soot oxidation capacity of these TiO2- and WO3-based photocatalysts was studied. In the final experimental chapter the previously obtained results were combined, striving towards an efficient, sunlight-driven and soot-degrading waste gas-to-energy PEC cell.

Abstract: Fibonacci numbers are a very popular subject in mathematics, culture and science. A major open question is why the ancient Greeks overlooked this series, while they were very familiar with the golden mean and division in extreme and mean ratio. Furthermore, they could compute the square root of five to a high degree of precision using Theon 's ladder. This fact is based on tables built with side and diagonal numbers, and it is a simple and incredibly efficient method to compute roots of integers, though it is little known even now among most of the experts. The biologist D 'Arcy Wentworth Thompson showed that the same method could be used to generate the Fibonacci series using a simple shift in the computation of the tables. He argues, quite convincingly, that the ancient Greeks could not have overlooked this. Actually, the same method can be used to generate all possible regular phyllotaxis patterns.

Abstract: Magnetic hyperthermia has the potential to play an important role in cancer therapy and its efficacy relies on the nanomaterials selected. Superparamagnetic iron oxide nanoparticles (SPIONs) are excellent candidates due to the ability of producing enough heat to kill tumor cells by thermal ablation. However, their heating properties depend strongly on crystalline structure and size, which may not be controlled and tuned during the synthetic process; therefore, a postprocessing is needed. We show how thermal annealing can be simultaneously coupled with ligand exchange to stabilize the SPIONs in polar solvents and to modify their crystal structure, which improves hyperthermia behavior. Using high-resolution transmission electron microscopy, X-ray diffraction, Mossbauer spectroscopy, vibrating sample magnetometry, and lock-in thermography, we systematically investigate the impact of size and ligand exchange procedure on crystallinity, their magnetism, and heating ability. We describe a valid and simple approach to optimize SPIONs for hyperthermia by carefully controlling the size, colloidal stability, and crystallinity.

Abstract: We present a computational imaging mode for large scale electron microscopy data, which retrieves a complex wave from noisy/sparse intensity recordings using a deep learning approach and subsequently reconstructs an image of the specimen from the Convolutional Neural Network (CNN) predicted exit waves. We demonstrate that an appropriate forward model in combination with open data frameworks can be used to generate large synthetic datasets for training. In combination with augmenting the data with Poisson noise corresponding to varying dose-values, we effectively eliminate overfitting issues. The U-NET[1] based architecture of the CNN is adapted to the task at hand and performs well while maintaining a relatively small size and fast performance. The validity of the approach is confirmed by comparing the reconstruction to well-established methods using simulated, as well as real electron microscopy data. The proposed method is shown to be effective particularly in the low dose range, evident by strong suppression of noise, good spatial resolution, and sensitivity to different atom types, enabling the simultaneous visualisation of light and heavy elements and making different atomic species distinguishable. Since the method acts on a very local scale and is comparatively fast it bears the potential to be used for near-real-time reconstruction during data acquisition.

Abstract: Fiber optic surface plasmon resonance (FO-SPR) has shown its potential for the detection of nucleic acids and more recently the technology has been combined with catalytic active strands such as DNAzymes. In this work, an innovative, generic solid-phase DNA sensor concept is presented, based on FO-SPR and PCR amplified DNAzyme activity. Improved levels of specificity and sensitivity were obtained down to picomolar concentrations. Moreover, the FO-SPR sensor concept enables AuNP amplified DNA target detection, independent of the target sequence length. The FO-SPR sensor was demonstrated for the screening of the mobile colistin resistance (MCR-2) gene, a gene important for the antimicrobial resistance in Gram-negative species such as E. Coli.