Abstract: The human population is projected to grow to 9.7 billion by 2050, resulting in an estimated increase in protein demand of 50%. From an environmental perspective, the current and future demand of protein cannot be sustainably met as the conventional food production chain is severely altering biogeochemical cycles of nitrogen and phosphorus, biodiversity and land-use, with flows towards the biosphere and oceans that are exceeding the planetary boundaries. Microbial protein (protein derived from microorganisms) has been suggested as an excellent sustainable protein source, a fortiori when produced in a land- and fossil free manner. The photoautohydrogenotrophic cultivation (i.e. with light, CO2 and H2) of purple bacteria links up perfectly with the upcoming green electrification of industry (green H2) and the need for carbon capture and utilization. However, this metabolism represented a gap in literature, and thus this thesis aimed to establish a basic knowledge platform on its kinetic, stoichiometric and nutritional performance. At first, three originally photoheterotrophically enriched purple bacteria were studied of which Rhodobacter capsulatus reached the highest protein productivity of 0.16 g protein/L/d, which aligned well with the commonly-known photoautotrophic microalgae. Moreover, a full dietary essential amino acid match was found for human food, while the fatty acid content was dominated by the health-stimulating vaccenic acid (82-86%). Lastly, the achieved protein yield in photoautohydrogenotrophic purple bacteria was 2.3 times higher compared to hydrogen oxidizing bacteria, indicating a resource-efficient use of H2. Next, a photoautohydrogenotrophic enrichment of wastewater treatment microbiomes was performed in search for specialist species. While the isolates of this enrichment showed improvements in their performance during acclimation, the kinetic and nutritional performance of Rhodobacter capsulatus still excelled. Subsequently, the influence of nutrient limitations (C or N) and nitrogen gas fixation was studied on the nutritional tuning potential. Both the limitations as well as the N2 fixation resulted in the shift of the essential amino acid profiles. Additionally, the limitations significantly decreased the pigment content, while an increase in the storage of poly-P was seen in case of carbon limitations. The next major challenge was the production intensification in a photobioreactor of which the design was linked to minimizing both H2 and light limitations. The chosen bubble-column photobioreactor already resulted in a doubled biomass productivity. Finally, the remaining technological and non-technological challenges ahead for the production of a high-value, cost-efficient, environment-friendly microbial protein that complies with legislative requirements and appeals to future consumers were discussed.

Abstract: Nanoscience and nanotechnologies are of immense importance across many fields of science and for numerous practical applications. In this context, scanning transmission electron microscopy (STEM) and 4D-STEM are among the most powerful characterization methods at the atomic scale. Annular dark-field (ADF)-STEM can be used to quantify atomic structures in 3D by counting atoms based on a single projection image. In 4D-STEM a full diffraction pattern is recorded at each scan step, which enables more dose efficient imaging and the utilization of various advanced imaging modalities, which can however be complex and slow. Both, STEM and 4D-STEM suffer from noise and distortions. In the first section of this work the most important of these distortions are discussed and it is shown how image restoration with a dedicated convolutional neural network (CNN) can be beneficial for atomic structure quantifications in ADF-STEM. In the second part, a new 4D-STEM imaging method real-time-integrated-centre-of-mass (riCOM) is introduced, which is a very dose-efficient and fast algorithm that enables unprecedented live-imaging capabilities for 4D-STEM. It is based on the integrated centre-of-mass approach, but is reformulated with variable integration ranges and optional filters, which allows for a tunable contrast transfer function. This enables the imaging of light and heavy elements simultaneously at very low doses. In the third part another new 4D-STEM method, coined AIRPI (AI-assisted rapid phase imaging) is introduced, which uses a CNN to retrieve a patch of the specimen's phase image for each scan position, based on the diffraction patterns in the probe's immediate surroundings. This allows also live imaging in principle and surpasses comparable state-of-the-art algorithms in terms of resolution also at low doses. Different atomic columns can be reliably distinguished over a wide range of atomic numbers, enabling a very good image interpretability. Further, AIRPI can recover low frequency image components, which preserves thickness information. This is a unique and important feature which could make quantitative 4D-STEM feasible.