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: Yamuna River sediments are more enriched in metals than those of the Ganges and average Indian river sediments. Variation of metals in suspended, bed and core sediment are due to the varying proportions of grain size and mineral content. Iron, Mn and Pb show a preference for the oxide fraction, whereas Cu and Zn are predominant in organic and carbonate fractions of sediments. Of the total elemental content, 80% Mn, 78% Fe, 69% Pb, 67% Cu and 55% Zn are available in chemically mobile phases of the sediments. The high partition coefficient of metals with respect to Mn suggests similar chemical mobility and preferences for solid phases. River sediments in the vicinity of Delhi show an increase in sorption of metals downstream, consequently metals are retained in sediments. The high correlation coefficient and significant regression relation among the metals indicate their similar behaviour during transport. At Allahabad, the contribution of the Yamuna to the Ganges is 3200 t Pb year−1, 12 100 t Zn year−1 and 8500 t Cu year−1 in particulate form.

Abstract: X-ray radiographic images of paintings often show little or no contrast. In order to increase the contrast in radiographic images we measured the X-ray spectrum of a low power X-ray tube, after passing through the painting, with a high energy-resolution SDD detector. To obtain images, the detector is collimated with a 400 mu m diameter pinhole and the painting was moved through the beam in the x and y-direction using a dwell time of a few seconds per pixel. The data obtained consists of a data cube of, typically, 200 x 200 pixels and a 512-channel X-ray spectrum for each pixel, spanning the energy range from 0 to 40 keV. Having the absorbance spectrum available for each pixel, we are able, a posteriori, to produce images by edge subtraction for any given element. In this way high contrast, element-specific, images can be obtained. Because of the high energy-resolution a much simpler edge subtraction algorithm can be applied. We also used principal-component imaging to obtain, in a more automated way, images with high contrast. Some of these images can easily be attributed to specific elements. It turns out that preprocessing of the spectral data is crucial for the success of the multivariate image processing.

Abstract: With the rapid increase in the speed of new-generation aerospace vehicles, conventional heat-resistant aluminum alloys cannot meet the long-term service of the equipment. Therefore, the development of new high-strength heat-resistant aluminum alloys is of great strategic for the sustainable and high-quality development of industries. Al-Cu-Mg-Ag alloy is an age-hardenable heat-resistant aluminum alloy and has high strength and heat resistance. The addition of alloying elements such as Si and Sc to Al-Cu-Mg-Ag alloy introduces a competitive relationship among the σ-Al5Cu6Mg2, θ′-Al2Cu, and Ω phases. Therefore, a systematic investigation of precipitation behavior and heat resistance of Al-Cu-Mg-Ag-(Si) is essential for guiding the design of high-strength heat-resistant aluminum alloys. Combined characterization testing methods such as scanning electron microscopy, transmission electron microscopy, atom probe tomography, microhardness testing, and tensile testing with simulation calculation methods such as calculation of phase diagram, first-principles calculations, and Ab initio molecular dynamics, the effects of heat treatment processes and element content on the precipitation behavior, mechanical properties, and heat resistance of Al-Cu-Mg-Ag-(Si) alloys were systematically investigated. Furthermore, a multiple interface segregation structure was constructed at the θ′/Al interface, and a new Al-Cu-Mg-Ag-Si-Sc alloy with synergistically improved strength and heat resistance was developed. The main conclusions are as follows: (1) Based on the Kampmann-Wagner-Numerical theory, the relationship between the coarsening rate of the Ω phase and the aging process was analyzed, revealing for the first time that the critical size of Ω phase ( ) under thermal exposure temperature was the key factor determining the coarsening rate of Ω phase during long time thermal exposure heat treatment. After artificial ageing, when the size of Ω phase was smaller than the critical size , the dissolution of smaller Ω phase leaded to a rapid decrease in the number density of Ω phases, thereby reducing the heat resistance of the alloy. When the size of Ω phase was greater than or equal to the critical size , the coarsening rate of Ω phase was consistent, but a larger initial size would result in a larger final size after long-term thermal exposure. Therefore, the closer the size of Ω phase in the alloy is to the critical size under heat exposure temperature, the better the heat resistance of the alloy. (2) A concept of constructing a multiple interface segregation structure at the precipitate/matrix interface was proposed, and based on this concept, a multiple interface segregation structure containing the C/L-AlMgSiCu interfacial phase, newly discovered χ-AgMg interfacial phase, and Sc segregation layer was successfully constructed at the θ′/Al interface. The existence of the multiple interface segregation structure ensured that the designed Al-Cu-Mg-Ag-Si-Sc alloy maintains a yield strength of 400 MPa after thermal exposure at 200 C for 100 h, with a strength retention rate of 97%, creating a new record for the synergistic improvement of strength and heat resistance in aluminum alloys. In addition, combining transmission electron microscopy ex-situ/in-situ characterization with first-principles calculations, it is shown that the χ-AgMg interface phase will be destroyed due to the diffusion of the outer Ag layer during thermal exposure, and gradually dissolve into the matrix, but it can still delay the coarsening behavior of θ′-Al2Cu phase. (3) The criteria for determining whether Ω phase can precipitate are updated in Al-Cu-Mg-Ag-Si alloys with low Mg/Si ratio based on phase diagram thermodynamic calculations and multi-scale structural characterization. When W(Mg)/W(Si) > 1.4 and X(Ag)/X(Mgexcess) > 1, Ω phase can precipitate in Al-Cu-Mg-Ag-Si alloys, where X(Mgexcess) represents the atomic percentage of residual Mg elements after the formation of the AlMgSiCu quaternary precipitate phase C/L phase in the supersaturated solid solution, and the W(Mg) is the mass fraction of Mg in the supersaturated solid solution before artificial ageing. (4) The effects of alloy element content on precipitation behavior and heat resistance of Al-Cu-Mg-Ag-Si alloys were systematically analyzed. Critical conditions for the precipitation of σ-Al5Cu6Mg2 and Ω phase in Al-Cu-Mg-Ag-Si alloys are revealed. Based on calculation of phase diagram results, the conditions for precipitating σ-Al5Cu6Mg2 phase in the alloy are: ① W(Mg)/W(Si) > 1.8; ② W(Cu) > 2.7W(Mg) – 5W(Si). When W(Mg)/W(Si) < 1.8, the alloy is mainly precipitated with C/L/Q′-AlMgSiCu. When W(Cu) < 2.7W(Mg) – 5W(Si), the alloy will generate GPB zone. In addition, W(Ag)/W(Si) > 4 is the critical condition which the Ω phase can the main precipitates in Al-Cu-Mg-Ag-Si alloys. Furthermore, the correlation between precipitate types and heat resistance was summarized, showing that Al-Cu-Mg-Ag-(Si) alloys with Ω phase as the main strengthening phase are more suitable for the preparation of structures with short service time but high temperature, while Al-Cu-Mg-Ag-(Si) alloys with low Mg content and multiple segregation structures are more suitable for structures requiring long-term service at medium to high temperatures. This study, for the first time, combines calculation of phase diagram with multi-scale microstructure characterization, systematically unraveling the effects of element content on precipitation behavior, strength, and heat resistance of Al-Cu-Mg-Ag-(Si) alloys. In addition, a concept of constructing a multiple interface segregation structure at the precipitate/matrix interface was proposed to synergistically improve alloy strength and heat resistance. This work provides theoretical guidance for optimizing the composition and processing of Al-Cu-Mg-Ag-(Si) alloy and regulating the microstructure. Furthermore, it also offers new ideas and theoretical guidance for the development of novel high-strength heat-resistant alloys in other systems.

Abstract: While deammonification of high-strength wastewater in the sludge line of sewage treatment plants has become well established, the potential cost savings spur the development of this technology for mainstream applications. This study aimed at identifying the effect of aeration and organic carbon on the deammonification process. Two 10 L sequencing bath reactors with different aeration frequencies were operated at 25 degrees C. Real wastewater effluents from chemically enhanced primary treatment and high-rate activated sludge process were fed into the reactors with biodegradable chemical oxygen demand/nitrogen (bCOD/N) of 2.0 and 0.6, respectively. It was found that shorter aerobic solids retention time (SRT) and higher aeration frequency gave more advantages for aerobic ammonium-oxidizing bacteria (AerAOB) than nitrite oxidizing bacteria (NOB) in the system. From the kinetics study, it is shown that the affinity for oxygen is higher for NOB than for AerAOB, and higher dissolved oxygen set-point could decrease the affinity of both AerAOB and NOB communities. After 514 days of operation, it was concluded that lower organic carbon levels enhanced the activity of anoxic ammonium-oxidizing bacteria (AnAOB) over denitrifiers. As a result, the contribution of AnAOB to nitrogen removal increased from 40 to 70%. Overall, a reasonably good total removal efficiency of 66% was reached under a low bCOD/N ratio of 2.0 after adaptation.

Abstract: When macrophytes are growing in the river, the vegetation induces substantial changes to the water quality. Some effects are the result of direct interactions, such as photosynthetic activity or nutrient uptake, whereas others may be attributed to indirect effects of the water plants on hydrodynamics and river processes. This research focused on the direct effect of macrophytes on oxygen dynamics and nutrient cycling. Discharge, macrophyte biomass density, basic water quality, dissolved oxygen and nutrient concentrations were in situ monitored throughout the year in a lowland river (Nete catchment, Belgium). In addition, various processes were investigated in more detail in multiple ex situ experiments. The field and aquaria measurement results clearly demonstrated that aquatic plants can exert considerable impact on dissolved oxygen dynamics in a lowland river. When the river was dominated by macrophytes, dissolved oxygen concentrations varied from 5 to 10 mg l−1. Considering nutrient retention, it was shown that the investigated in-stream macrophytes could take up dissolved inorganic nitrogen (DIN) from the water column at rates of View the MathML source. And DIN fluxes towards the vegetation were found to vary from 0.03 to 0.19 g N ha−1 h−1 in spring and summer. Compared to the measured changes in DIN load over the river stretch, it means that about 313% of the DIN retention could be attributed to direct nitrogen uptake from the water by macrophytes. Yet, the role of macrophytes in rivers should not be underrated as aquatic vegetation also exerts considerable indirect effects that may have a greater impact than the direct fixation of nutrients into the plant biomass.

Abstract: When applying hydrological models, different sources of uncertainty are present, and evaluations of model performances should take these into account to assess model outcomes correctly. Furthermore, uncertainty in the discharge observations complicates the model identification, both in terms of model structure and parameterization. In this paper, the authors compare two different lumped model structures (PDM and NAM) considering uncertainty coming from the rating curve. Limits of acceptability for the model simulations were determined based on derived uncertainty bounds of the discharge observations. The authors applied the DYNamic Identifiability Approach (DYNIA) to identify structural failure of both models and to evaluate the configuration of their structures. In general, similar model performances are observed. However, the model structures tend to behave differently in the course of time, as revealed by the DYNIA approach. Based on the analyses performed, the probability based soil storage representation of the PDM model outperforms the NAM structure. The incorporation of the observation error did not prevent the DYNIA analysis to identify potential model structural deficiencies that are limiting the representation of the seasonal variation, primarily indicated by shifting regions of parameter identifiability. As such, the proposed approach is able to indicate where deficiencies are found and model improvement is needed.

Abstract: Wearable electrochemical sensors are driven by the user-friendly capability of continuous monitoring of key biomarkers for diagnostic or therapeutic operations. Particularly, microneedle (MN)-based sensors can access the interstitial fluid (ISF) in the dermis layer of skin to carry out on-body transdermal detection of analytes. Interestingly, 3D-printing technology allows for rapid and versatile prototyping reaching micrometer resolution. Herein, for the first time, we explore 3D-printed hollow MN patches (1 mm height x 1 mm base with 0.3 mm hole) which are modified with conductive inks to develop a potentiometric sensor for pH monitoring. First, the piercing capability of 3D-printed MN patches is demonstrated by using the parafilm model and their insertion in porcine skin. Subsequently, the hollow MNs are filled with conductive inks to engineer a set of microelectrodes. Thereafter, the working and reference electrodes are properly modified with polyaniline and polyvinyl butyral, respectively, toward a highly stable potentiometric cell. A full in vitro characterization is performed within a broad range of pH (i.e. pH 4 to pH 9). Besides, the MN sensor is analytically assessed in phantom gel and pierced on porcine skin to evaluate the resilience of the MN sensor. Finally, the MN sensor is pierced on the forearm of a subject and tested for its on-body monitoring capability. Overall, 3D-printed MN-based potentiometric sensing brings a versatile and affordable technology to minimally-invasively monitor key physiological parameters in the body.

Abstract: We theoretically investigate the effect of intercalation of third row transition metals (Co, Cr, Fe, Mn, Ti and V) in the layers of WSe2. Using density functional theory (DFT), we investigate the structural stability. We also compute the DFT energies of various magnetic spin configurations. Using these energies, we construct a Heisenberg Hamiltonian and perform a Monte Carlo study on each WSe2 + intercalant system to estimate the Curie or Neel temperature. We find ferromagnetic ground states for Ti and Cr intercalation, with Curie temperatures of 31K and 225K, respectively. In Fe-intercalated WSe2, we predict that antiferromagnetic ordering is present up to 564K. For V intercalation, we find that the system exhibits a double phase transition.

Abstract: We study the electronic spectral properties at zero temperature of the one-dimensional (1D) version of the degenerate two-orbital Kanamori-Hubbard model, one of the well-established frameworks to study transition metal compounds, using state-of-the-art numerical techniques based on the density matrix renormalization group. While the system is Mott insulating for the half-filled case, as expected for an interacting 1D system, we find interesting and rich structures in the single-particle density of states (DOS) for the hole-doped system. In particular, we find the existence of in-gap states which are pulled down to lower energies from the upper Hubbard band with increasing the interorbital Coulomb interaction V. We analyze the composition of the DOS by projecting it onto different local excitations, and we observe that for large dopings these in-gap excitations are formed mainly by interorbital holon-doublon (HD) states and their energies follow approximately the HD states in the atomic limit. We observe that the Hund interaction J increases the width of the in-gap band, as expected from the two-particle fluctuations in the Hamiltonian. The observation of a finite density of states within the gap between the Hubbard bands for this extended 1D model indicates that these systems present a rich excitation spectra which could help us understand the microscopic physics behind multiorbital compounds.

Abstract: We provide a comprehensive analysis of the prominent tight-binding (TB) models for transition metal dichalcogenides (TMDs) available in the literature. We inspect the construction of these TB models, discuss their parameterization used and conduct a thorough comparison of their effectiveness in capturing important electronic properties. Based on these insights, we propose a novel TB model for TMDs designed for enhanced computational efficiency. Utilizing MoS2 as a representative case, we explain why specific models offer a more accurate description. Our primary aim is to assist researchers in choosing the most appropriate TB model for their calculations on TMDs.

Abstract: We present the growth and optimization of ultralow loss Si-based Al2O3 planar waveguides, which have a high potential to boost the performance of rare-earth ion doped waveguide devices operating at visible and C-band wavelength ranges. The planar waveguide structures are grown using thermal atomic layer deposition. Systematic characterization of the obtained thin films is performed by spectroscopic ellipsometry, X-ray diffraction, Fourier transform infrared spectroscopy, and X-ray photoelectron spectroscopy analyses, and the optimum parameters are identified. The optical loss measurements for both transverse electric (TE) and transverse magnetic polarized light at 633, 829, and 1549 nm are performed. The lowest propagation loss value of 0.04 +/- 0.02 dB/cm for the Al2O3 waveguides for TE polarization at 1549 nm is demonstrated.

Abstract: We present a large dataset containing simulated core-loss electron energy loss spectroscopy (EELS) spectra with the elemental content as ground-truth labels. Additionally we present some neural networks trained on this data for element identification.  The simulated dataset contains zero padded core-loss spectra from 0 to 3072 eV, which represents 107 core-loss edges through all 80 elements from Be up to Bi. The core-loss edges are calculated from the generalised oscillator strength (GOS) database presented by Zhang et al.[1] Generic fine structures using lifetime broadened peaks are used to imitate fine structure due to solid-state effects in experimental spectra. Generic low-loss regions are used to imitate the effect of multiple scattering. Each spectrum contains at least one edge of a given query element and possibly additional edges depending on samples drawn from The Materials Project [2]. The dataset contains for each of the 80 elements: 7000 training spectra, 1500 test spectra, 600 validation spectra and 100 spectra representing only the query element. This results in a total 736 000 labeled spectra. Code on how to  – read the simulated data – transform HDF5 format to TFRecord format – train and evaluate neural networks using the simulated data – use the trained networks for automated element identification is available on GitHub at arnoannys/EELS_ID A full report on the simulation of the dataset and the training and evaluation of the neural networks can be found at:                    Annys, A., Jannis, D. & Verbeeck, J. Deep learning for automated materials characterisation in core-loss electron energy loss spectroscopy. Sci Rep 13, 13724 (2023). https://doi.org/10.1038/s41598-023-40943-7 [1] Zezhong Zhang, Ivan Lobato, Daen Jannis, Johan Verbeeck, Sandra Van Aert, & Peter Nellist. (2023). Generalised oscillator strength for core-shell electron excitation by fast electrons based on Dirac solutions (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7729585 [2] Anubhav Jain, Shyue Ping Ong, Geoffroy Hautier, Wei Chen, William Davidson Richards, Stephen Dacek, Shreyas Cholia, Dan Gunter, David Skinner, Gerbrand Ceder, Kristin A. Persson; Commentary: The Materials Project: A materials genome approach to accelerating materials innovation. APL Mater 1 July 2013; 1 (1): 011002. [https://doi.org/10.1063/1.4812323](https://doi.org/10.1063/1.4812323)

Abstract: We present a computational model to model the magnetic structure of two-dimensional (2D) dilute-magnetic-semiconductors (DMS) both the monolayers and multilayers using first-principles density functional theory (DFT), as well as their magnetic phase transition as a function of temperature using Monte-Carlo simulations. Using our method, we model the magnetic structure of bulk, bilayer, and monolayer MoS2 substitutionally doped with Fe atoms. We find that the out-of-plane interaction in bilayer MoS2 is weakly ferromagnetic, whereas in bulk MoS2 it is strongly anti-ferromagnetic. Finally, we show that the magnetic order is more robust in bilayer Fe-doped MoS2 compared to the monolayer and results in a room-temperature FM at an atomic substitution of 14-16%.

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.