Abstract: Electron tomography (ET) is a powerful tool to determine the three-dimensional (3D) structure of nanomaterials in a transmission electron microscope. However, the acquisition of a conventional tilt series for ET is a time-consuming process and can therefore not provide 3D structural information in a time-efficient manner. Here, we propose surface-sensitive secondary electron (SE) imaging as an alternative to ET for the investigation of the morphology of nanomaterials. We use the SE electron beam induced current (SEEBIC) technique that maps the electrical current arising from holes generated by the emission of SEs from the sample. SEEBIC imaging can provide valuable information on the sample morphology with high spatial resolution and significantly shorter throughput times compared with ET. In addition, we discuss the contrast formation mechanisms that aid in the interpretation of SEEBIC data.

Abstract: This study presents new insights on the effect of (sub)micrometer particle sized materials in plasma-based CO2-
CH4 reforming by investigating the performance of SiO2 spheres (with/without supported metal) of varying
particle sizes. (Sub)micron particles synthesized through the St¨ober method were used instead of (sub)millimeter
particles employed in previous studies. Increasing particle size (from 120 nm to 2390 nm) was found to first
increase and then decrease conversion and energy yield, with optimal performance achieved using 740 nm 5 wt%
Ni loaded SiO2, which improved CO2 and CH4 conversion, and energy yield to 44%, 55%, and 0.271 mmol/kJ,
respectively, compared to 20%, 27%, and 0.116 mmol/kJ in an empty reactor at the same flow rate. This is the
first to achieve significant performance improvement in a fully packed reactor, highlighting the importance of
selecting a suitable particle size. The findings can offer guidance towards rational design of catalysts for plasmabased
reactions.

Abstract: Herein, we describe for the first time the synthesis of the highly porous Hf-tetracarboxylate porphyrin-based metal-organic framework (MOF) (Hf)PCN-224(M) (M = H2, Co2+). (Hf)PCN-224(H2) was easily and efficiently prepared following a simple microwave-assisted procedure with good yields (56–67%; space-time yields: 1100–1270 kg m−3·day−1), high crystallinity and phase purity by using trifluoromethanesulfonic acid and benzoic acid as modulators in less than 30 min. By simply introducing a preliminary step (10 min), 5,10,15,20-(tetra-4-carboxyphenyl)porphyrin linker (TCPP) was quantitatively metalated with Co2+ without additional purification and/or time consuming protection/deprotection steps to further obtain (Hf)PCN-224(Co). (Hf)PCN-224(Co) was then tested as catalyst in CO2 cycloaddition reaction with different epoxides to yield cyclic carbonates, showing the best catalytic performance described to date compared to other PCNs, under mild conditions (1 bar CO2, room temperature, 18–24 h). Twelve epoxides were tested, obtaining from moderate to excellent conversions (35–96%). Moreover, this reaction was gram scaled-up (x50) without significant loss of yield to cyclic carbonates. (Hf)PCN-224(Co) maintained its integrity and crystallinity even after 8 consecutive runs, and poisoning was efficiently reverted by a simple thermal treatment (175 °C, 6 h), fully recovering the initial catalytic activity.

Abstract: e investigate atmospheric pressure microwave (MW) plasma (2.45 GHz) conversion in CO2 and CH4 mixtures (i.e., dry reforming of methane, DRM) focusing on reaction performance and carbon formation. Promising energy costs of ~2.8–3.0 eV/molecule or ~11.1–11.9 kJ/L are amongst the best performance to date considering the current state-of-the-art for plasma-based DRM for all types of plasma. The conversion is in the range of ~46–49% and ~55–67% for CO2 and CH4, respectively, producing primarily syngas (i.e., H2 and CO) with H2/CO ratios of ~0.6–1 at CH4 fractions ranging from 30% to 45%. Water is the largest byproduct with levels ranging ~7–14% in the exhaust. Carbon particles visibly impact the plasma at higher CH4 fractions (> 30%), where they become heated and incandescent. Particle luminosity increases with increasing CH4 fractions, with the plasma becoming unstable near a 1:1 mixture (i.e., > 45% CH4). Electron microscopy of the carbon material reveals an agglomerated morphology of pure carbon nanoparticles. The mean particle size is determined as ~20 nm, free of any metal contamination, consistent with the electrode-less MW design.

Abstract: Quantification of annular dark field (ADF) scanning transmission electron microscopy (STEM) images in terms
of composition or thickness often relies on probe-position integrated scattering cross sections (PPISCS). In
order to compare experimental PPISCS with theoretically predicted ones, expensive simulations are needed for
a given specimen, zone axis orientation, and a variety of microscope settings. The computation time of such
simulations can be in the order of hours using a single GPU card. ADF STEM simulations can be efficiently
parallelized using multiple GPUs, as the calculation of each pixel is independent of other pixels. However, most
research groups do not have the necessary hardware, and, in the best-case scenario, the simulation time will
only be reduced proportionally to the number of GPUs used. In this manuscript, we use a learning approach and
present a densely connected neural network that is able to perform real-time ADF STEM PPISCS predictions as
a function of atomic column thickness for most common face-centered cubic (fcc) crystals (i.e., Al, Cu, Pd, Ag,
Pt, Au and Pb) along [100] and [111] zone axis orientations, root-mean-square displacements, and microscope
parameters. The proposed architecture is parameter efficient and yields accurate predictions for the PPISCS
values for a wide range of input parameters that are commonly used for aberration-corrected transmission
electron microscopes.

Abstract: Hybrid pixel direct electron detectors are gaining popularity in electron microscopy due to their excellent properties. Some commercial cameras based on this technology are relatively affordable which makes them attractive tools for experimentation especially in combination with an SEM setup. To support this, a detector characterization (Modulation Transfer Function, Detective Quantum Efficiency) of an Advacam Minipix and Advacam Advapix detector in the 15–30 keV range was made. In the current work we present images of Point Spread Function, plots of MTF/DQE curves and values of DQE(0) for these detectors. At low beam currents, the silicon detector layer behaviour should be dominant, which could make these findings transferable to any other available detector based on either Medipix2, Timepix or Timepix3 provided the same detector layer is used.

Abstract: In the present work, a method was utilized to control the in‐situ partial hydrolysis of a diphosphonate ester in presence of a titania precursor and in function of acid content and its impact on the hybrid nanoparticles was assessed. The hydrolysis degree of organodiphosphonate ester linkers during the formation of hybrid organic‐inorganic metal oxide nanoparticles, are relatively underexplored . Quantitative solution NMR spectroscopy revealed that during the synthesis of TiO2 nanoparticles, an increase in acid concentration introduces a higher degree of partial hydrolysis of the TEPD linker into diverse acid/ester derivatives of TEPD. Increasing the HCl/Ti ratio from 1 to 3, resulted in an increase in degree of partial hydrolysis of the TEPD linker in solution from 4% to 18.8% under the here applied conditions. As a result of the difference in partial hydrolysis, the linker‐TiO2 bonding was altered. Upon subsequent drying of the colloidal TiO2 solution, different textures, at nanoscale and macroscopic scale, were obtained dependent on the HCl/Ti ratio and thus the degree of hydrolysis of TEPD. Understanding such linker‐TiO2 nanoparticle surface dynamics is crucial for making hybrid organic‐inorganic materials (i.e. (porous) metal phosphonates) employed in applications such as electronic/photonic devices, separation technology and heterogeneous catalysts.

Abstract: A facile, “one-pot”, chemical approach to synthesize gold-based nanoparticles finely dispersed on porous activated carbon (Norit) was demonstrated in this work. The pH of the synthesis bath played a critical role in determining the optimal gold-carbon interaction, which enabled a successful deposition of the gold nanoparticles onto the carbon matrix with a maximized metal utilization of 93 %. The obtained AuNP/C nanocomposite was characterized using SEM, HAADF-STEM electron tomography and electrochemical techniques. It was found that the Au nanoparticles, with diameters between 5 and 20 nm, were evenly distributed over the carbon matrix, both inside and outside the pores. Electrochemical characterization indicated that the composite had a very large electroactive surface area (EASA), as high as 282.4 m2 gAu-1. By exploiting its very high EASA, the catalyst was intended to boost the productivity of glucaric acid in the electrooxidation of its precursor, gluconic acid. However, cyclic voltammetry experiments revealed a very limited reactivity towards gluconic acid oxidation, due to the spacial hindrance of gluconic acid molecule which prevented diffusion inside the catalyst nanopores. On the other hand, the as-synthesized nanocomposite promises to be effective towards the ORR, and might thus find potential application as anode catalyst for fuel cells as well as for the scalability of all those electrochemical reactions involving small molecules with high diffusivity and catalysed by noble metals (i. e. CO2, CH4, N2, etc..). Electrocatalysis: Gold nanoparticles with diameter between 5 and 20 nm evenly distributed onto porous activated carbon (Norit) were obtained using a facile “one-pot” chemical synthesis technique with very high metal utilization. The AuNP/C nanocomposite was characterized using SEM, HAADF-STEM electron tomography and electrochemical techniques, revealing a very large electroactive surface area (EASA). The figure shows the HAADF-STEM image (a) and the respective EDX elemental distribution (b) for the AuNP/C composite with 9.3 % Au-loading developed in this work (Au is marked in red and C in green).image

Abstract: In this paper convexity constraints are derived for a background model of electron energy loss spectra (EELS) that is linear in the fitting parameters. The model outperforms a power-law both on experimental and simulated backgrounds, especially for wide energy ranges, and thus improves elemental quantification results. Owing to the model’s linearity, the constraints can be imposed through fitting by quadratic programming. This has important advantages over conventional nonlinear power-law fitting such as high speed and a guaranteed unique solution without need for initial parameters. As such, the need for user input is significantly reduced, which is essential for unsupervised treatment of large datasets. This is demonstrated on a demanding spectrum image of a semiconductor device sample with a high number of elements over a wide energy range.

Abstract: The bottom-up production of chiral gold nanomaterials holds great potential for the advancement of biosensing and nano-optics, among other applications. Reproducible preparations of colloidal nanomaterials with chiral morphology have been reported, using cosurfactants or chiral inducers such as thiolated amino acids. However, the underlying growth mechanisms for these nanomaterials remain insufficiently understood. We introduce herein a purposely devised chiral inducer, a cysteine modified with a hydrophobic chain, as a versatile chiral inducer. The amphiphilic and chiral features of this molecule provide control over the chiral morphology and the chiroptical signature of the obtained nanoparticles by simply varying the concentration of chiral inducer. These results are supported by circular dichroism and electromagnetic modeling as well as electron tomography to analyze structural evolution at the facet scale. Our observations suggest complex roles for the factors involved in chiral synthesis: the chemical nature of the chiral inducers and the influence of cosurfactants.

Abstract: Consortia of aerobic heterotrophic bacteria (AHB) are appealing as sustainable alternative protein ingredient for aquaculture given their high nutritional qualities, and their production potential on feed-grade industrial wastewater. Today, the impacts of pre-treatment, bioprocess choice and key parameter settings on AHB productivity and nutritional properties are unknown. This study investigated for the first time AHB microbial protein production effects based on (i) raw vs anaerobically fermented brewery wastewater, (ii) high-rate activated sludge (HRAS) without vs with feast-famine conditions, and (iii) three short solid retention time (SRT): 0.25, 0.50 and 1.00 d. High biomass (4.4–8.0 g TSS/L/d) and protein productivities (1.9–3.2 g protein/L/d) were obtained while achieving COD removal efficiencies up to 98 % at SRT 0.50 d. The AHB essential amino acid (EAA) profiles were above rainbow trout requirements, excluding the S-containing EAA, highlighting the AHB biomass replacement potential for unsustainable fishmeal in salmonid diets.

Abstract: Lignin determination in lignocellulose with the conventional two-step acid hydrolysis method is highly laborious and time-consuming. However, its quantification is crucial to monitor fungal pretreatment of wood, as the increase of acid-insoluble lignin (AIL) degradation linearly correlates with the achievable enzymatic saccharification yield. Therefore, in this study, a new attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy method was developed to track fungal delignification in an easy and rapid manner. Partial least square regression (PLSR) with cross-validation (CV) was applied to correlate the ATR-FTIR spectra with the AIL content (19.9%–27.1%). After variable selection and normalization, a PLSR model with a high coefficient of determination (

Abstract: The application of dielectric barrier discharge (DBD) plasma reactors is promising in various environmental and energy processes, but is limited by their low energy yield. In this study, we put a number of stainless steel rings over the inner electrode rod of the DBD reactor to change the local discharge gap and electric field, and we studied the dry reforming performance. At 50 W supplied power, the metal rings mostly have a negative impact on the performance, which we attribute to the non-uniform spatial distribution of the discharges caused by the rings. However, at 30 W supplied power, the energy yield is higher than at 50 W and the placement of the rings improves the performance of the reactor. More rings and with a larger cross-sectional diameter can further improve the performance. The reactor with 20 rings with a 3.2 mm cross-sectional diameter exhibits the best performance in this study. Compared to the reactor without rings, it increases the CO2 conversion from 7% to 16 %, the CH4 conversion from 12% to 23%, and the energy yield from 0.05 mmol/kJ supplied power to 0.1 mmol/kJ (0.19 mmol/kJ if calculated from the plasma power), respectively. The presence of the rings increases the local electric field, the displaced charge and the discharge fraction, and also makes the discharge more stable and with more uniform intensity. It also slightly improves the selectivity to syngas. The performance improvement observed by placing stainless steel rings in this study may also be applicable to other plasma-based processes.

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: Natural deep eutectic solvents (NADES) show great promise as media for enzymatic reactions in areas where (bio)compatibility with natural or medicinal products is a must. While in theory they can be tailored to the intended reaction to ensure optimized yields, the knowledge to date is predominantly empirical, with some mechanistic reports providing a fragmented view at best. Therefore, it is not easy to explain experimental observations, let alone make predictions. The aim of this study was to develop a structured, holistic understanding of the effects of NADES media on enzymatic reactions, distinguishing between effects on solubility, solvation, viscosity, inhibition and denaturation. Experimental and computational chemistry methods were combined to separately study the interactions between enzyme, substrate, and NADES as reaction media. The initial enzyme activity and the final conversion of vinyl laurate transesterification by immobilized Candida antarctica lipase were studied experimentally. The direct effect of NADES on the same enzyme was modeled by molecular dynamics simulation. The effect of solubility was studied by both experimental and computational methods. To predict the solubility and viscosity of NADES, data-driven models were developed by combining group contribution and machine learning methods, based on the accumulated experimental knowledge on NADES found in the literature. Finally, the composed relationships and prediction models were applied to the practical example of deacetylation of mannosylerythritol lipids (MELs). The experimental findings show that the chosen NADES system has a significant effect on both the apparent initial activity and the final conversion. However, in the simulations, the enzyme retains its original structure; moreover, NADES has an additional stabilizing effect on the enzyme. In addition, changes in the molar ratio of the compounds in NADES do not show a significant effect on the stability of the enzyme. These results indicate that the main effect of NADES on the reaction is mainly related to the substrate-solvent interactions (solvation energy) and the viscosity of the system. On the other hand, the experimental results only confirmed the significance of solvation, viscosity did not show a clear correlation with the studied reaction parameters. The machine learning models built for solubility and viscosity gave quantitative predictions of these properties. The accumulated knowledge was used to optimize the yield in the deacetylation reaction of MELs. The combination of these methods provides fundamental knowledge about the effect of NADES on biocatalysis, but the results are also applicable to other uses of NADES.