Abstract: Research efforts on the description of the low-temperature magnetic order and electronic properties of U3O8 have been inconclusive so far. Reinterpreting neutron scattering results, we use group representation theory to show that the ground state presents collinear out-of-plane magnetic moments, with antiferromagnetic coupling both in-layer and between layers. Charge order relieves the initial geometric frustration, generating a slightly distorted honeycomb sublattice with Néel-type order. The precise knowledge of the characteristics of this magnetic ground state is then used to explain the fine features of the band gap. In this system, spin-orbit coupling (SOC) is of critical importance, as it strongly affects the electronic structure, narrowing the gap by ∼38%, compared to calculations neglecting SOC. The predicted electronic structure actually explains the salient features of recent optical absorption measurements, further demonstrating the excellent agreement between the calculated ground state properties and experiment.

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: The recent energy crisis revealed that there is a strong need to replace hydrocarbon-fueled industrial nitrogen fixation processes by alternative, more sustainable methods. In light of this, plasma-based nitrogen fixation remains one of the most promising options, considering both theoretical and experimental aspects. Lately, plasma interacting with water has received considerable attention in nitrogen fixation applications as it can trigger a unique gas- and liquid-phase chemistry. Within this context, a critical exploration of plasma-assisted nitrogen fixation with or without water presence is of great interest with an emphasis on energy costs, particularly in plasma reactors which have potential for large-scale industrial application. In this work, the presence of water in a multi-pin plasma system on nitrogen oxidation is experimentally investigated by comparing two pulsed negative DC voltage plasmas in metal–metal and metal–liquid electrode configurations. The plasma setups are designed to create similar plasma properties, including plasma power and discharge regime in both configurations. The system energy cost is calculated, considering nitrogen-containing species generated in gas and liquid phases as measured by a gas analyzer, nitrate sensor, and a colorimetry method. The energy cost profile as a function of specific energy input showed a strong dependency on the plasma operational frequency and the gas flow rate, as a result of different plasma operation regimes and initiated reverse processes. More importantly, the presence of the plasma/liquid interface increased the energy cost up to 14 ± 8%. Overall, the results showed that the presence of water in the reaction zone has a negative impact on the nitrogen fixation process.

Abstract: Plasma-catalytic dry reforming of CH4 (DRM) is promising to convert the greenhouse gasses CH4 and CO2 into value-added chemicals, thus simultaneously providing an alternative to fossil resources as feedstock for the chemical industry. However, while many experiments have been dedicated to plasma-catalytic DRM, there is no consensus yet in literature on the optimal choice of catalyst for targeted products, because the underlying mechanisms are far from understood. Indeed, plasma catalysis is very complex, as it encompasses various chemical and physical interactions between plasma and catalyst, which depend on many parameters. This complexity hampers the comparison of experimental results from different studies, which, in our opinion, is an important bottleneck in the further development of this promising research field. Hence, in this perspective paper, we describe the important physical and chemical effects that should be accounted for when designing plasma-catalytic experiments in general, highlighting the need for standardized experimental setups, as well as careful documentation of packing properties and reaction conditions, to further advance this research field. On the other hand, many parameters also create many windows of opportunity for further optimizing plasma-catalytic systems. Finally, various experiments also reveal the lack of improvement in plasma catalysis compared to plasma-only, specifically for DRM, but the underlying mechanisms are unclear. Therefore, we present our newly developed coupled plasma-surface kinetics model for DRM, to provide more insight in the underlying reasons. Our model illustrates that transition metal catalysts can adversely affect plasmacatalytic DRM, if radicals dominate the plasma-catalyst interactions. Thus, we demonstrate that a good understanding of the plasma-catalyst interactions is crucial to avoiding conditions at which these interactions negatively affect the results, and we provide some recommendations for improvement. For instance, we believe that plasma-catalytic DRM may benefit more from higher reaction temperatures, at which vibrational excitation can enhance the surface reactions.

Abstract: Chiral ligands are considered a required ingredient during the synthesis of dissymmetric plasmonic metal nanocrystals. The mechanism behind the generation of chiral structures involves the formation of high Miller index chiral facets, induced by the adsorption of such chiral ligands. We found however that, chirality can also evolve spontaneously, without the involvement of any chiral ligands, during the co-deposition of Au and Ag on Au nanorods. When using a specific Au/Ag ratio, phase segregation of the two metals leads to an interface within the obtained AuAg shell, which can be exposed by removing the Ag component via oxidative etching. Although a close-to-racemic mixture of chiral Au nanorods with right and left handedness is found in solution, electron tomography analysis evidences left- and righthanded helicities, both at the Au-Ag interface and at the exposed surface of Au NRs after Ag etching. The helicity profile of the NRs indicates dominating inclination angles in a range from 30° to 60°. Single-particle optical characterization also reveals random handedness in the plasmonic response of individual nanorods. We hypothesize that, the origin of chirality is related with symmetry breaking during the co-deposition of Au and Ag, through an initial perturbation in a small region on the Au-Ag interface that eventually leads to chiral segregation throughout the nanocrystal.

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: Fundamental and applied research on plasma‐treated liquids for biomedical applications was boosted in the last few years, dictated by their advantages with respect to direct treatments. However, often, the lack of consistent analysis at a molecular level of these liquids, and of the processes used to produce them, have raised doubts of their usefulness in the clinic. The aim of this article is to critically discuss some basic aspects related to the use of plasma‐treated liquids in medicine, with a focus on their chemical composition. We analyze the main liquids used in the field, how they are affected by non‐thermal plasmas, and the possibility to replicate them without plasma treatment.

Abstract: Electron energy loss spectroscopy (EELS) is a well established technique in electron microscopy that yields information on the elemental content of a sample in a very direct manner. One of the persisting limitations of EELS is the requirement for manual identification of core-loss edges and their corresponding elements. This can be especially bothersome in spectrum imaging, where a large amount of spectra are recorded when spatially scanning over a sample area. This paper introduces a synthetic dataset with 736,000 labeled EELS spectra, computed from available generalized oscillator strength tables, that represents 107 K, L, M or N core-loss edges and 80 chemical elements. Generic lifetime broadened peaks are used to mimic the fine structure due to band structure effects present in experimental core-loss edges. The proposed dataset is used to train and evaluate a series of neural network architectures, being a multilayer perceptron, a convolutional neural network, a U-Net, a residual neural network, a vision transformer and a compact convolutional transformer. An ensemble of neural networks is used to further increase performance. The ensemble network is used to demonstrate fully automated elemental mapping in a spectrum image, both by directly mapping the predicted elemental content and by using the predicted content as input for a physical model-based mapping.

Abstract: Advanced electron microscopy techniques, including scanning electron microscopes (SEM), scanning transmission electron microscopes (STEM), and transmission electron microscopes (TEM), have revolutionized imaging capabilities. However, achieving high-quality experimental images remains a challenge due to various distortions stemming from the instrumentation and external factors. These distortions, introduced at different stages of imaging, hinder the extraction of reliable quantitative insights. In this paper, we will discuss the main sources of distortion in TEM and S(T)EM images, develop models to describe them, and propose a method to correct these distortions using a convolutional neural network. We validate the effectiveness of our method on a range of simulated and experimental images, demonstrating its ability to significantly enhance the signal-to-noise ratio. This improvement leads to a more reliable extraction of quantitative structural information from the images. In summary, our findings offer a robust framework to enhance the quality of electron microscopy images, which in turn supports progress in structural analysis and quantification in materials science and biology.

Abstract: A chemical kinetics model was developed to characterise the gas‐phase dynamics of H₂production in nanosecond‐pulsed CH₄plasmas. Pulsed behaviour was observed in the calculated electric field, electron temperature and species densities at all pressures. The model agrees reasonably with experimental results, showing CH₄conversion at 30% and C₂H₂and H₂as major products. The underlying mechanisms in CH₄dissociation and H₂formation were analysed, highlighting the large contribution of vibrationally excited CH₄and H₂to coupling energy from the plasma into gas‐phase heating, and revealing that H₂synthesis is not affected by applied pressure, with selectivity remaining unchanged at ~42% in the 1–5 bar range.

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: To understand the structure–property relationship of nanostructures, reliably quantifying parameters, such as the number of atoms along the projection direction, is important. Advanced statistical methodologies have made it possible to count the number of atoms for monotype crystalline nanoparticles from a single ADF STEM image. Recent developments enable one to simultaneously acquire multiple ADF STEM images. Here, we present an extended statistics-based method for atom counting from a combination of multiple statistically independent ADF STEM images reconstructed from non-overlapping annular detector collection regions which improves the accuracy and allows one to retrieve precise atom-counts, especially for images acquired with low electron doses and multiple element structures.

Abstract: We report a plasma‐assisted CO₂hydrogenation to CH₃OH over Fe₂O₃/γ‐Al₂O₃catalysts, achieving 12% CO₂conversion and 58% CH₃OH selectivity at a temperature of nearly 80°C atm pressure. We investigated the effect of various supports and loadings of the Fe‐based catalysts, as well as optimized reaction conditions. We characterized catalysts by X‐ray powder diffraction (XRD), hydrogen temperature programmed reduction (H₂‐TPR), CO₂and CO temperature programmed desorption (CO₂/CO‐TPD), high‐resolution transmission electron microscopy (HRTEM), scanning transmission electron microscopy (STEM), x‐ray photoelectron spectroscopy (XPS), Mössbauer, and Fourier transform infrared<bold>(</bold>FTIR). The XPS results show that the enhanced CO₂conversion and CH₃OH selectivity are attributed to the chemisorbed oxygen species on Fe₂O₃/γ‐Al₂O₃. Furthermore, the diffuse reflectance infrared Fourier transform spectroscopy (DRIFTs) and TPD results illustrate that the catalysts with stronger CO₂adsorption capacity exhibit a higher reaction performance.<italic>In situ</italic>DRIFTS gain insight into the specific reaction pathways in the CO₂/H₂plasma. This study reveals the role of chemisorbed oxygen species as a key intermediate, and inspires to design highly efficient catalysts and expand the catalytic systems for CO₂hydrogenation to CH₃OH.

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.