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: Nowadays, 3D Electron Diffraction (3DED) is widely used for the structure determination of sub-micron-sized particles. In this work, we investigate the influence of the acceleration voltage on the quality of 3DED datasets acquired on BaTiO3 nanoparticles. Datasets were acquired using a wide range of beam energies, from common, high acceleration voltages (300 kV and 200 kV) to medium (120 kV and 80 kV) and low acceleration voltages (60 kV and 30 kV). In the integration process, Rint increases as the beam energy reduces, which is mainly due to the increased dynamical scattering. Nevertheless, the structure was solved successfully in all cases. The structure refinement was comparable for all beam energies with small deficiencies such as negative atomic displacements for the heaviest atom in the structure, barium. Including extinction correction in the refinement noticeably improved the model for low acceleration voltages, probably due to higher beam absorption in these cases. Dynamical refinement, however, shows superior results for higher acceleration voltages, since the dynamical refinement calculations currently discard inelastic scattering effects.

Abstract: The use of microfluidic devices in the field of plasma-liquid interaction can unlock unique possibilities to investigate the effects of plasma-generated reactive species for environmental and biomedical applications. So far, very little simulation work has been performed on microfluidic devices in contact with a plasma source. We report on the modelling and computational simulation of physical and chemical processes taking place in a novel plasma-microfluidic platform. The main production and transport pathways of reactive species both in plasma and liquid are modelled by a novel modelling approach that combines 0D chemical kinetics and 2D transport mechanisms. This combined approach, applicable to systems where the transport of chemical species occurs in unidirectional flows at high Péclet numbers, decreases calculation times considerably compared to regular 2D simulations. It takes advantage of the low computational time of the 0D reaction models while providing spatial information through multiple plug-flow simulations to yield a quasi-2D model. The gas and liquid flow profiles are simulated entirely in 2D, together with the chemical reactions and transport of key chemical species. The model correctly predicts increased transport of hydrogen peroxide into the liquid when the microfluidic opening is placed inside the plasma effluent region, as opposed to inside the plasma region itself. Furthermore, the modelled hydrogen peroxide production and transport in the microfluidic liquid differs by less than 50% compared with experimental results. To explain this discrepancy, the limits of the 0D–2D combined approach are discussed.

Abstract: A gliding arc (GA) plasma, operating at atmospheric pressure in a gas mixture of 50% N₂and 50% O₂, is studied using laser-induced fluorescence spectroscopy. The main goal is to determine the two-dimensional distribution of both the gas temperature and the NO ground state density in the afterglow. As GA plasma discharges at atmospheric pressure normally produce rather high NO_{<italic>x</italic>}densities, the high concentration of relevant absorbers, such as NO, may impose essential restrictions for the use of ‘classical’ laser-induced fluorescence methods (dealing with excitation in the bandhead vicinity), as the laser beam would be strongly absorbed along its propagation in the afterglow. Since this was indeed the case for the studied discharge, an approach dealing with laser-based excitation of separate rotational lines is proposed. In this case, due to a non-saturated absorption regime, simultaneous and reliable measurements of both the NO density and the gas temperature (using a reference fitting spectrum) are possible. The proposed method is applied to provide a two-dimensional map for both the NO density and the gas temperature at different plasma conditions. The results show that the input gas flow rate strongly alters the plasma shape, which appears as an elongated column at low input gas flow rate and spreads laterally as the flow rate increases. Finally, based on temperature map analysis, a clear correlation between the gas temperature and NO concentration is found. The proposed method may be interesting for the plasma-chemical analysis of discharges with high molecular production yields, where knowledge of both molecular concentration and gas temperature is required.

Abstract: Nanoscale extension and refinement of the Lucas-Washburn model is presented with a detailed analysis of recent experimental data and extensive molecular dynamics simulations to investigate rapid water flow and water imbibition within nanocapillaries. Through a comparative analysis of capillary rise in hydrophilic nanochannels, an unexpected reversal of the anticipated trend, with an abnormal peak, of imbibition length below the size of 3 nm was discovered in hydrophilic nanochannels, surprisingly sharing the same physical origin as the well-known peak observed in flow rate within hydrophobic nanochannels. The extended imbibition model is applicable across diverse spatiotemporal scales and validated against simulation results and existing experimental data for both hydrophilic and hydrophobic

Abstract: The basal plane of graphene can function as a selective barrier that is permeable to protons but impermeable to all ions and gases, stimulating its use in applications such as membranes, catalysis and isotope separation. Protons can chemically adsorb on graphene and hydrogenate it, inducing a conductor–insulator transition that has been explored intensively in graphene electronic devices. However, both processes face energy barriersand various strategies have been proposed to accelerate proton transport, for example by introducing vacancies, incorporating catalytic metalsor chemically functionalizing the lattice. But these techniques can compromise other properties, such as ion selectivity or mechanical stability. Here we show that independent control of the electric field,<italic>E</italic>, at around 1 V nm⁻¹, and charge-carrier density,<italic>n</italic>, at around 1 × 10¹⁴ cm⁻², in double-gated graphene allows the decoupling of proton transport from lattice hydrogenation and can thereby accelerate proton transport such that it approaches the limiting electrolyte current for our devices. Proton transport and hydrogenation can be driven selectively with precision and robustness, enabling proton-based logic and memory graphene devices that have on–off ratios spanning orders of magnitude. Our results show that field effects can accelerate and decouple electrochemical processes in double-gated 2D crystals and demonstrate the possibility of mapping such processes as a function of<italic>E</italic>and<italic>n</italic>, which is a new technique for the study of 2D electrode–electrolyte interfaces.

Abstract: Electron tomography has become a commonly used tool to investigate the three-dimensional (3D) structure of nanomaterials, including colloidal nanoparticle assemblies. However, electron microscopy is typically done under high-vacuum conditions, requiring sample preparation for assemblies obtained by wet colloid chemistry methods. This involves solvent evaporation and deposition on a solid support, which consistently alters the nanoparticle organization. Here, we suggest using electron tomography to study nanoparticle assemblies in their original colloidal liquid environment. To address the challenges related to electron tomography in liquid, we devise a method that combines fast data acquisition in a commercial liquid-cell with a dedicated alignment and reconstruction workflow. We present the advantages of this methodology in accurately characterizing two different systems. 3D reconstructions of assemblies comprising polystyrene-capped Au nanoparticles encapsulated in polymeric shells reveal less compact and more distorted configurations for experiments performed in a liquid medium compared to their dried counterparts. A similar expanded trend can be observed in quantitative analysis of the surface-to-surface distances of self-assembled Au nanorods in water rather than in a vacuum, which agrees with bulk measurements. This study, therefore, emphasizes the importance of developing high-resolution characterization tools that preserve the native environment of colloidal nanostructures.

Abstract: Metal halide perovskites (MHP) are highly promising semiconductors. In this study, we focus on FAPbBr3 nanocrystals, which are of great interest for green light-emitting diodes. Structural parameters significantly impact the properties of MHPs and are linked to phase instability, which hampers long-term applications. Clearly, there is a need for local and precise characterization techniques at the atomic scale, such as transmission electron microscopy. Because of the high electron beam sensitivity of MHPs, these investigations are extremely challenging. Here, we applied a low-dose method based on four-dimensional scanning transmission electron microscopy. We quantified the observed elongation of the projections of the Br atomic columns, suggesting an alternation in the position of the Br atoms perpendicular to the Pb–Br–Pb bonds. Together with molecular dynamics simulations, these results remarkably reveal local distortions in an on-average cubic structure. Additionally, this study provides an approach to prospectively investigating the fundamental degradation mechanisms of MHPs.

Abstract: In order to model correctly the corrosion of spent nuclear fuel under disposal conditions, it is important to understand its behavior in the presence of oxidants. To advance in this direction, we consider the oxidation of UO2. We investigate computationally the adsorption of various species on its three most stable surfaces: (111), (110), and (100), with emphasis on incorporating a full non-collinear PBE+U approach. Various species, namely O, O2, H2O and H2O2 are considered due to their relevance for the oxidation of UO2. The dissociation energy and an estimate for the dissociation barrier for O2 were obtained, using the preferred adsorption configurations of O and O2. The adsorption configurations for H2O in our study compare well with previous studies that used collinear approximations, both in terms of relative stability of configurations and bond lengths. Differences in adsorption energies were found, which may be important for reaction kinetics. Dissociative reactions in which the water molecule splits in hydrogen and hydroxyl occur only on one of the three surfaces. The hydrogen further reacts with a surface oxygen to also form a hydroxyl group. Not surprisingly, we find that H2O2 binds more strongly to the three surfaces than water (lower formation energy), and similar to H2O adsorption, dissociative reactions may occur. The dissociated hydrogen reacts with a surface oxygen to form a hydroxyl group and the hydroperoxyl molecule binds with a surface uranium. Our study, which includes a detailed study of electron transfer, magnetic structure and the preferred adsorption configurations, gives insight into the uranium oxidation states and the influence of surface geometry on adsorption. The findings contribute to a more comprehensive understanding of the early stages of UO2 oxidation.

Abstract: This study investigates the dry reformation of methane (DRM) over Ni/Al2O3 catalysts in a dielectric barrier discharge (DBD) non-thermal plasma reactor. A novel hybrid machine learning (ML) model is developed to optimize the plasma-catalytic DRM reaction with limited experimental data. To address the non-linear and complex nature of the plasma-catalytic DRM process, the hybrid ML model integrates three well-established algorithms: regression trees, support vector regression, and artificial neural networks. A genetic algorithm (GA) is then used to optimize the hyperparameters of each algorithm within the hybrid ML model. The ML model achieved excellent agreement with the experimental data, demonstrating its efficacy in accurately predicting and optimizing the DRM process. The model was subsequently used to investigate the impact of various operating parameters on the plasma-catalytic DRM performance. We found that the optimal discharge power (20 W), CO2/CH4 molar ratio (1.5), and Ni loading (7.8 wt%) resulted in the maximum energy yield at a total flow rate of
51 mL/min. Furthermore, we investigated the relative significance of each operating parameter on the performance of the plasmacatalytic DRM process. The results show that the total flow rate had the greatest influence on the conversion, with a significance exceeding 35% for each output, while the Ni loading had the least impact on the overall reaction performance. This hybrid model demonstrates a remarkable ability to extract valuable insights from limited datasets, enabling the development and optimization of more efficient and selective plasma-catalytic chemical processes.

Abstract: We explored the potential of plasma-based In-Situ Resource Utilization (ISRU) for Mars through the conversion of Martian atmosphere (~96% CO2, 2% N2, and 2% Ar) into life-sustaining chemicals. As the Martian surface pressure is about 1% of the Earth’s surface pressure, it is an ideal environment for plasma-based gas conversion using microwave reactors. At 1000 W and 10 Ln/min (normal liters per minute), we produced ~76 g/h of O2 and ~3 g/h of NOx using a 2.45 GHz waveguided reactor at 25 mbar, which is ~3.5 times Mars ambient pressure. The energy cost required to produce O2 was ~0.013 kWh/g, which is very promising compared to recently concluded MOXIE experiments on the Mars surface. This marks a crucial step towards realizing the extension of human exploration.

Abstract: Electrochemical reduction of CO2 (CO2RR) is expected to play a key role among the various strategies being explored to limit global warming. In this scenario, Layered Double Hydroxides (LDHs) are emerging as a promising class of electrocatalysts to replace the most used noble metals. In this work three Zn-Al LDH with different Zn2+/Al3+ ratio were synthesized and characterized by means of XRD, STEM-EDX and HR-TEM. Their suitability for CO2RR to CO was assessed by means of a custom-made three-compartment cell, showing an increase in CO selectivity by decreasing the Zn2+/Al3+ ratio. The CO2 interaction with the samples was firstly
characterized by means of volumetric adsorption measurements, exhibiting an increase in capture capacity by decreasing the Zn2+/Al3+ ratio. The evolution of the samples in interaction with a CO2-saturated liquid flow was then deeply investigated by means of in-situ ATR-IR spectroscopy. The samples displayed a different evolution in the vibrational region of the carbonate-like species (1800–1200 cm???? 1). To better discriminate the different carbonate cyclohexane was also employed. A definitive assignment of the main IR bands of the carbonate was
carried out by studying the spectral behavior of the different bands observed in the ATR-IR experiments and by comparing these results with the existing literature. Interestingly, Zn-Al 1:2 LDH, the most efficient electrocatalyst for CO2RR, is also the sole sample exhibiting a higher monodentate to total bidentate carbonates ratio, suggesting that the existence of a higher content of low coordination oxygen anions with stronger basic character can influence the final catalytic activity.

Abstract: This article discusses the ‘power-to-X’ (P2X) concept, highlighting the integral role of non-thermal plasma (NTP) in P2X for the eco-friendly production of chemicals and valuable fuels. NTP with unique thermally non-equilibrium characteristics, enables exotic reactions to occur under ambient conditions. This review summarizes the plasma-based P2X systems, including plasma discharges, reactor configurations, catalytic or non-catalytic processes, and modeling techniques. Especially, the potential of NTP to directly convert stable molecules including CO₂, CH₄and air/N₂is critically examined. Additionally, we further present and discuss hybrid technologies that integrate NTP with photocatalysis, electrocatalysis, and biocatalysis, broadening its applications in P2X. It concludes by identifying key challenges, such as high energy consumption, and calls for the outlook in plasma catalysis and complex reaction systems to generate valuable products efficiently and sustainably, and achieve the industrial viability of the proposed plasma P2X strategy.

Abstract: Density functional theory is widely used to gain insights into molecule−metal surface reaction networks, which is important for a better understanding of catalysis. However, it is well-known that generalized gradient approximation (GGA)
density functionals (DFs), most often used for the study of reaction networks, struggle to correctly describe both gas-phase molecules and metal surfaces. Also, GGA DFs typically underestimate reaction barriers due to an underestimation of the selfinteraction energy. Screened hybrid GGA DFs have been shown to reduce this problem but are currently intractable for wide usage. In this work, we use a more affordable meta-GGA (mGGA) DF in combination with a nonlocal correlation DF for the first time to study and gain new insights into a catalytically important surface
reaction network, namely, CO2 hydrogenation on Cu. We show that the mGGA DF used, namely, rMS-RPBEl-rVV10, outperforms typical GGA DFs by providing similar or better predictions for metals and molecules, as well as molecule−metal surface adsorption
and activation energies. Hence, it is a better choice for constructing molecule−metal surface reaction networks.