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 treatment of a well plate by an atmospheric pressure plasma jet is common for<italic>in vitro</italic>plasma medicine research. Here, reactive species are largely produced through the mixing of the jet effluent with the surrounding atmosphere. This mixing can be influenced not only by the ambient conditions, but also by the geometry of the treated well. To limit this influence and control the atmosphere, a shielding gas is sometimes applied. However, the interplay between the gas shield and the well geometry has not been investigated. In this work, we developed a 2D-axisymmetric computational fluid dynamics model of the kINPen plasma jet, to study the mixing of the jet effluent with the surrounding atmosphere, with and without gas shield. Our computational and experimental results show that the choice of well type can have a significant influence on the effluent conditions, as well as on the effectiveness of the gas shield. Furthermore, the geometry of the shielding gas device can substantially influence the mixing as well. Our results provide a deeper understanding of how the choice of setup geometry can influence the plasma treatment, even when all other operating parameters are unchanged.

Abstract: The effect of Cu on the evolution of the Al3Zr phase in an Al-Cu-Zr cast alloy during solution treatment at 500 °C has been thoroughly studied by combining atomic resolution high-angle annular dark-field scanning transmission electron microscopy, energy-dispersive X-ray spectroscopy and first-principles cal- culations. The heat treatment initially produces a pure L12-Al3Zr microstructure, allowing for about 13 % Cu to be incorporated in the dispersoid. Cu incorporation increases the energy barrier for anti-phase boundary (APB) activation, thus stabilizing the L12 structure. Additional heating leads to a Cu-induced “branched”path for the L12 structural transformation, with the latter process accelerated once the first APB has been created. Cu atoms may either (i) be repelled by the APBs, promoting the transformation to a Cu-poor D023 phase, or (ii) they may segregate at one Al-Zr layer adjacent to the APB, promoting a transformation to a new thermodynamically favored phase, Al4CuZr, formed when these segregation layers are periodically arranged. Theoretical studies suggest that the branching of the L12 transformation path is linked to the speed at which an APB is created, with Cu attraction triggered by a comparatively slow process. This unexpected transformation behavior of the L12-Al3Zr phase opens a new path to understanding, and potentially regulating the Al3Zr dispersoid evolution for high temperature applications.

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: 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: 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: In this work we evaluate the chemical kinetics of dry reforming of methane in warm plasmas (1000–4000 K) using modelling with a newly developed chemistry set, for a broad range of parameters (temperature, power density and CO2/CH4 ratio). We compare the model against thermodynamic equilibrium concentrations, serving as validation of the thermal chemical kinetics. Our model reveals that plasma-specific reactions (i.e., electron impact collisions) accelerate the kinetics compared to thermal conversion, rather than altering the overall kinetics pathways and intermediate products, for gas temperatures below 2000 K. For higher temperatures, the kinetics are dominated by heavy species collisions and are strictly thermal, with negligible influence of the electrons and ions on the overall kinetics. When studying the effects of different gas mixtures on the kinetics, we identify important intermediate species, side reactions and side products. The use of excess CO2 leads to H2O formation, at the expense of H2 formation, and the CO2 conversion itself is limited, only approaching full conversion near 4000 K. In contrast, full conversion of both reactants is only kinetically limited for mixtures with excess CH4, which also gives rise to the formation of C2H2, alongside syngas. Within the given parameter space, our model predicts the 30/70 ratio of CO2/CH4 to be the most optimal for syngas formation with a H2/CO ratio of 2.

Abstract: In this paper, a methodology is presented to count the number of atoms in heterogeneous nanoparticles based on the combination of multiple annular dark field scanning transmission electron microscopy (ADF STEM) images. The different non-overlapping annular detector collection regions are selected based on the principles of optimal statistical experiment design for the atom-counting problem. To count the number of atoms, the total intensities of scattered electrons for each atomic column, the so-called scattering cross-sections, are simultaneously compared with simulated library values for the different detector regions by minimising the squared differences. The performance of the method is evaluated for simulated Ni@Pt and Au@Ag core-shell nanoparticles. Our approach turns out to be a dose efficient alternative for the investigation of beam-sensitive heterogeneous materials as compared to the combination of ADF STEM and energy dispersive X-ray spectroscopy.

Abstract: The electroreduction of carbon dioxide (eCO2RR) to CO using Ag nanoparticles as an electrocatalyst is promising as an industrial carbon capture and utilization (CCU) technique to mitigate CO2 emissions. Nevertheless, the long-term stability of these Ag nanoparticles has been insufficient despite initial high Faradaic efficiencies and/or partial current densities. To improve the stability, we evaluated an up-scalable and easily tunable synthesis route to deposit low-weight percentages of Ag nanoparticles (NPs) on and into the framework of a nitrogen-doped ordered mesoporous carbon (NOMC) structure. By exploiting this so-called nanoparticle confinement strategy, the nanoparticle mobility under operation is strongly reduced. As a result, particle detachment and agglomeration, two of the most pronounced electrocatalytic degradation mechanisms, are (partially) blocked and catalyst durability is improved. Several synthesis parameters, such as the anchoring agent, the weight percentage of Ag NPs, and the type of carbonaceous support material, were modified in a controlled manner to evaluate their respective impact on the overall electrochemical performance, with a strong emphasis on operational stability. The resulting powders were evaluated through electrochemical and physicochemical characterization methods, including X-ray diffraction (XRD), N2-physisorption, Inductively coupled plasma mass spectrometry (ICP-MS), scanning electron microscopy (SEM), SEM-energy-dispersive X-ray spectroscopy (SEM-EDS), high-angle annular dark field scanning transmission electron microscopy (HAADF-STEM), STEM-EDS, electron tomography, and X-ray photoelectron spectroscopy (XPS). The optimized Ag/soft-NOMC catalysts showed both a promising selectivity (∼80%) and stability compared with commercial Ag NPs while decreasing the loading of the transition metal by more than 50%. The stability of both the 5 and 10 wt % Ag/soft-NOMC catalysts showed considerable improvements by anchoring the Ag NPs on and into a NOMC framework, resulting in a 267% improvement in CO selectivity after 72 h (despite initial losses) compared to commercial Ag NPs. These results demonstrate the promising strategy of anchoring Ag NPs to improve the CO selectivity during prolonged experiments due to the reduced mobility of the Ag NPs and thus enhanced stability.

Abstract: Plasma catalysis is a rapidly growing field, often employing a packed-bed dielectric barrier discharge plasma reactor. Such dielectric barrier discharges are complex, especially when a packing material (e.g., a catalyst) is introduced in the discharge volume. Catalysts are known to affect the plasma discharge, though the underlying mechanisms influencing the plasma physics are not fully understood. Moreover, the effect of the catalysts on the plasma discharge and its subsequent effect on the overall performance is often overlooked. In this work, we deliberately design and synthesize catalysts to affect the plasma discharge in different ways. These Ni or Co alumina-based catalysts are used in plasma-catalytic dry reforming of methane and ammonia synthesis. Our work shows that introducing a metal to the dielectric packing can affect the plasma discharge, and that the distribution of the metal is crucial in this regard. Further, the altered discharge can greatly influence the overall performance. In an atmospheric pressure dielectric barrier discharge reactor, this apparently more uniform plasma yields a significantly better performance for ammonia synthesis compared to the more conventional filamentary discharge, while it underperforms in dry reforming of methane. This study stresses the importance of analyzing the plasma discharge in plasma catalysis experiments. We hope this work encourages a more critical view on the plasma discharge characteristics when studying various catalysts in a plasma reactor.

Abstract: The combination of plasma with catalysis for the synthesis and decomposition of NH3 is an attractive route to the production of carbon-neutral fertiliser and energy carriers and its conversion into H2. Recent years have seen fast developments in the field of plasma-catalytic NH3 life cycle. This work summarises the most recent advances in plasma-catalytic and related NH3-focussed processes, identifies some of the most important discoveries, and addresses plausible strategies for future developments in plasma-based NH3 technology.

Abstract: Activated carbon (AC) materials from renewable sources are widely used in electrochemical applications due to their well‐known high surface area. However, their application as electrode material in double‐layer electrochemical devices may be limited due to their relatively low electrical conductivity and lightweight. To overcome these limitations, the incorporation of pseudocapacitance metal oxide nanoparticles is an optimum approach. These nanoparticles can provide a second energy storage mechanism to the composite, mitigating the loss of surface area associated with their incorporation. As a result, the composite material is endowed with increased conductivity and higher density, making it more suitable for practical implementation in real devices. In this study, we have incorporated a fine dispersion of 1 % of MnO clusters into a highly porous activated carbon synthesized from walnut shells (WAC). The high‐resolution electron microscopy studies, combined with their related analytical techniques, allow us to determine the presence of the cluster within the matrix carbon precisely. The resulting MnO@WAC composite demonstrated significantly improved capacitive behavior compared with the WAC material, with increased volumetric capacitance and higher charge retention at higher current densities. The composite‘s electrochemical performance suggests its potential as a promising electrode material for supercapacitors, addressing drawbacks associated with traditional AC materials.

Abstract: Chirality in gold nanostructures offers an exciting opportunity to tune their differential optical response to left- and right-handed circularly polarized light, as well as their interactions with biomolecules and living matter. However, tuning and understanding such interactions demands quantification of the structural features that are responsible for the chiral behavior. Electron tomography (ET) enables structural characterization at the single-particle level and has been used to quantify the helicity of complex chiral nanorods. However, the technique is time-consuming and consequently lacks statistical value. To address this issue, we introduce herein a high-throughput methodology that combines images acquired by secondary electron-based electron beam-induced current (SEEBIC) with quantitative image analysis. As a result, the geometric chirality of hundreds of nanoparticles can be quantified in less than 1 h. When combining the drastic gain in data collection efficiency of SEEBIC with a limited number of ET data sets, a better understanding of how the chiral structure of individual chiral nanoparticles translates into the ensemble chiroptical response can be reached.

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.