Abstract: In this review, we discuss the progress of emerging dry processes for nanoscale fabrication. Experts in the fields of plasma processing have contributed to addressing the increasingly challenging demands in achieving atomic-level control of material selectivity and physicochemical reactions involving ion bombardment. The discussion encompasses major challenges shared across the plasma science and technology community. Focus is placed on advances in the development of fabrication technologies for emerging materials, especially metallic and intermetallic compounds and multiferroic, and two-dimensional (2D) materials, as well as state-of-the-art techniques used in nanoscale semiconductor manufacturing with a brief summary of future challenges.

Abstract: Neutron diffraction experiments conducted at 5 K in a magnetic field 0 < H/kOe < 50 have shown that the monoclinic perovskite La3Ni2TaO9 behaves as a relaxor ferromagnet. Compositions in the series La3Ni2SbxTayNb1–x–yO9 have been synthesized in polycrystalline form. Electron microscopy, X–ray diffraction and neutron diffraction have shown that the solid solutions are largely homogeneous and monophasic. Magnetometry and neutron diffraction have shown that the relaxor magnetisation persists in low fields when x + y = 1 but is rapidly diminished by the introduction of niobium. This change in magnetic behaviour is ascribed to the differences in the d–orbital energies of Sb5+, Nb5+ and Ta5+.

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: 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: 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: Agricultural cooperatives are seen as an efficient way for smallholder farmers to create bargaining power in order to achieve poverty reduction and food security. However, the success of these cooperatives depends on their ability to maintain their social capital, which is at the core of collective action. A few studies have addressed issues of member participation, commitment, and trust, yet less is known about rural cooperatives in developing countries as a social organization. It is also unclear whether a relationship exists between cooperative size and the incidence of conflict, fraud, and distrust. Using unique data collected from 511 agricultural cooperatives in 12 districts of Tigray region in northern Ethiopia, this paper examines the effects of cooperative size on conflict, fraud, and distrust. We used instrumental variables (IV) probit estimation techniques, accounting for endogeneity of membership size, to confirm that cooperative size does affect the occurrence of conflict, fraud, and trust. The results also indicate that other influencing factors include: cooperative age, number of employees, payment of dividends based on transaction volume, and heterogeneity of member goals.

Abstract: The properties of a capacitive HF discharge with growing nanoparticles are studied with the use of kinetic PIC-MCC simulation. At the initial stage of growth, the nanoparticles are shown to be localized at the interface between the near-electrode layer and quasi-neutral plasma, where the rate of ionization by electron impact has the maximum value. At the beginning of formation of particles, plasma parameters change rapidly and a transition between the capacitive and spatial discharge burning modes is observed for a certain critical size of the particles. If the growth of the dust particles continues, their distribution over the discharge becomes more uniform and the steady-state parameters of the gas-discharge plasma hardly change.

Abstract: We have developed a sustainable method to produce NH3 directly from air using a plasma tandem-electrocatalysis system that operates via the N2−NOx−NH3 pathway. To efficiently reduce NO2− to NH3, we propose a novel electrocatalyst consisting of defective N-doped molybdenum sulfide nanosheets on vertical graphene arrays (N-MoS2/VGs). We used a plasma engraving process to form the metallic 1T phase, N doping, and S vacancies in the electrocatalyst simultaneously. Our system exhibited a remarkable NH3 production rate of 7.3 mg h−1 cm−2 at −0.53 V vs RHE, which is almost 100 times higher than the state-of-the-art electrochemical nitrogen reduction reaction and more than double that of other hybrid systems. Moreover, a low energy consumption of only 2.4 MJ molNH3−1 was achieved in this study. Density functional theory calculations revealed that S vacancies and doped N atoms play a dominant role in the selective reduction of NO2− to NH3. This study opens up new avenues for efficient NH3 production using cascade systems.

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: CsPbI3 is a promising perovskite material for photovoltaic applications in its photoactive perovskite or black phase. However, the material degrades to a photovoltaically inactive or yellow phase at room temperature. Various mitigation strategies are currently being developed to increase the lifetime of the black phase, many of which rely on inducing strains in the material that hinder the black-to-yellow phase transition. Physical insight into how these strategies exactly induce strain as well as knowledge of the spatial extent over which these strains impact the material is crucial to optimize these approaches but is still lacking. Herein, we combine machine learning potential-based molecular dynamics simulations with our in silico strain engineering approach to accurately quantify strained large-scale atomic structures on a nanosecond time scale. To this end, we first model the strain fields introduced by atomic substitutions as they form the most elementary strain sources. We demonstrate that the magnitude of the induced strain fields decays exponentially with the distance from the strain source, following a decay rate that is largely independent of the specific substitution. Second, we show that the total strain field induced by multiple strain sources can be predicted to an excellent approximation by summing the strain fields of each individual source. Finally, through a case study, we illustrate how this additive character allows us to explain how complex strain fields, induced by spatially extended strain sources, can be predicted by adequately combining the strain fields caused by local strain sources. Hence, the strain additivity proposed here can be adopted to further our insight into the complex strain behavior in perovskites and to design strain from the atomic level onward to enhance their sought-after phase stability.

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.

Abstract: When dealing with Atmospheric Boundary Layer (ABL) simulations, commercial computational fluid dynamics (CFD) acquires a strategic resonance. Thanks to its good compromise between accuracy of results and calculation time, RANS still represents a valid alternative to more resource-demanding methods. However, focusing on the models' performances in urban studies, LES generally outmatches RANS results, even if the former is at least one order of magnitude more expensive. Consequently, the present work aims to propose a variety of approaches meant to solve some of the major problems linked to RANS simulations and to further improve its accuracy in typical urban contexts. All of these models are capable of switching from an undisturbed flux formulation to a disturbed one through a local deviation or a marker function. For undisturbed flows, a comprehensive approach is adopted, solving the issue of the erroneous stream-wise gradients affecting the turbulent profiles. Around obstacles, Non-Linear Eddy-Viscosity closures are adopted, due to their prominent capability in capturing the anisotropy of turbulence. The purpose of this work is then to propose a new Building Influence Area concept and to offer more affordable alternatives to LES simulations without sacrificing a good grade of accuracy.

Abstract: The cubic phase of HfO2 was stabilized by addition of yttrium in thin films grown on Si/SiO2 by metal-organic chemical vapor deposition. The cubic phase was obtained for contents of 6.5 at. % Y or higher at a temperature as low as 470 °C. The complete compositional range (from 1.5 to 99.5 at. % Y) was investigated. The crystalline structure of HfO2 was determined from x-ray diffraction, electron diffraction, and attenuated total-reflection infrared spectroscopy. For cubic films, the continuous increase in the lattice parameter indicates the formation of a solid-solution HfO2Y2O3. As shown by x-ray photoelectron spectroscopy, yttrium silicate is formed at the interface with silicon; the interfacial layer thickness increases with increasing yttrium content and increasing film thickness. The dependence of the intrinsic relative permittivity r as a function of Y content was determined. It exhibits a maximum of ~30 for ~8.8 at. % Y. The cubic phase is stable upon postdeposition high-temperature annealing at 900 °C under NH3.

Abstract: In traditional capacitively coupled plasmas, the discharge can be described by an electrostatic model, in which the Poisson equation is employed to determine the electrostatic electric field. However, current plasma reactors are much larger and driven at a much higher frequency. If the excitation wavelength k in the plasma becomes comparable to the electrode radius, and the plasma skin depth d becomes comparable to the electrode spacing, the electromagnetic (EM) effects will become significant and compromise the plasma uniformity. In this regime, capacitive discharges have to be described by an EM model, i.e., the full set of Maxwells equations should be solved to address the EM effects. This paper gives an overview of the theory, simulation and experiments that have recently been carried out to understand these effects, which cause major uniformity problems in plasma processing for microelectronics and flat panel display industries. Furthermore, some methods for improving the plasma uniformity are also described and compared.