Abstract: First-principles simulations were employed to gain atomistic insights on the working principles of amorphous HfO2 based Resistive Random Access Memory stack: the nature of the defect responsible for the switching between the High and Low Resistive States has been unambiguously identified to be the substoichiometric Hf sites (commonly called oxygen vacancy-V-O) and the kinetics of the process have been investigated through the study of O diffusion. Also the role of each material layer in the TiN/HfO2/Hf/TiN RRAM stack and the impact of the deposition techniques have been examined: metallic Hf sputtering is needed to provide an oxygen exchange layer that plays the role of defect buffer. TiN shall be a good defect barrier for O but a bad defect buffer layer. A possible scenario to explain the device degradation (switching failure) mechanism has been proposed – the relaxation of the metastable amorphous phase towards crystalline structure leads to denser, more structured cluster that can increase the defect migration barriers. (C) 2013 Elsevier B.V. All rights reserved.

Abstract: Plasma-liquid systems have attracted increasing attention in recent years, owing to their high potential in material processing and nanoscience, environmental remediation, sterilization, biomedicine, and food applications. Due to the multidisciplinary character of this scientific field and due to its broad range of established and promising applications, an updated overview is required, addressing the various applications of plasma-liquid systems till now. In the present review, after a brief historical introduction on this important research field, the authors aimed to bring together a wide range of applications of plasma-liquid systems, including nanomaterial processing, water analytical chemistry, water purification, plasma sterilization, plasma medicine, food preservation and agricultural processing, power transformers for high voltage switching, and polymer solution treatment. Although the general understanding of plasma-liquid interactions and their applications has grown significantly in recent decades, it is aimed here to give an updated overview on the possible applications of plasma-liquid systems. This review can be used as a guide for researchers from different fields to gain insight in the history and state-of-the-art of plasma-liquid interactions and to obtain an overview on the acquired knowledge in this field up to now.

Abstract: The adsorption of CO2 on surfaces of graphene and carbon nanotubes (CNTs), decorated with Fe atoms, are investigated using the self-consistent-charge density-functional tight-binding (SCC-DFTB) method, neglecting the heat effects. Fe ad-atoms are more stable when they are dispersed on hollow sites. They introduce a large density of states at the Fermi level (N-F); where keeping such density low would help in gas sensing. Furthermore, the Fe ad-atom can weaken the C=O double bonds of the chemisorbed CO2 molecule, paving the way for oxygen atoms to drain more charges from Fe. Consequently, chemisorption of CO2 molecules reduces both N-F and the conductance while it enhances the sensitivity with the increasing gas dose. Conducting armchair CNTs (ac-CNTs) have higher sensitivity than graphene and semiconducting zigzag CNTs (zz-CNT5). Comparative study of sensitivity of ac-CNT-Fe composite towards various gases (e.g., O-2, N-2, H-2, H2O, CO and CO2) has shown high sensitivity and selectivity towards CO, CO2 and H2O gases. (C) 2016 Elsevier B.V. All rights reserved.

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: 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: Plasma-based gas conversion has great potential for enabling carbon-free fertilizer production powered by renewable electricity. Sustaining an energy-efficient plasma process without eroding the containment vessel is currently a significant challenge, limiting scaling to higher powers and throughputs. Isolation of the plasma from contact with any solid surfaces is an advantage, which both limits energy loss to the walls and prevents material erosion that could lead to disastrous soil contamination. This paper presents highly energy-efficient nitrogen fixation from air into NOx by microwave plasma, with the plasma filament isolated at the center of a quartz tube using a vortex gas flow. NOx production is found to scale very efficiently when increasing both gas flow rate and absorbed power. The lowest energy cost recorded of ~2 MJ/mol, for a total NOx production of ~3.8%, is the lowest reported up to now for atmospheric pressure plasmas.

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: 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: 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.