Abstract: For the energy industry, a process that is able to transform methane—being the prime component of natural gas—efficiently into a liquid product would be equivalent to a goose with golden eggs. As such it is no surprise that research efforts in this field already date back to the nineteen hundreds. Plasma technology can be considered to be a novel player in this field, but nevertheless one with great potential. Over the past decades this technology has evolved from sole hydrogen production, over indirect methane liquefaction to eventually direct plasma-assisted methane liquefaction processes. An overview of this evolution and these processes is presented, from which it becomes clear that the near future probably lies with the direct two phase plasma-assisted methane liquefaction and the far future with the direct oxidative methane liquefaction.

Abstract: above atmospheric) pressure regimes (1–3.5 bar). It was demonstrated that these operational conditions significantly influence both the discharge dynamics and the process efficiencies of O2 and CO2 discharges. For the case of the O2 DBD, the pressure rise results in the amplification of the discharge current, the appearance of emission lines of the metal electrode material (Fe, Cr, Ni) in the optical emission spectrum and the formation of a granular film of the erosion products (10–300 nm iron oxide nanoparticles) on the reactor walls. Somewhat similar behavior was observed also for the CO2 DBD. The discharge current, the relative intensity of the CO Angstrom band measured by Optical Emission Spectroscopy (OES) and the CO2 conversion rates could be stimulated to some extent by the rise in pressure. The optimal conditions for the O2 DBD (P = 2 bar) and the CO2 DBD (P = 1.5 bar) are demonstrated. It can be argued that the dynamics of the microdischarges (MD) define the underlying process of this behavior. It could be
demonstrated that the pressure increase stimulates the formation of more intensive but fewer MDs. In this way, the operating pressure can represent an additional tool to manipulate the properties of the MDs in a DBD, and as a result also the discharge performance.

Abstract: Electrospinning is a versatile technique for the fabrication of polymer-based nano/microfibers. Both physical and chemical characteristics of pre-electrospinning polymer solutions affect the morphology and chemistry of electrospun nanofibers. An atmospheric-pressure plasma jet has previously been shown to induce physical modifications in polylactic acid (PLA) solutions. This work aims at investigating the plasma-induced chemistry in organic solutions of PLA, and their effects on the resultant PLA nanofibers. Therefore, very broad range of gas, liquid, and solid (nanofiber) analyzing techniques has been applied. Plasma alters the acidity of the solutions. SEM studies illustrated that complete fiber morphology enhancement only occurred when both PLA and solvent molecules were exposed to preelectrospinning plasma treatment.
Additionally, the surface
chemistry of the PLA nanofibers
was mostly preserved.

Abstract: Climate change, environmental pollution control, and resource utilization efficiency, as well as food security, sustainable agriculture, and water supply are among the main challenges facing society today. Expertise across different academic fields, technologies,anddisciplinesisneededtogeneratenewideastomeetthesechallenges. This “white paper” aims to provide a written summary by describing the main aspects and possibilities of the technology. It shows that plasma science and technology can make significant contributions to address the mentioned issues. The paper also addresses to people in the scientific community (inside and outside plasma science) to give inspiration for further work in these fields.

Abstract: The penetration of reactive oxygen and nitrogen species (RONS) across the stratum corneum (SC) is a necessary and crucial process in many skin‐related plasma medical applications. To gain more insights into this penetration behavior, we combined experimental measurements of the permeability of dry and moist SC layers with computer simulations of model lipid membranes. We measured the permeation of relatively stable molecules, which are typically generated by plasma, namely H2O2, NO3−, and NO2−. Furthermore, we calculated the permeation free energy profiles of the major plasma‐generated RONS and their derivatives (i.e., H2O2, OH, HO2, O2, O3, NO, NO2, N2O4, HNO2, HNO3, NO2−, and NO3−) across native and oxidized SC lipid bilayers, to understand the mechanisms of RONS permeation across the SC. Our results indicate that hydrophobic RONS (i.e., NO, NO2, O2, O3, and N2O4) can translocate more easily across the SC lipid bilayer than hydrophilic RONS (i.e., H2O2, OH, HO2, HNO2, and HNO3) and ions (i.e., NO2− and NO3−) that experience much higher permeation barriers. The permeability of RONS through the SC skin lipids is enhanced when the skin is moist and the lipids are oxidized. These findings may help to understand the underlying mechanisms of plasma interaction with a biomaterial and to optimize the environmental parameters in practice in plasma medical applications.

Abstract: Cold atmospheric plasma (CAP) is a promising technology for several medical applications, including the removal of biofilms from surfaces. However, the molecular mechanisms of CAP treatment are still poorly understood. Here we unravel the possible mechanisms of CAP‐induced oxidation of oligosaccharides, employing reactive molecular dynamics simulations based on the density functional‐tight binding potential. Specifically, we find that the interaction of oxygen atoms (used as CAP‐generated reactive species) with cellotriose (a model system for the oligosaccharides) can break structurally important glycosidic bonds, which subsequently leads to the disruption of the oligosaccharide molecule. The overall results help to shed light on our experimental evidence for cellotriose CAP. This oxidation by study provides atomic‐level insight into the onset of plasma‐induced removal of biofilms, as oligosaccharides are one of the main components of biofilm.

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: Plasma medicine has attracted tremendous interest in a variety of medical conditions, ranging from wound healing to antimicrobial applications, even in cancer treatment, through the interactions of cold atmospheric plasma (CAP) and various biological tissues directly or indirectly. The underlying mechanisms of CAP treatment are still poorly understood although the oxidative effects of CAP with amino acids, peptides, and proteins have been explored experimentally. In this study, machine learning (ML) technology is introduced to efficiently unveil the interaction mechanisms of amino acids and reactive oxygen species (ROS) in seconds based on the data obtained from the reactive molecular dynamics (MD) simulations, which are performed to probe the interaction of five types of amino acids with various ROS on the timescale of hundreds of picoseconds but with the huge computational load of several days. The oxidative reactions typically start with H-abstraction, and the details of the breaking and formation of chemical bonds are revealed; the modification types, such as nitrosylation, hydroxylation, and carbonylation, can be observed. The dose effects of ROS are also investigated by varying the number of ROS in the simulation box, indicating agreement with the experimental observation. To overcome the limits of timescales and the size of molecular systems in reactive MD simulations, a deep neural network (DNN) with five hidden layers is constructed according to the reaction data and employed to predict the type of oxidative modification and the probability of occurrence only in seconds as the dose of ROS varies. The well-trained DNN can effectively and accurately predict the oxidative processes and productions, which greatly improves the computational efficiency by almost ten orders of magnitude compared with the reactive MD simulation. This study shows the great potential of ML technology to efficiently unveil the underpinning mechanisms in plasma medicine based on the data from reactive MD simulations or experimental measurements. In this study, since reactive molecular dynamics simulation can currently only describe interactions between a few hundred atoms in a few hundred picoseconds, deep neural networks (DNN) are introduced to enhance the simulation results by predicting more data efficiently. image

Abstract: <script type='text/javascript'>document.write(unpmarked('The resistive random-access memory (RRAM) device concept is close to enabling the development of a new generation of non-volatile memories, provided that their reliability issues are properly understood. The design of a RRAM operating with extrinsic defects based on metallic inclusions, also called conductive bridge RAM, allows the use of a large spectrum of solid electrolytes. However, when scaled to device dimensions that meet the requirements of the latest technological nodes, the discrete nature of the atomic structure of the materials impacts the device operation. Using density functional theory simulations, we evaluated the migration kinetics of Cu conducting species in amorphous and solid electrolyte materials, and established that atomic disorder leads to a large variability in terms of defect stability and kinetic barriers. This variability has a significant impact on the filament resistance and its dynamics, as evidenced during the formation step of the resistive filament. Also, the atomic configuration of the formed filament can age/relax to another metastable atomic configuration, and lead to a modulation of the resistivity of the filament. All these observations are qualitatively explained on the basis of the computed statistical distributions of the defect stability and on the kinetic barriers encountered in RRAM materials.'));

Abstract: Magnetic Pole Enhanced-Inductively Coupled Plasmas (MaPE-ICPs) in analogy to the conventional ICPs exhibit two modes of operation, depending on the power coupling mechanism, i.e., a low power mode with dominant capacitive coupling (E-mode) and a high power mode with dominant inductive coupling (H-mode). A comparative study of the electron temperature measured by a Langmuir probe (T-e(LP)) and the electron excitation temperature (T-exc(OES)) determined by Optical Emission Spectroscopy (OES) is reported in the two distinct modes of a MaPE-ICP operated in argon. The dependence of T-e(LP), T-exc(OES) and their ratio (T-e(LP)/T-exc(OES)) on applied power (5-50 W) and gas pressure (15-60 mTorr) is explored, and the validity of T-exc(OES) as an alternative diagnostic to T-e(LP) is tested in the two modes of MaPE-ICP. The OES based non-invasive measurement of the plasma parameters such as electron temperature is very useful for plasma processing applications in which probe measurements are limited. (C) 2013 Elsevier B. V. All rights reserved.

Abstract: The hyperdynamics method is a powerful tool to simulate slow processes at the atomic level. However, the construction of an optimal hyperdynamics potential is a task that is far from trivial. Here, we propose a generally applicable implementation of the hyperdynamics algorithm, borrowing two concepts from metadynamics. First, the use of a collective variable (CV) to represent the accelerated dynamics gives the method a very large flexibility and simplicity. Second, a metadynamics procedure can be used to construct a suitable history-dependent bias potential on-the-fly, effectively turning the algorithm into a self-learning accelerated molecular dynamics method. This collective variable-driven hyperdynamics (CVHD) method has a modular design: both the local system properties on which the bias is based, as well as the characteristics of the biasing method itself, can be chosen to match the needs of the considered system. As a result, system-specific details are abstracted from the biasing algorithm itself, making it extremely versatile and transparent. The method is tested on three model systems: diffusion on the Cu(001) surface and nickel-catalyzed methane decomposition, as examples of reactive processes with a bond-length-based CV, and the folding of a long polymer-like chain, using a set of dihedral angles as a CV. Boost factors up to 109, corresponding to a time scale of seconds, could be obtained while still accurately reproducing correct dynamics.

Abstract: We introduce a simple enhanced sampling approach for the calculation of free energy differences and barriers along a one-dimensional reaction coordinate. First, a small number of short nonequilibrium simulations are carried out along the reaction coordinate, and the Jarzynski equality is used to learn an approximate free energy surface from the nonequilibrium work distribution. This free energy estimate is represented in a compact form as an artificial neural network and used as an external bias potential to accelerate rare events in a subsequent molecular dynamics simulation. The final free energy estimate is then obtained by reweighting the equilibrium probability distribution of the reaction coordinate sampled under the influence of the external bias. We apply our reweighted Jarzynski sampling recipe to four processes of varying scales and complexities.spanning chemical reaction in the gas phase, pair association in solution, and droplet nucleation in supersaturated vapor. In all cases, we find reweighted Jarzynski sampling to be a very efficient strategy, resulting in rapid convergence of the free energy to high precision.

Abstract: Binding of the SARS-CoV-2 S-glycoprotein to cell receptors is vital for the entry of the virus into cells and subsequent infection. ACE2 is the main cell receptor for SARS-CoV-2, which can attach to the C-terminal receptor-binding domain (RBD) of the SARS-CoV-2 S-glycoprotein. The GRP78 receptor plays an anchoring role, which attaches to the RBD and increases the chance of other RBDs binding to ACE2. Although high levels of reactive oxygen and nitrogen species (RONS) are produced during viral infections, it is not clear how they affect the RBD structure and its binding to ACE2 and GRP78. In this research, we apply molecular dynamics simulations to study the effect of oxidation of the highly reactive cysteine (Cys) amino acids of the RBD on its binding to ACE2 and GRP78. The interaction energy of both ACE2 and GRP78 with the whole RBD, as well as with the RBD main regions, is compared in both the native and oxidized RBDs. Our results show that the interaction energy between the oxidized RBD and ACE2 is strengthened by 155 kJ/mol, increasing the binding of the RBD to ACE2 after oxidation. In addition, the interaction energy between the RBD and GRP78 is slightly increased by 8 kJ/mol after oxidation, but this difference is not significant. Overall, these findings highlight the role of RONS in the binding of the SARS-CoV-2 S-glycoprotein to host cell receptors and suggest an alternative mechanism by which RONS could modulate the entrance of viral particles into the cells.