Abstract: An extensive CO2 plasmamodel is presented that is relevant for the production of ‘‘solar fuels.’’ It is based on reaction rate coefficients fromrigorously reviewed literature, and is augmented with reactionrate coefficients that are obtained fromscaling laws.The input data set,which is suitable for usage with the plasma simulation software Plasimo (https://plasimo.phys.tue.nl/), is available via the Plasimo and publisher’s websites.1 The correctness of this model implementation has been established by independent ZDPlasKin implementation (http://www.zdplaskin.
laplace.univ-tlse.fr/), to verify that the results agree. Results of these ‘‘global models’’ are presented for a DBD plasma reactor.

Abstract: The rapid advances in the field of cold plasma research led to the development of many plasma jets for various purposes. The COST plasma jet was created to set a comparison standard between different groups in Europe and the world. Its physical and chemical properties are well studied, and diagnostics procedures are developed and benchmarked using this jet. In recent years, it has been used for various research purposes. Here, we present a brief overview of the reported applications of the COST plasma jet. Additionally, we discuss the chemistry of the plasma-liquid systems with this plasma jet, and the properties that make it an indispensable system for plasma research.

Abstract: An Ar plasma sustained by a surfaguide wave launcher is investigated at intermediate pressure (200–2667 Pa). Two 2D self-consistent models (quasi-neutral and plasma bulk-sheath) are developed and benchmarked. The complete set of electromagnetic and fluid equations and the boundary conditions are presented. The transformation of fluid equations from a local reference frame, that is, moving with plasma or when the gas flow is zero, to a laboratory reference frame, that is,
accounting for the gas flow, is discussed. The pressure range is extended down to 80 Pa by experimental measurements. The electron temperature decreases with pressure. The electron density depends linearly on power, and changes its behavior with pressure depending on the product of pressure and radial plasma size.

Abstract: Plasma bioscience and medicine are both rapidly growing fields. Their aim is to utilize cold physical plasmas for desired biological outcomes in medicine, biotechnology, agriculture, and general hygienic purposes. Great success has been achieved in many applications with individually designed plasma sources and plasma parameters. Although lab and application-specific tuning of plasmas is a great advantage of this technology, standardized units to define plasma treatments are required to facilitate comparison of the effects found by different researchers who do not use the same plasma sources. By drawing conclusions from over a century of plasma biomedical research, we propose that all researchers adopt the use of a standardized value, the plasma treatment unit (PTU), to describe the biological effects of different cold plasma sources and treatment regimens. It quantifies a key plasma effector in biological systems as an indicator and may provide the foundation for an analogous and clinically relevant unit in the future.

Abstract: The behavior of nanoparticles in a low-pressure silane discharge is studied with the use of a self-consistent one-dimensional fluid model. Nanoparticles of a given (prescribed) radius are formed in the discharge by the incorporation of a dust growth mechanism, i.e., by including a step in which large anions (typically Si12H−25), produced in successive chemical reactions of anions with silane molecules, are transformed into particles. Typically a few thousand anions are used for one nanoparticle. The resulting particle density and the charge on the particles are calculated with an iterative method. While the spatial distribution and the charge of the particles are influenced by the plasma, the presence of the nanoparticles will in turn influence the plasma properties. Several simulations with different particle radii are performed. The resulting density profile of the dust will greatly depend on the particle size, as it reacts to the shift of the balance of the different forces acting on the particles.

Abstract: The conversion of methane to value-added chemicals and fuels is considered to be one of the challenges of the 21st century. In this paper we study, by means of fluid modeling, the conversion of methane to higher hydrocarbons or oxygenates by partial oxidation with CO2 or O2 in a dielectric barrier discharge. Sixty-nine different plasma species (electrons, ions, molecules, radicals) are included in the model, as well as a comprehensive set of chemical reactions. The calculation results presented in this paper include the conversion of the reactants and the yields of the reaction products as a function of residence time in the reactor, for different gas mixing ratios. Syngas (i.e. H2 + CO) and higher hydrocarbons (C2Hx) are typically found to be important reaction products.

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: A 2d3v Particle-in-cell/Monte Carlo collisions (PIC/MCC) model was constructed for an Ar/N2 reactive gas mixture in a magnetron discharge. A titanium target was used, in order to study the sputter deposition of a TiNx thin film. Cathode currents and voltages were calculated self-consistently and compared with experiments. Also, ion fluxes to the cathode were calculated, which cause sputtering of the target. The sputtered atom fluxes from the target, and to the substrate were calculated, in order to visualize the deposition of the TiNx film.