Abstract: A one-dimensional fluid model for a dielectric barrier discharge in methane, used as a chemical reactor for gas conversion, is developed. The model describes the gas phase chemistry governing the conversion process of methane to higher hydrocarbons. The spatially averaged densities of the various plasma species as a function of time are discussed. Besides, the conversion of methane and the yields of the reaction products as a function of the residence time in the reactor are shown and compared with experimental data. Higher hydrocarbons (C2Hy and C3Hy) and hydrogen gas are typically found to be important reaction products. Furthermore, the main underlying reaction pathways are determined.

Abstract: This paper reports on the incorporation of three commercial fluorescent dyes, i.e., rhodamine 6G, fluorescein, and fluorescent brightener 184, in plasma coatings, by utilizing a dielectric barrier discharge (DBD) reactor, and the subsequent monitoring of the coatings homogeneity based on the emitted fluorescent light. The plasma coatings are qualitatively characterized with fluorescence microscopy, UVvis spectroscopy and profilometry for the determination of the coating thickness. The emitted fluorescent light of the coating correlates to the amount of dye per area, and deviations of these factors can hence be observed by monitoring the intensity of this light. This allows monitoring the homogeneity of the plasma coatings in a fast and simple way, without making major adjustments to the process.

Abstract: A crucial step, which is still not well understood in the destruction of volatile organic compounds (VOCs) with low temperature plasmas, is the initiation of the process. Here, we present a kinetic model for the destruction of ethylene in low temperature plasmas that allows us to calculate the relative importance of all plasma species and their related reactions. Modifying the ethylene concentration and/or the SED had a major impact on the relative importance of the radicals (i.e., mainly atomic oxygen) and the metastable nitrogen (i.e., more specifically N2(equation image)) in the destruction process. Our results show that the direct destruction by electron impact reactions for ethylene can be neglected; however, we can certainly not neglect the influence of N2(equation image)).

Abstract: Simulations and experiments are performed to obtain a better insight in the plasma enhanced chemical vapor deposition process of SiO2 by SiCl4/O2/Ar plasmas for introducing a SiO2-like coating in wafer processing reactors. Reaction sets describing the plasma and surface chemistry of the SiCl4/O2/Ar mixture are presented. Typical calculation results include the bulk plasma characteristics, i.e., electrical properties, species densities, and information on important production and loss processes, as well as the chemical composition of the deposited coating, and the thickness uniformity of the film on all reactor surfaces. The film deposition characteristics, and the trends for varying discharge conditions, are explained based on the plasma behavior, as calculated by the model.

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.

Abstract: In this review paper, we present several examples of reactive molecular dynamics simulations, which contribute to a better understanding of the underlying mechanisms in plasma medicine on the atomic scale. This includes the interaction of important reactive oxygen plasma species with the outer cell wall of both gram-positive and gram-negative bacteria, and with lipids present in human skin. Moreover, as most biomolecules are surrounded by a liquid biofilm, the behavior of these plasma species in a liquid (water) layer is presented as well. Finally, a perspective for future atomic scale modeling studies is given, in the field of plasma medicine in general, and for cancer treatment in particular.

Abstract: The catalytic oxidative dehydrogenation of hydrocarbons is an industrially important process, in which selectivity is a key issue. We here investigate the conversion of methanol to formaldehyde on a vanadia surface employing long timescale simulations, reaching a time scale of seconds. In particular, we compare the thermal process to the case where an additional external electric field is applied, as would be the case in a direct plasma-catalysis setup. We find that the electric field influences the retention time of the molecules at the catalyst surface. These simulations provide an atomic scale insight in the thermal catalytic oxidative dehydrogenation process, and in how an external electric field may affect this process.

Abstract: The energy dependence of a pulsed-dielectric barrier discharge (DBD) plasma treatment on chemical species production and biological responses was investigated. We hypothesized that the total plasma energy delivered during treatment encompasses the influence of major application parameters. A microsecond-pulsed DBD system was used to treat three different cancer cell lines and cell viability was analyzed. The energy per pulse was measured and the total plasma treatment energy was controlled by adjusting the pulse frequency, treatment time, and application distance. Our data suggest that the delivered plasma energy plays a predominant role in stimulating a biological response in vitro. This study aids in developing steps toward defining a plasma treatment unit and treatment dose for biomedical and clinical research.

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