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: We studied equally charged particles, suspended in a complex plasma, which move in a plane and interact with a screened Coulomb potential (Yukawa type) and with an additional external confining parabolic potential in one direction, which makes the system quasi-one-dimensional (Q1D). The normal modes of the system are studied in the presence of dissipation. We also investigated how a perpendicular magnetic field couples the phonon modes with each other. Two different ways of exciting the normal modes are discussed: (1) a uniform excitation of the Q1D lattice, and (2) a local forced excitation of the system in which one particle is driven by, e.g., a laser. Our results are in very good agreement with recent experimental findings on a finite single chain system [Liu , Phys. Rev. Lett. 91, 255003 (2003)]. Predictions are made for the normal modes of multichain structures in the presence of damping.

Abstract: This chapter discusses modeling efforts for plasma-based CO2 conversion, which are needed to obtain better insight in the underlying mechanisms, in order to improve this application. We will discuss two types of (complementary) modeling efforts that are most relevant, that is, (i) modeling of the detailed plasma chemistry by zero-dimensional (0D) chemical kinetic models and (ii) modeling of reactor design, by 2D or 3D fluid dynamics models. By showing some characteristic calculation results of both models, for CO2 splitting and in combination with a H-source, and for packed bed DBD and gliding arc plasma, we can illustrate the type of information they can provide.

Abstract: Plasma catalysis is gaining increasing interest for various applications, but the underlying mechanisms are still far from understood. Hence, more fundamental research is needed to understand these mechanisms. This can be obtained by both modelling and experiments. This foundations paper describes the fundamental insights in plasma catalysis, as well as efforts to gain more insights by modelling and experiments. Furthermore, it discusses the state-of-the-art of the major plasma catalysis applications, as well as successes and challenges of technology transfer of these applications.

Abstract: A chemical kinetics model was developed to characterise the gas‐phase dynamics of H₂production in nanosecond‐pulsed CH₄plasmas. Pulsed behaviour was observed in the calculated electric field, electron temperature and species densities at all pressures. The model agrees reasonably with experimental results, showing CH₄conversion at 30% and C₂H₂and H₂as major products. The underlying mechanisms in CH₄dissociation and H₂formation were analysed, highlighting the large contribution of vibrationally excited CH₄and H₂to coupling energy from the plasma into gas‐phase heating, and revealing that H₂synthesis is not affected by applied pressure, with selectivity remaining unchanged at ~42% in the 1–5 bar range.