Abstract: Non-equilibrium inductively coupled plasmas (ICPs) operating in hydrogen are of significant interest for applications including large-area materials processing. Increasing control of spatial gas heating, which drives the formation of neutral species density gradients and the rate of gas-temperature-dependent reactions, is critical. In this study, we use 2D fluid-kinetic simulations with the Hybrid Plasma Equipment Model to investigate the spatially resolved production of atomic hydrogen in a low-pressure planar ICP operating in pure hydrogen (10-20 Pa or 0.075-0.15 Torr, 300 W). The reaction set incorporates self-consistent calculation of the spatially resolved gas temperature and 14 vibrationally excited states. We find that the formation of neutral-gas density gradients, which result from spatially non-uniform electrical power deposition at constant pressure, can drive significant variations in the vibrational distribution function and density of atomic hydrogen when gas heating is spatially resolved. This highlights the significance of spatial gas heating on the production of reactive species in relatively high-power-density plasma processing sources.

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: The global relaxation process after pulsed laser induced photodetachment in a rf electronegative SIH4 discharge is studied by a self-consistent kinetic one-dimensional particle-in-cell-Monte Carlo model. Our results reveal a comprehensive physical picture of the relaxation process, including the main plasma variables, after a perturbation up to the full recovery of the steady state. A strong influence of the photodetachment on the discharge is found, which results from an increase of the electron density, leading to a weaker bulk field, and hence to a drop in the high energy tail of the electron energy distribution function (EEDF), a reduction of the reaction rates of electron impact attachment and ionization, and a subsequent decrease of the positive and negative ion densities. All the plasma quantities related to electrons recover synchronously. The recovery time of the ion densities is about 1-2 orders of magnitude longer than that of the electrons due to different recovery mechanisms. The modeled behavior of all the charged particles agrees very well with experimental results from the literature. In addition, our work clarifies some unclear processes assumed in the literature, such as the relaxation of the EEDF, the evolution of the electric field, and the recovery of negative ions.

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: In dc magnetrons the electrons emitted from the cathode may return there due to the applied magnetic field. When that happens, they can be recaptured or reflected back into the discharge, depending on the value of the reflection coefficient (RC). A 2d3v (two-dimensional in coordinate and three-dimensional in velocity space) particle-in-cellMonte Carlo model, including an external circuit, is developed to determine the role of the electron recapture in the discharge processes. The detailed discharge structure as a function of RC for two pressures (4 and 25mtorr) is studied. The importance of electron recapture is clearly manifested, especially at low pressures. The results indicate that the discharge characteristics are dramatically changed with varying RC between 0 and 1. Thus, the electron recapture at the cathode appears to be a significant mechanism in magnetron discharges and RC a very important parameter in their correct quantitative description that should be dealt with cautiously.

Abstract: The initial stage of nanoparticle formation and growth in radiofrequency acetylene (C2H2) plasmas is investigated by means of a self-consistent one-dimensional fluid model. A detailed chemical kinetic scheme, containing electron impact, ion-neutral, and neutral-neutral reactions, has been developed in order to predict the underlying dust growth mechanisms and the most important dust precursors. The model considers 41 different species (neutrals, radicals, ions, and electrons) describing hydrocarbons (CnHm) containing up to 12 carbon atoms. Possible routes for particle growth are discussed. Both positive and negative ion reaction pathways are considered, as consecutive anion- and cation-molecule reactions seem to lead to a fast build up of the carbon skeleton.