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: 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: 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: In this paper, an overview is given of different modeling approaches used for describing gas discharge plasmas, as well as plasma-surface interactions. A fluid model is illustrated for describing the detailed plasma chemistry in capacitively coupled rf discharges. The strengths and limitations of Monte Carlo simulations and of a particle-in-cell-Monte Carlo collisions model are explained for a magnetron discharge, whereas the capabilities of a hybrid Monte Carlo-fluid approach are illustrated for a direct current glow discharge used for spectrochemical analysis of materials. Finally, some examples of molecular dynamics simulations, for the purpose of plasma-deposition, are given.

Abstract: Dielectric Barrier Discharges operating in CO and CO2 form solid products at atmospheric pressure. The main differences between both plasmas and their deposits were analyzed, at similar energy input. GC measurements revealed a mixture of CO2, CO, and O2 in the CO2 DBD exhaust, while no O2 was found in the CO plasma. A coating of nanoparticles composed of Fe, O, and C was produced by the CO2 discharge, whereas, a microscopic dendrite-like carbon structure was formed in the CO plasma. Fe3O4 and Fe crystalline phases were found in the CO2 sample. The CO
deposition was characterized as an amorphous structure, close to polymeric CO (p-CO). Interestingly, p-CO is not formed in the CO2 plasma, in spite of the significant amounts of CO produced (up to 30% in the reactor exhaust).