Abstract: Cold atmospheric plasma (CAP) has shown its potential in biomedical applications, such as wound healing, cancer treatment and bacterial disinfection. Recent experiments have provided evidence that CAP can also enhance the intracellular uptake of glucose molecules which is important in diabetes therapy. In this respect, it is essential to understand the underlying mechanisms of intracellular glucose uptake induced by CAP, which is still unclear. Hence, in this study we try to elucidate the possible mechanism of glucose uptake by cells by performing computer simulations. Specifically, we study the transport of glucose molecules through native and oxidized membranes. Our simulation results show that the free energy barrier for the permeation of glucose molecules across the membrane decreases upon increasing the degree of oxidized lipids in the membrane. This indicates that the glucose permeation rate into cells increases when the CAP oxidation level in the cell membrane is increased.

Abstract: The CO and CO2 Dielectric Barrier Discharges (DBD) and their solid products were analyzed keeping similar energy input regimes. Gas chromatography analysis revealed the presence of CO2, CO and O-2 mixture in the exhaust of the CO2 DBD, while no O-2 was found when CO was used as a feed gas. It was shown that the C-2 Swan lines observed with optical emission spectroscopy were distinct in the CO plasma while they were not observed in the CO2 emission spectrum. Also the solid products of the plasmas exhibited remarkable differences. Nanoparticles with a diameter between10 and 300 nm, composed of Fe, O and C (Fe: O: C similar to 13: 50: 30) were produced by the CO2 DBD, while microscopic dendrite-like carbon structure (C: O similar to 73: 27) were formed in the CO plasma. The growth rate in the CO2 and CO DBDs was evaluated to be on the level of 0.15 mg/min and 15 mg/min, respectively. The difference of the CO and CO2 discharges and their products might be attributed to the oxygen content in the latter (6.4 mol.% O-2 in the exhaust) and subsequent etching of the carbonaceous film.

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: 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: Cold atmospheric plasma (CAP) is a promising technology for several medical applications, including the removal of biofilms from surfaces. However, the molecular mechanisms of CAP treatment are still poorly understood. Here we unravel the possible mechanisms of CAP‐induced oxidation of oligosaccharides, employing reactive molecular dynamics simulations based on the density functional‐tight binding potential. Specifically, we find that the interaction of oxygen atoms (used as CAP‐generated reactive species) with cellotriose (a model system for the oligosaccharides) can break structurally important glycosidic bonds, which subsequently leads to the disruption of the oligosaccharide molecule. The overall results help to shed light on our experimental evidence for cellotriose CAP. This oxidation by study provides atomic‐level insight into the onset of plasma‐induced removal of biofilms, as oligosaccharides are one of the main components of biofilm.

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