Abstract: Plasma bioscience and medicine are both rapidly growing fields. Their aim is to utilize cold physical plasmas for desired biological outcomes in medicine, biotechnology, agriculture, and general hygienic purposes. Great success has been achieved in many applications with individually designed plasma sources and plasma parameters. Although lab and application-specific tuning of plasmas is a great advantage of this technology, standardized units to define plasma treatments are required to facilitate comparison of the effects found by different researchers who do not use the same plasma sources. By drawing conclusions from over a century of plasma biomedical research, we propose that all researchers adopt the use of a standardized value, the plasma treatment unit (PTU), to describe the biological effects of different cold plasma sources and treatment regimens. It quantifies a key plasma effector in biological systems as an indicator and may provide the foundation for an analogous and clinically relevant unit in the future.

Abstract: Cancer immunotherapy is a promising strategy that engages the patient's immune system to kill cancer cells selectively while sparing normal tissue. Treatment of macrophages with a nanosecond-pulsed dielectric barrier discharge directly enhanced their cytotoxic activity against tumor cells but not normal cells. These results underscore the clinical potential of plasma for cancer immunotherapy.

Abstract: Low-temperature plasmas (LTPs) have a proven bactericidal activity governed by the generated reactive oxygen and nitrogen species (RONS) that target microbial cell components. However, RONS also interact with biomolecules in the environment. Here we assess the impact of these interactions upon exposure of liquid suspensions with variable organic content to an atmospheric-pressure dielectric barrier discharge plasma jet. Salmonella enterica serovar Typhimurium viability in the suspension was reduced in the absence [e. g., phosphate buffered saline (PBS)], but not in the presence of (high) organic content [Dulbecco's Modified Eagle's Medium (DMEM), DMEM supplemented with foetal calf serum, and Lysogeny Broth]. The reduced viability of LTP-treated bacteria in PBS correlated to a loss of membrane integrity, whereas double-strand DNA breaks could not be detected in treated single cells. The lack of bactericidal activity in solutions with high organic content correlated with a relative decrease of center dot OH and O-3/O-2(a(1)Delta g)/O, and an increase of H2O2 and NO2- in the plasma-treated solutions. These results indicate that the redox reactions of LTP-generated RONS with nontarget biomolecules resulted in a RONS composition with reduced bactericidal activity. Therefore, the chemical composition of the bacterial environment should be considered in the development of LTP for antimicrobial treatment, and may affect other biomedical applications as well.

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: In recent years there is increased interest in plasma-based CO2 conversion. Several plasma setups are being investigated for this purpose, but the most commonly used ones are a dielectric barrier discharge (DBD), a microwave (MW) plasma and a gliding arc (GA) reactor. In this proceedings paper, we will show results from our experiments in a (packed bed) DBD reactor and in a vortex-flow GA reactor, as well as from our model calculations for the detailed plasma chemistry in a DBD, MW and GA, for pure CO2 as well as mixtures of CO2 with N-2, CH4 and H2O.

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: Packed-bed DBD (PB-DBD) plasmas hold promise for effective degradation of greenhouse gases like SF6. In this work, we conducted a combined experimental and theoretical study to investigate the effect of the packing surface structure and the plasma surface discharge on the SF6 degradation in a gamma-Al2O3 packing DBD system. Experimental results show that both the hydration effect of the surface (upon moisture) and the presence of excessive reactive gases in the plasma can significantly reduce the SF6 degradation, but they hardly change the discharge behavior. DFT results show that the pre-adsorption of species such as H, OH, H2O and O-2 can occupy the active sites (Al-III site) which negatively impacts the SF6 adsorption. H2O molecules pre-adsorbed at neighboring sites can promote the activation of SF6 molecules and lower the reaction barrier for the S-F bond-breaking process. Surface-induced charges and local external electric fields caused by the plasma can both improve the SF6 adsorption and enhance the elongation of the S-F bonds. Our results indicate that both the surface structure of the packing material and the plasma surface discharge are crucial for SF6 degradation performance, and the packing beads should be kept dry during the degradation. This work helps to understand the underlying mechanisms of SF6 degradation in a PB-DBD system.

Abstract: A two-dimensional self-consistent fluid model combined with the full set of Maxwell equations is developed to investigate an argon capacitively coupled plasma, focusing on the electromagnetic effects on the discharge characteristics at various discharge conditions. The results indicate that there exist distinct differences in plasma characteristics calculated with the so-called electrostatic model (i.e., without taking into account the electromagnetic effects) and the electromagnetic model (which includes the electromagnetic effects), especially at very high frequencies. Indeed, when the excitation source is in the high frequency regime and the electromagnetic effects are taken into account, the plasma density increases significantly and meanwhile the ionization rate evolves to a very different distribution when the electromagnetic effects are dominant. Furthermore, the dependence of the plasma characteristics on the voltage and pressure is also investigated, at constant frequency. It is observed that when the voltage is low, the difference between these two models becomes more obvious than at higher voltages. As the pressure increases, the plasma density profiles obtained from the electromagnetic model smoothly shift from edge-peaked over uniform to a broad maximum in the center. In addition, the edge effect becomes less pronounced with increasing frequency and pressure, and the skin effect rather than the standing-wave effect becomes dominant when the voltage is high.