Abstract: Fundamental and applied research on plasma‐treated liquids for biomedical applications was boosted in the last few years, dictated by their advantages with respect to direct treatments. However, often, the lack of consistent analysis at a molecular level of these liquids, and of the processes used to produce them, have raised doubts of their usefulness in the clinic. The aim of this article is to critically discuss some basic aspects related to the use of plasma‐treated liquids in medicine, with a focus on their chemical composition. We analyze the main liquids used in the field, how they are affected by non‐thermal plasmas, and the possibility to replicate them without plasma treatment.

Abstract: above atmospheric) pressure regimes (1–3.5 bar). It was demonstrated that these operational conditions significantly influence both the discharge dynamics and the process efficiencies of O2 and CO2 discharges. For the case of the O2 DBD, the pressure rise results in the amplification of the discharge current, the appearance of emission lines of the metal electrode material (Fe, Cr, Ni) in the optical emission spectrum and the formation of a granular film of the erosion products (10–300 nm iron oxide nanoparticles) on the reactor walls. Somewhat similar behavior was observed also for the CO2 DBD. The discharge current, the relative intensity of the CO Angstrom band measured by Optical Emission Spectroscopy (OES) and the CO2 conversion rates could be stimulated to some extent by the rise in pressure. The optimal conditions for the O2 DBD (P = 2 bar) and the CO2 DBD (P = 1.5 bar) are demonstrated. It can be argued that the dynamics of the microdischarges (MD) define the underlying process of this behavior. It could be
demonstrated that the pressure increase stimulates the formation of more intensive but fewer MDs. In this way, the operating pressure can represent an additional tool to manipulate the properties of the MDs in a DBD, and as a result also the discharge performance.

Abstract: In this review paper, we present several examples of reactive molecular dynamics simulations, which contribute to a better understanding of the underlying mechanisms in plasma medicine on the atomic scale. This includes the interaction of important reactive oxygen plasma species with the outer cell wall of both gram-positive and gram-negative bacteria, and with lipids present in human skin. Moreover, as most biomolecules are surrounded by a liquid biofilm, the behavior of these plasma species in a liquid (water) layer is presented as well. Finally, a perspective for future atomic scale modeling studies is given, in the field of plasma medicine in general, and for cancer treatment in particular.

Abstract: A numerical model for NxOy remediation in humid air plasma produced with a dielectric barrier discharge at atmospheric pressure is presented. Special emphasis is given to NO2 and N2O reduction with the decrease of O2 content in the feedstock gas. A detailed reaction mechanism including electronic and ionic processes, as well as the contribution of radicals and excited atomic/molecular species is proposed. The temporal evolution of the densities of NO, NO2 and N2O species, and some other by-products, is analyzed, and the major pathways for the NxOy remediation are discussed for one pulse. Subsequently, simulations are presented for a multi-pulses case, where three O2 contents are tested for optimization of the remediation process. It is found that when the gas mixture O2/N2/H2O/NOx has no initial O2 content, the best NOx and N2O remediation is achieved.

Abstract: The energy dependence of a pulsed-dielectric barrier discharge (DBD) plasma treatment on chemical species production and biological responses was investigated. We hypothesized that the total plasma energy delivered during treatment encompasses the influence of major application parameters. A microsecond-pulsed DBD system was used to treat three different cancer cell lines and cell viability was analyzed. The energy per pulse was measured and the total plasma treatment energy was controlled by adjusting the pulse frequency, treatment time, and application distance. Our data suggest that the delivered plasma energy plays a predominant role in stimulating a biological response in vitro. This study aids in developing steps toward defining a plasma treatment unit and treatment dose for biomedical and clinical research.

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