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: Modeling results are presented to gain a better insight in the properties of a SiH4/O2/Ar inductively coupled plasma (ICP) and how it interacts with a silicon substrate (wafer), as applied in the microelectronics industry for the fabrication of electronic devices. The SiH4/O2/Ar ICP is used for the filling of microtrenches with isolating material (SiO2), as applied in shallow trench isolation (STI). In this article, a detailed reaction set that describes the plasma chemistry of SiH4/O2/Ar discharges as well as surface processes, such as sputtering, oxidation, and deposition, is presented. Results are presented on the plasma properties during the plasma enhanced chemical vapor deposition process (PECVD) for different gas ratios, as well as on the shape of the filled trenches and the surface compositions of the deposited layers. For the operating conditions under study it is found that the most important species accounting for deposition are SiH2, SiH3O, SiH3 and SiH2O, while SiH+2, SiH+3, O+2 and Ar+ are the dominant species for sputtering of the surface. By diluting the precursor gas (SiH4) in the mixture, the deposition rate versus sputtering rate can be controlled for a desired trench filling process. From the calculation results it is clear that a high deposition rate will result in undesired void formation during the trench filling, while a small deposition rate will result in undesired trench bottom and mask damage by sputtering. By varying the SiH4/O2 ratio, the chemical composition of the deposited layer will be influenced. However, even at the highest SiH4/O2 ratio investigated (i.e., 3.2:1; low oxygen content), the bulk deposited layer consists mainly of SiO2, suggesting that low-volatile silane species deposit first and subsequently become oxidized instead of being oxidized first in the plasma before deposition. Finally, it was found that the top surface of the deposited layer contained less oxygen due to preferential sputtering of O atoms, making the top layer more Si-rich. However, this effect is negligible at a SiH4/O2 ratio of 2:1 or lower.

Abstract: In this article, surface processes occurring during the etching of Si with a Cl2/O2/Ar plasma are investigated by means of experiments and modeling. Cl2-based plasmas are commonly used to etch silicon, while a small fraction of O2 is added to protect the sidewalls from lateral etching during the shallow trench isolation process. When the oxygen fraction exceeds a critical value, the wafer surface process changes from an etching regime to a deposition regime, drastically reducing the etch rate. This effect is commonly referred to as the etch stop phenomenon. To gain better understanding of this mechanism, the oxygen fraction is varied in the gas mixture and special attention is paid to the effects of oxygen and of the redeposition of non-volatile etched species on the overall etch/deposition process. It is found that, when the O2 flow is increased, the etch process changes from successful etching to the formation of a rough surface, and eventually to the actual growth of an oxide layer which completely blocks the etching of the underlying Si. The size of this etch stop island was found to increase as a function of oxygen flow, while its thickness was dependent on the amount of Si etched. This suggests that the growth of the oxide layer mainly depends on the redeposition of non-volatile etch products. The abrupt change in the etch rate as a function of oxygen fraction was not found back in the oxygen content of the plasma, suggesting the competitive nature between oxidation and chlorination at the wafer. Finally, the wafer and reactor wall compositions were investigated by modeling and it was found that the surface rapidly consisted mainly of SiO2 when the O2 flow was increased above about 15 sccm.

Abstract: In the present work, reactive magnetron sputtering in DC mode was used to grow complex oxide thin films, starting from two separate pure metal targets. A series of coatings was produced with a stoichiometry of the film ranging from MgO, over MgxAlyOz to Al2O3. The surface energy, crystallinity, hardness, refractive index, and surface roughness were investigated. A relationship between all properties studied and the Mg content of the samples was found. A critical compositional region for the Mg-Al-O system where all properties exhibit a change was noticed.

Abstract: Advances in the synthesis of porous microspheres and in their functionalization are increasing the interest in applications of alumina. This paper deals with coatings plasma deposited from 3-aminopropyltriethoxysilane by means of dielectric barrier discharges on alumina porous microspheres, shaped by a vibrational droplet coagulation technique. Aims of the work are the functionalization of the particles with active amino groups, as well as the evaluation of their surface coverage and of the penetration of the coatings into their pores. A multi-diagnostic approach was used for the chemical/morphological characterization of the particles. It was found that 5 min exposure to plasma discharges promotes the deposition of homogeneous coatings onto the microspheres and within their pores, down to 1 μm.

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