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: A chemical kinetics model was developed to characterise the gas‐phase dynamics of H₂production in nanosecond‐pulsed CH₄plasmas. Pulsed behaviour was observed in the calculated electric field, electron temperature and species densities at all pressures. The model agrees reasonably with experimental results, showing CH₄conversion at 30% and C₂H₂and H₂as major products. The underlying mechanisms in CH₄dissociation and H₂formation were analysed, highlighting the large contribution of vibrationally excited CH₄and H₂to coupling energy from the plasma into gas‐phase heating, and revealing that H₂synthesis is not affected by applied pressure, with selectivity remaining unchanged at ~42% in the 1–5 bar range.

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: Heterostructures of III-V materials under a mechanical strain are being actively researched to enhance the performance of the tunnel field-effect transistor (TFET). In scaled III-V device structures, however, the interplay between the effects of strain and quantum confinement on the semiconductor band structure and hence the performance is highly non-trivial. We have therefore developed a computationally efficient quantum mechanical simulator Pharos, which enables self-consistent full-zone k.p-based simulations of III-V TFETs under a general non-uniform strain. We present the self-consistent procedure and demonstrate it on confined staggered bandgap GaAs0.5Sb0.5/In0.53Ga0.47As TFETs. We find a large performance degradation due to size-induced quantum confinement compared to non-confined devices. We show that some performance can be regained either by applying a uniform biaxial tensile strain or through the non-uniform strain profile at a lattice-mismatched heterostructure.

Abstract: The energy distributions of ions produced in a spark source mass spectrometer were recorded for different trace element ions and for the total ion current. Both electrodes of pure doped graphite, and with 10% potassium salts added, were examined. The results show that the measured energy of an ion is mainly determined by the m/z ratio and that the energy of the total ion current depends on the sample composition. It was also clear that if the accelerating voltage is set to obtain maximum transmission of the total ion current, the fraction of the heavy elements that is discriminated at the β-slit depends on the matrix composition if the pass band of the instrument is narrow. These effects influence considerably the accuracy in the analysis of variable samples.

Abstract: We theoretically investigate the effect of intercalation of third row transition metals (Co, Cr, Fe, Mn, Ti and V) in the layers of WSe2. Using density functional theory (DFT), we investigate the structural stability. We also compute the DFT energies of various magnetic spin configurations. Using these energies, we construct a Heisenberg Hamiltonian and perform a Monte Carlo study on each WSe2 + intercalant system to estimate the Curie or Neel temperature. We find ferromagnetic ground states for Ti and Cr intercalation, with Curie temperatures of 31K and 225K, respectively. In Fe-intercalated WSe2, we predict that antiferromagnetic ordering is present up to 564K. For V intercalation, we find that the system exhibits a double phase transition.

Abstract: We present a computational model to model the magnetic structure of two-dimensional (2D) dilute-magnetic-semiconductors (DMS) both the monolayers and multilayers using first-principles density functional theory (DFT), as well as their magnetic phase transition as a function of temperature using Monte-Carlo simulations. Using our method, we model the magnetic structure of bulk, bilayer, and monolayer MoS2 substitutionally doped with Fe atoms. We find that the out-of-plane interaction in bilayer MoS2 is weakly ferromagnetic, whereas in bulk MoS2 it is strongly anti-ferromagnetic. Finally, we show that the magnetic order is more robust in bilayer Fe-doped MoS2 compared to the monolayer and results in a room-temperature FM at an atomic substitution of 14-16%.

Abstract: Reactive molecular dynamics simulations are performed to study the interaction of reactive oxygen species, such as OH, HO2 and H2O2, with the endotoxic biomolecule lipid A of the gram-negative bacterium Escherichia coli. It is found that the aforementioned plasma species can destroy the lipid A, which consequently results in reducing its toxic activity. All bond dissociation events are initiated by hydrogen-abstraction reactions. However, the mechanisms behind these dissociations are dependent on the impinging plasma species, i.e. a clear difference is observed in the mechanisms upon impact of HO2 radicals and H2O2 molecules on one hand and OH radicals on the other hand. Our simulation results are in good agreement with experimental observations.

Abstract: Spark source mass spectrometric analyses of doped carbon samples were performed at different pulse lengths, pulse frequencies and breakdown voltages. The pulse length and pulse frequencies had no influence on the analysis. The relation between breakdown voltage, spark gap and sample matrix was determined. With increasing breakdown voltage, the abundance of the multiply charged ions was found to decrease, whereas the abundance of the cluster ions increased. The breakdown voltage was also found to affect the absolute and relative intensities of different elements in different manners. Interpretation of these results led to some new considerations on the phenomena in a spark discharge.

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.