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: 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: We calculate the contact resistance for an edge- and top-contacted 2D semiconductor. The contact region consists of a metal contacting a monolayer of MoS2 which is otherwise surrounded by SiO2. We use the quantum transmitting boundary method to compute the contact resistance as a function of the 2D semiconductor doping concentration. An effective mass Hamiltonian is used to describe the properties of the various materials. The electrostatic potentials are obtained by solving the Poisson equation numerically. We incorporate the effects of the image-force barrier lowering on the Schottky barrier and examine the impact on the contact resistance. At low doping concentrations, the contact resistance of the top contact is lower compared to edge contact, while at high doping concentrations, the edge contact exhibits lower resistance.

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