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 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: 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: We model transport through two-dimensional topological insulator (TI) nanoribbons. To model the quantum transport, we employ the non-equilibrium Green's function approach. With the presented approach, we study the effect of lattice imperfections on the carrier transport. We observe that the topologically protected edge states of TIs are robust against a high percentage (2%) of vacancy defects. We also investigate tunneling of the edge states in two decoupled TI nanoribbons.

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