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: P-n photovoltaic junctions are essential building blocks for optoelectronic devices for energy conversion. However, this photovoltaic efficiency has almost reached its theoretical limit. Here, a brand-new excitonic photovoltaic effect in 2D CsPbBr3/CdS heterostructures is revealed. These heterostructures, synthesized by epitaxial growth, display a clean interface and a strong interlayer coupling. The excitonic photovoltaic effect is a function of both the built-in equilibrium electrical potential energy and the chemical potential energy, which is generated by the significant concentration gradient of electrons and holes at the heterojunction interface. Excitingly, this novel photovoltaic effect results in a large open-circuit voltage of 0.76 V and a high power conversion efficiency of 17.5%. In addition, high photodetection performance, including a high photoswitch ratio (I-light/I-dark) of 10(5)and a fast response rate of 23 mu s are obtained. These findings provide a new platform for photovoltaic applications.

Abstract: Monolayers of graphene and hexagonal boron nitride (hBN) are highly permeable to thermal protons1,2. For thicker two-dimensional (2D) materials, proton conductivity diminishes exponentially, so that, for example, monolayer MoS2 that is just three atoms thick is completely impermeable to protons1. This seemed to suggest that only one-atom-thick crystals could be used as proton-conducting membranes. Here, we show that few-layer micas that are rather thick on the atomic scale become excellent proton conductors if native cations are ion-exchanged for protons. Their areal conductivity exceeds that of graphene and hBN by one to two orders of magnitude. Importantly, ion-exchanged 2D micas exhibit this high conductivity inside the infamous gap for proton-conducting materials3, which extends from ∼100 °C to 500 °C. Areal conductivity of proton-exchanged monolayer micas can reach above 100 S cm−2 at 500 °C, well above the current requirements for the industry roadmap4. We attribute the fast proton permeation to ~5-Å-wide tubular channels that perforate micas’ crystal structure, which, after ion exchange, contain only hydroxyl groups inside. Our work indicates that there could be other 2D crystals5 with similar nanometre-scale channels, which could help close the materials gap in proton-conducting applications.

Abstract: Nanoporous membranes based on two dimensional materials are predicted to provide highly selective gas transport in combination with extreme permeance. Here we investigate membranes made from multilayer graphdiyne, a graphene-like crystal with a larger unit cell. Despite being nearly a hundred of nanometers thick, the membranes allow fast, Knudsen-type permeation of light gases such as helium and hydrogen whereas heavy noble gases like xenon exhibit strongly suppressed flows. Using isotope and cryogenic temperature measurements, the seemingly conflicting characteristics are explained by a high density of straight-through holes (direct porosity of similar to 0.1%), in which heavy atoms are adsorbed on the walls, partially blocking Knudsen flows. Our work offers important insights into intricate transport mechanisms playing a role at nanoscale.

Abstract: Starting from the shell structure in atoms and the significant correlation within electron pairs, we distinguish the exchange-correlation effects between two electrons of opposite spins occupying the same orbital from the average correlation among many electrons in a crystal. In the periodic potential of the crystal with lattice constant larger than the effective Bohr radius of the valence electrons, these correlated electron pairs can form a metastable energy band above the corresponding single-electron band separated by an energy gap. In order to determine if these metastable electron pairs can be stabilized, we calculate the many-electron exchange-correlation renormalization and the polaron correction to the two-band system with single electrons and electron pairs. We find that the electron-phonon interaction is essential to counterbalance the Coulomb repulsion and to stabilize the electron pairs. The interplay of the electron-electron and electron-phonon interactions, manifested in the exchange-correlation energies, polaron effects, and screening, is responsible for the formation of electron pairs (bipolarons) that are located on the Fermi surface of the single-electron band.

Abstract: Systems which rapidly evolve through symmetry-breaking transitions on timescales comparable to the fluctuation timescale of the single-particle excitations may behave very differently than under controlled near-ergodic conditions. A real-time investigation with high temporal resolution may reveal insights into the ordering through the transition that are not available in static experiments. We present an investigation of the system trajectory through a normal-to-superconductor transition in a prototype high-temperature superconducting cuprate in which such a situation occurs. Using a multiple pulse femtosecond spectroscopy technique we measure the system trajectory and time evolution of the single-particle excitations through the transition in La1.9Sr0.1CuO4 and compare the data to a simulation based on the time-dependent Ginzburg-Landau theory, using the laser excitation fluence as an adjustable parameter controlling the quench conditions in both experiment and theory. The comparison reveals the presence of significant superconducting fluctuations which precede the transition on short timescales. By including superconducting fluctuations as a seed for the growth of the superconducting order we can obtain a satisfactory agreement of the theory with the experiment. Remarkably, the pseudogap excitations apparently play no role in this process.

Abstract: Lattice thermal conductivity (kappa(L)) is of great scientific interest for the development of efficient energy conversion technologies. Therefore, microscopic understanding of phonon transport is critically important for designing functional materials. In our previous study (Roshan et al., ACS Applied Energy Mater. 2021, 5, 882-896), anomalous kappa(L) trends were predicted for rocksalt alkaline-earth chalcogenides (AECs). In the present work, we extended it to alkali halides (AHs) and conducted a thorough investigation to explore the role of atomic mass contrast on lattice dynamics and phonon transport properties of 36 binary compounds (20 AHs + 16 AECs). The calculated spectral and cumulative kappa(L) reveal that low-lying optical phonon modes significantly boost kappa(L) alongside acoustic phonons in materials where the atomic mass ratio approaches unity and cophonocity nears zero. Phonon scattering rates are relatively low for materials with a mass ratio close to one, and the corresponding phonon lifetimes are higher, which enhances kappa(L). Phonon lifetimes play a critical role, outweighing phonon group velocities, in determining the anomalous trends in kappa(L) for both AHs and AECs. To further explore the role of atomic mass contrast in kappa(L), the effect of tensile lattice strain on phonon transport has also been investigated. Under tensile strain, both group velocities and phonon lifetimes decrease in the low frequency range, leading to a decrease in kappa(L). This work provides insights on how atomic mass contrast can tune the contribution of optical phonons to kappa(L) and its implications on scattering rates by either enhancing or suppressing kappa(L). These insights would aid in the selection of elements for designing new functional materials with and without atomic mass contrast to achieve relatively high and low kappa(L) values, respectively.