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: 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: We assessed the impact of road disturbances on the dominant mycorrhizal types in ecosystems at the global level and how this mechanism can potentially lead to lasting plant community changes. We used a database of coordinated plant community surveys following mountain roads from 894 plots in 11 mountain regions across the globe in combination with an existing database of mycorrhizal-plant associations in order to approximate the relative abundance of mycorrhizal types in natural and disturbed environments. Our findings show that roadside disturbance promotes the cover of plants associated with arbuscular mycorrhizal (AM) fungi. This effect is especially strong in colder mountain environments and in mountain regions where plant communities are dominated by ectomycorrhizal (EcM) or ericoid-mycorrhizal (ErM) associations. Furthermore, non-native plant species, which we confirmed to be mostly AM plants, are more successful in environments dominated by AM associations. These biogeographical patterns suggest that changes in mycorrhizal types could be a crucial factor in the worldwide impact of anthropogenic disturbances on mountain ecosystems. Indeed, roadsides foster AM-dominated systems, where AM-fungi might aid AM-associated plant species while potentially reducing the biotic resistance against invasive non-native species, often also associated with AM networks. Restoration efforts in mountain ecosystems will have to contend with changes in the fundamental make-up of EcM- and ErM plant communities induced by roadside disturbance.