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.

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: Aim: We aimed to assess 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. Location: Globally distributed mountain regions Time Period: 2007-2018 Taxa studied: Plants (linked to their associated mycorrhizal fungi) Methods: 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. Results: 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. Main Conclusions: 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.