Abstract: Renewed interest in the ferroelectric semi-conductor germanium telluride was recently triggered by the direct observation of a giant Rashba effect and a 30-year-old dream about a functional spin field-effect transistor. In this respect, all-electrical control of the spin texture in this material in combination with ferroelectric properties at the nanoscale would create advanced functionalities in spintronics and data information processing. Here, we investigate the atomic and electronic properties of GeTe bulk single crystals and their (111) surfaces. We succeeded in growing crystals possessing solely inversion domains of similar to 10 nm thickness parallel to each other. Using HAADF-TEM we observe two types of domain boundaries, one of them being similar in structure to the van der Waals gap in layered materials. This structure is responsible for the formation of surface domains with preferential Te-termination (similar to 68%) as we determined using photoelectron diffraction and XPS. The lateral dimensions of the surface domains are in the range of similar to 10-100 nm, and both Ge- and Te-terminations reveal no reconstruction. Using spin-ARPES we establish an intrinsic quantitative relationship between the spin polarization of pure bulk states and the relative contribution of different terminations, a result that is consistent with a reversal of the spin texture of the bulk Rashba bands for opposite configurations of the ferroelectric polarization within individual nanodomains. Our findings are important for potential applications of ferroelectric Rashba semiconductors in nonvolatile spintronic devices with advanced memory and computing capabilities at the nanoscale.

Abstract: In the last few years, governments all around the world have agreed upon migrating towards carbon-neutral economies as a strategy for restraining the effects of climate change. A major obstacle limiting this achievement is greenhouse gases emissions, for which the aviation sector is a key contributor because of its dependence on fossil fuels. As an alternative, biofuels with similar characteristics to current fossil-fuels and fully compatible with the existing petroleum infrastructure (i.e., drop-in biofuels) are being developed. In this regard, microalgae are a promising feedstock thanks to, among other aspects, their potential for lipid accumulation. This review outlines the development status, opportunities, and challenges of different technologies that are capable of or applicable to transform microalgae into aviation fuels. To this effect, a baseline of the existing jet fuels and the requirements for potential aviation biofuels is initially presented. Then, microalgae production and valorization techniques are discussed with an emphasis on the thermochemical pathways. Finally, an assessment of the present techno-economic feasibility of microalgae-derived aviation fuels is discussed, along with the authors’ point of view on the suitability of these techniques. Further developments are needed to reduce the costs of cultivation and harvesting of microalgae, and a biorefinery approach might improve the economics of the overall process. In addition, while each of the conversion routes described has its advantages and drawbacks, they converge upon the need of optimizing the deoxygenation techniques and the proportion of the suitable type of hydrocarbons that match fuel requirements.

Abstract: Using spin- and angle-resolved photoemission spectroscopy and relativistic many-body calculations, we investigate the evolution of the electronic structure of (Bi1-xInx)(2)Se-3)(2)Se-3 bulk single crystals around the critical point of the trivial to topological insulator quantum-phase transition. By increasing x, we observe how a surface gap opens at the Dirac point of the initially gapless topological surface state of Bi2Se3, leading to the existence of massive fermions. The surface gap monotonically increases for a wide range of x values across the topological and trivial sides of the quantum-phase transition. By means of photon-energy-dependent measurements, we demonstrate that the gapped surface state survives the inversion of the bulk bands which occurs at a critical point near x = 0.055. The surface state exhibits a nonzero in-plane spin polarization which decays exponentially with increasing x, and which persists in both the topological and trivial insulator phases. Our calculations reveal qualitative agreement with the experimental results all across the quantum-phase transition upon the systematic variation of the spin-orbit coupling strength. A non-time-reversal symmetry-breaking mechanism of bulk-mediated scattering processes that increase with decreasing spin-orbit coupling strength is proposed as explanation.

Abstract: This work aims to enhance the efficiency of integrated poultry waste management in bio-circular-green economy by maximizing the co-digestion of chicken manure and its digestate-grown biomass. In a series of batch assays, Napier grass (NG) was mixed with chicken manure (CM) at various proportions (100:0, 80:20, 60:40, 50:50, 40:60, 20:80 and 0:100) to identify co-substrate synergism, followed by physiochemical conditioning (size reduction and ultrasonication) of NG before co-digestion. Results indicated that NG mix of at least 80% was required to gain a full methanation potential of the individual substrates; no synergistic ratio above unity was found. However, the combined effect of size reduction and sonication was found to markedly improve the cosubstrate's biodegradability by 88.7%. The findings were then used to run continuous co-digestion at various operating regimes. In optimal continuous co-digestion condition, NG particle size of 0.6-2.4 mm combined with sonication intensity at 1111 kJ/kgTS improved biomethanation yield as high as 106.3%. Sub-thermophilic digestion at 45 degrees C was shown to give a higher and more stable CH4 yield than at 55 degrees C. Finally, it was also found that recycling liquid effluent at 40% to replace freshwater in feed, although showed no significant difference in CH4 yield (& alpha; = 0.05), noticeably increased system buffer capacity. This optimized biodegradation regime could give co-digestion waste management a higher overall plant efficiency and economic return.

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.

Abstract: We study the photoionization of the s states in the systems bound by sufficiently weak central fields V(r) for the large photon energies corresponding to the relativistic photoelectrons. We demonstrate that the energy dependence of the photoionization cross section can be obtained without solving the wave equation. We show that the shape of the energy dependence of the cross section is determined by analytical properties of the binding potential V(r). We find the cross sections for the potentials V(r) which have singularities in the origin, on the real axis and in the complex plane.