In two dimensional honeycomb ferromagnets, bosonic magnon quasiparticles (spin waves) may either behave as massless Dirac fermions or form topologically protected edge states. The key ingredient defining their nature is the next-nearest neighbor Dzyaloshinskii-Moriya (DM) interaction that breaks the inversion symmetry of the lattice and discriminates chirality of the associated spinwave excitations. Using inelastic neutron scattering, we find that spin waves of the insulating honeycomb ferromagnet CrI3 (TC = 61 K) have two distinctive bands of ferromagnetic excitations separated by a ∼4 meV gap at the Dirac points. These results can only be understood by considering a Heisenberg Hamiltonian with DM interaction, thus providing experimental evidence that spin waves in CrI3 can have robust topological properties potentially useful for dissipationless spintronic applications.

Accurate forecasting of carbon price is important and fundamental for anticipating the changing trends of the energy market, and, thus, to provide a valid reference for establishing power industry policy. However, carbon price forecasting is complicated owing to the nonlinear and non-stationary characteristics of carbon prices. In this paper, a combined forecasting model based on variational mode decomposition (VMD) and spiking neural networks (SNNs) is proposed. An original carbon price series is firstly decomposed into a series of relatively stable components through VMD to simplify the interference and coupling across characteristic information of different scales in the data. Then, a SNN forecasting model is built for each component, and the partial autocorrelation function (PACF) is used to determine the input variables for each SNN model. The final forecasting result for the original carbon price can be obtained by aggregating the forecasting results of all the components. Actual InterContinental Exchange (ICE) carbon price data is used for simulation, and comprehensive evaluation criteria are proposed for quantitative error evaluation. Simulation results and analysis suggest that the proposed VMD-SNN forecasting model outperforms conventional models in terms of forecasting accuracy and reliability.

The effects of principal mechanisms (selection and complementarity) of biodiversity on ecosystem functionality have been well studied. However, it remains unknown how environmental conditions affect the relative strength of these two mechanisms. To answer this question, a controlled pot experiment was conducted in which species diversity was manipulated in low (natural soil) and high stress (mine tailing) plots, respectively. Our results demonstrate that the principal mechanism underlying the increasing biomass shifts from the selection to complementarity with increasing abiotic stress. The shift occurs because species interactions varied with increasing abiotic stress. Competition prevails in low stress plots, while facilitation dominates in high stress plots. In low stress plots, the monoculture biomass of a specific species is a good indicator of the competitive ability of that species in the mixture, and the dominant species significantly affects the plot biomass. In high stress plots, the tolerance indexes of all individual species increase with the manipulated species richness, providing clear evidence for the increasing role of facilitation.

Strain can be used as an effective tool to tune the crystal structure of materials and hence to modify their electronic structures, including topological properties. Here, taking Na3Bi as a paradigmatic example, we demonstrated with first-principles calculations and k · p models that the topological phase transitions can be induced by various types of strains. For instance, the Dirac semimetal phase of ambient Na3Bi can be tuned into a topological insulator (TI) phase by uniaxial strain along the 100 axis. Hydrostatic pressure can let the ambient structure transfer into a new thermodynamically stable phase with Fm3m symmetry, coming with a perfect parabolic semimetal having a single contact point between the conduction and valence bands, exactly at Γ point on the Fermi level like α-Sn. Furthermore, uniaxial strain in the 100 direction can tune the new parabolic semimetal phase into a Dirac semimetal, while shear strains in both the 100 and 111 directions can take the new parabolic semimetal phase into a TI. k · p models are constructed to gain more insights into these quantum topological phase transitions. At last, we calculated surface states of Fm3m Na3Bi without and with strains to verify these topological transitions.

The structural stability and magnetic properties of iridium clusters Irn (n = 2-10) and their interaction on γ-Al2O3(001) and MgO(100) surfaces have been investigated from first principles calculations. It is found that the adsorption energy of Irn (n = 2-10)/γ-Al2O3(001) is lower than that of Irn/MgO(100); meanwhile, the strongest adsorption energy cluster for γ-Al2O3(001) is the tetrahedral Ir4 cluster, while for MgO(100) is a cubic Ir8 cluster. On the other hand, the nucleation of Irn (n = 2-10) clusters on both surfaces is thermodynamically favorable and exothermic. Moreover, the nucleation energy of Irn (n = 2-10) clusters on the γ-Al2O3(001) surface is close to the corresponding energy on the MgO(100) surface, except for Ir3, Ir4 and Ir6 clusters. Interestingly, the nucleation of Ir3 and Ir6 clusters on the MgO(100) surface is more favorable than that on the γ-Al2O3(001) surface, while the nucleation of the Ir4 cluster is reverse and very close to the gas phase Ir4 cluster. More importantly, deformation energy and charge density analysis show that the adsorbed Ir4 cluster on the γ-Al2O3(001) surface has obviously charge transfer between Ir atoms and surface Al, O atoms with negligible deformation. However, for the MgO(100) surface, the Ir4 cluster connects directly to three surface O atoms with severe distortion, which inhibits the activity of the tetrahedral Ir4 cluster as a catalyst.