Nodal multigap superconductivity in the anisotropic iron-based compound RbCa2Fe4As4F2

The 12442 compounds are a recently discovered family of iron-based superconductors, that share several features with the cuprates due to their strongly anisotropic structure, but are so far poorly understood. Here, we report on the gap structure and anisotropy of RbCa2(Fe1−xNix)4As4F2 single crystals, investigated by a combination of directional point-contact Andreev-reflection spectroscopy and coplanar waveguide resonator measurements. Two gaps were identified, with clear signatures of d-wave-like nodal structures which persist upon Ni doping, well described by a two-band d − d state with symmetry-imposed nodes. A large London penetration depth anisotropy was revealed, weakly dependent on temperature and fully compatible with the d − d model

Unusually weak irradiation effects in anisotropic iron-based superconductor RbCa2Fe4As4F2

We report on the effects of 3.5 MeV proton irradiation in RbCa2Fe4As4F2, an iron-based superconductor with unusual properties in between those of the pnictides and of the cuprate hightemperature superconductors. We studied how structural disorder introduced by ion bombardment affects the critical temperature, superfluid density and gap values by combining a coplanar waveguide resonator technique, electric transport measurements and point-contact Andreevreflection spectroscopy. We find an unusually weak dependence of the superconducting properties on the amount of disorder in this material when compared to other iron-based superconductors under comparable irradiation conditions. The nodal multigap state exhibited by pristine RbCa2Fe4As4F2 is also robust against proton irradiation, with a two-band d-d model being the one that best fits the experimental data

Relationships between Burn Severity and Environmental Drivers in the Temperate Coniferous Forest of Northern China

Burn severity is a key component of fire regimes and is critical for quantifying fires’ impacts on key ecological processes. The spatial and temporal distribution characteristics of forest burn severity are closely related to its environmental drivers prior to the fire occurrence. The temperate coniferous forest of northern China is an important part of China’s forest resources and has suffered frequent forest fires in recent years. However, the understanding of environmental drivers controlling burn severity in this fire-prone region is still limited. To fill the gap, spatial pattern metrics including pre-fire fuel variables (tree canopy cover (TCC), normalized difference vegetation index (NDVI), and live fuel moisture content (LFMC)), topographic variables (elevation, slope, and topographic radiation aspect index (TRASP)), and weather variables (relative humidity, maximum air temperature, cumulative precipitation, and maximum wind speed) were correlated with a remote sensing-derived burn severity index, the composite burn index (CBI). A random forest (RF) machine learning algorithm was applied to reveal the relative importance of the environmental drivers mentioned above to burn severity for a fire. The model achieved CBI prediction accuracy with a correlation coefficient (R) equal to 0.76, root mean square error (RMSE) equal to 0.16, and fitting line slope equal to 0.64. The results showed that burn severity was mostly influenced by flammable live fuels and LFMC. The elevation was the most important topographic driver, and meteorological variables had no obvious effect on burn severity. Our findings suggest that in addition to conducting strategic fuel reduction management activities, planning the landscapes with fire-resistant plants with higher LFMC when possible (e.g., “Green firebreaks”) is also indispensable for lowering the burn severity caused by wildfires in the temperate coniferous forests of northern China

Relationships between Burn Severity and Environmental Drivers in the Temperate Coniferous Forest of Northern China

Burn severity is a key component of fire regimes and is critical for quantifying fires’ impacts on key ecological processes. The spatial and temporal distribution characteristics of forest burn severity are closely related to its environmental drivers prior to the fire occurrence. The temperate coniferous forest of northern China is an important part of China’s forest resources and has suffered frequent forest fires in recent years. However, the understanding of environmental drivers controlling burn severity in this fire-prone region is still limited. To fill the gap, spatial pattern metrics including pre-fire fuel variables (tree canopy cover (TCC), normalized difference vegetation index (NDVI), and live fuel moisture content (LFMC)), topographic variables (elevation, slope, and topographic radiation aspect index (TRASP)), and weather variables (relative humidity, maximum air temperature, cumulative precipitation, and maximum wind speed) were correlated with a remote sensing-derived burn severity index, the composite burn index (CBI). A random forest (RF) machine learning algorithm was applied to reveal the relative importance of the environmental drivers mentioned above to burn severity for a fire. The model achieved CBI prediction accuracy with a correlation coefficient (R) equal to 0.76, root mean square error (RMSE) equal to 0.16, and fitting line slope equal to 0.64. The results showed that burn severity was mostly influenced by flammable live fuels and LFMC. The elevation was the most important topographic driver, and meteorological variables had no obvious effect on burn severity. Our findings suggest that in addition to conducting strategic fuel reduction management activities, planning the landscapes with fire-resistant plants with higher LFMC when possible (e.g., “Green firebreaks”) is also indispensable for lowering the burn severity caused by wildfires in the temperate coniferous forests of northern China

Correction: Wang, L., et al. Assessment of the Dual Polarimetric Sentinel-1A Data for Forest Fuel Moisture Content Estimation. Remote Sensing 2019, 11(13), 1568

The authors wish to make the following corrections to this paper [1]: 1 [...