Lanzhou Nonferrous Metals Design and Research Institute (China)

ABSTRACTIn the construction process of the artificial ground freezing (AGF), the utilization of the temperature field to determine the freezing time is crucial for the safe construction. While the thermal parameter is the core parameter of the temperature field calculation. How to obtain the joint probability distribution of thermal parameters of frozen soil under limited test data is essential to improve the accuracy of the stochastic temperature field and guide safe construction. In this study, based on the sample of frozen soil in Mengtie railway station of Anhui metro line, the statistical characteristics of the thermal conductivity, volumetric heat capacity, and thermal conductivity at various temperatures were obtained. Then multidimensional Gaussian copula models at different temperatures were constructed using three construction methods to characterize the correlations among the thermal parameters. Additionally, the correlation coefficients under three methods were simulated using Monte Carlo simulation (MCS) and the fitting errors were calculated. Finally, the Sobol indices of thermal parameters were calculated by a simple one‐dimensional heat conduction model. The results show that the frozen soil thermal parameters have obvious correlation variability at various temperatures. The Pearson method presents the most favorable fitting capability in the construction of the joint distribution model of frozen soil thermal variables. The error of the simulation results is the smallest when the construction method and correlation coefficient are identical. The Sobol indices calculated by different methods have significant differences, with the Sobol indices using the Kendall method exhibiting a higher sensitivity to the nonlinearity of the parameters.

The application of Ca2Al(OH)6Cl·2H2O enables simultaneous defluoridation, dealumination and resourcing of impurity aluminium from high-concentration RECl3 solution.

The urban functional zone, serving as a bridge to understanding the complex interactions between human spatial activities and surface thermal environmental changes, explores the driving force information of its internal temperature changes, which is crucial for improving the urban thermal environment. However, the impacts of the current urban functional zones on the thermal environment, based on the delineation of human activities, have yet to be sufficiently investigated. To address the issue, we constructed a two-factor weighted dominant function vector model of “population heat—land use scale” to identify urban functional zones. This model is based on multisource data and considers the perspective of urban functional supply and demand matching. We then analyzed the spatial differentiation and driving factors of the relationship between urban functional zones and the surface thermal environment using the random forest algorithm, bivariate spatial autocorrelation, geographical detectors, and geographically weighted regression models. The results showed that there are significant differences in the Land Surface Temperature among different urban functional zones in the central urban area of Lanzhou. Among these, the life service zone has the greatest impact on the surface thermal environment, followed by the industrial zone and catering service zone, while the green space zone has the least impact. The surface thermal environment exhibits high-high clusters in localized spatial clustering patterns with life service, industrial, catering service, and residential zones. In contrast, it tends to exhibit low-high clusters with green spaces. Significant spatial clustering and dependence exist between various functional zones and the surface thermal environment. The land cover types characterized by the Normalized Difference Bare Land and Building Index, the vegetation coverage represented by the Fraction of Vegetation Cover, and the density of industrial activities indicated by the Industrial POI Kernel Density Index are the main drivers of the surface thermal environment in the various functional zones of the central urban area of Lanzhou, and all exhibit significant spatial heterogeneity.

Abstract Although lithium‐sulfur batteries have a high theoretical energy density that is higher than lithium‐ion batteries, their development is limited by the slow kinetics of lithium polysulfide conversion. In this research, we utilize the excellent bidirectional catalysis and adsorption of lithium polysulfide by the bimetallic oxide Co 3 V 2 O 8 composite carbon hollow sphere to address the kinetic obstacle of lithium‐sulfur battery. On the one hand, the carbon hollow sphere substrate provides a cavity that can hold a large amount of sulfur. On the other hand, it can limit the diffusion of lithium polysulfide by van der Waals forces. The combination of the above two points improves the capacity and stability of lithium‐sulfur batteries. It has a specific capacity of 1237.2 mAh g −1 at 0.2 C current density and retains 603 mAh g −1 after 100 cycles. At a high current density of 2 C, the specific capacity is 976.2 mAh g −1 . After 1000 cycles, it holds at 338.3 mAh g −1 , and the capacity retention rate per cycle is 99.89 %. This work discovers the new potential of Co 3 V 2 O 8 as an electrocatalyst and proposes a process that can widely prepare carbon materials with complex uniform distribution of electrocatalysts to achieve high specific capacity of lithium‐sulfur batteries.

A gas mixture is introduced into the in situ TEM sample area during gas solid catalysis to monitor the evolution of the surface dynamics of the catalyst and to explore the catalytic mechanism as well.