A metal-doped flexible porous carbon cloth for enhanced CO2/CH4 separation

Reconstructing annual XCO2 at a 1 km×1 km spatial resolution across China from 2012 to 2019 based on a spatial CatBoost method

Long-time-series, high-resolution datasets of the column-averaged dry-air mole fraction of carbon dioxide (XCO2) have great practical importance for mitigating the greenhouse effect, assessing carbon emissions and implementing a low-carbon cycle. However, the mainstream XCO2 datasets obtained from satellite observations have coarse spatial resolutions and are inadequate for supporting research applications with different precision requirements. Here, we developed a new spatial machine learning model by fusing spatial information with CatBoost, called SCatBoost, to fill the above gap based on existing global land-mapped 1° XCO2 data (GLM-XCO2). The 1-km-spatial-resolution dataset containing XCO2 values in China from 2012 to 2019 reconstructed by SCatBoost has stronger and more stable predictive power (confirmed with a cross-validation (R2 = 0.88 and RSME = 0.20 ppm)) than other traditional models. According to the estimated dataset, the overall national XCO2 showed an increasing trend, with the annual mean concentration rising from 392.65 ppm to 410.36 ppm. In addition, the spatial distribution of XCO2 concentrations in China reflects significantly higher concentrations in the eastern coastal areas than in the western inland areas. The contributions of this study can be summarized as follows: (1) It proposes SCatBoost, integrating the advantages of machine learning methods and spatial characteristics with a high prediction accuracy; (2) It presents a dataset of fine-scale and high resolution XCO2 over China from 2012 to 2019 by the model of SCatBoost; (3) Based on the generated data, we identify the spatiotemporal trends of XCO2 in the scale of nation and city agglomeration. These long-term and high resolution XCO2 data help understand the spatiotemporal variations in XCO2, thereby improving policy decisions and planning about carbon reduction.

Heterometallic Gd-Dy Formate Frameworks for Enhanced Magnetocaloric Properties

Lanthanide-based metal-organic frameworks (MOFs) have great potential as magnetic refrigerants under cryogenic conditions and are comparable to conventional alloys and magnetic nanoparticles. In particular, MOFs with Gd³⁺ ions behave as excellent magnetic refrigerants because of their large spin ground states. However, the major drawback of Gd³⁺-based MOFs is that they are not affected by the ligand material owing to the excessively large spin-only magnetic moment; therefore, their application is limited to the cryogenic region in the magnetic cooling field. In this study, we report the magnetic properties and magnetocaloric effect (MCE) resulting from heterogenized MOFs obtained from the reaction of Gd³⁺ and Dy³⁺ ions and their varied molar composition with the formate ligand. For Gd_xDy_1-x-(HCOO)₃, where 0 ≤ x ≤ 1, the isothermal magnetic entropy change (ΔS_m) increased with the increase in the fraction of Gd in the heterogenized MOFs. Meanwhile, with increasing Dy contents, the maximum peak temperature of ΔS_m is shifted to a higher temperature while preserving a relatively high ΔS_m value of 22.35 J·kg^-1 K^-1 at T = 7 K for an applied field change (ΔH) of 7 T despite the anisotropy and crystalline electric field effects. Furthermore, it was confirmed that the samples with a Dy content of 75% or more maintained the ΔS_m operating temperature longer. Therefore, the current approach of including Dy³⁺ ions in lanthanide compounds provides the possibility of further extending the operating temperature of magnetic cooling materials from cryogenic temperatures.