More and more attention has been paid to environmentally friendly bio-based renewable materials as the substitution of fossil-based materials, due to the increasing environmental concerns. In this study, regenerated cellulose films with enhanced mechanical property were prepared via incorporating different plasticizers using ionic liquid 1-allyl-3-methylimidazolium chloride (AmimCl) as the solvent. The characteristics of the cellulose films were investigated by scanning electron microscopy (SEM), atomic force microscopy (AFM), thermal analysis (TG), X-ray diffraction (XRD), 13C Solid-state cross-polarization/magic angle spinning nuclear magnetic resonance (CP/MAS NMR) and tensile testing. The results showed that the cellulose films exhibited a homogeneous and smooth surface structure. It was noted that the thermal stability of the regenerated cellulose film plasticized with glycerol was increased compared with other regenerated cellulose films. Furthermore, the incorporation of plasticizers dramatically strengthened the tensile strength and improved the hydrophobicity of cellulose films, as compared to the control sample. Therefore, these notable results exhibited the potential utilization in producing environmentally friendly cellulose films with high performance properties.

Hollow carbon spheres are attracting great attention due to their great potential uses in drug delivery, energy storage, and catalysis. However, the formation process and mechanism of the hollow carbon spheres are still unclear. Herein, we chose glucose as a carbon precursor and double surfactants poly­(ethylene glycol)-block-poly­(propylene glycol)-block-poly­(ethylene glycol) triblock copolymers and sodium oleate as the soft template. The synthesis process of hollow carbon spheres was investigated in the coupling of a soft templating method and hydrothermal carbonization system by regulating the reaction time. A dynamic formation process of the hollow carbon spheres was identified based on the results from scanning electron microscopy and transmission electron microscopy images, in which three evolution stages were seen including hollow carbon bowls, capsules, and spheres. In addition, the formation mechanism was also presumed: During the synthesis process, the double surfactants interacted with each other to act as the soft template, and the glucose underwent hydration, polymerization, and aromatization stages. When the concentration of aromatic compounds reached the critical supersaturation, the nucleation took place from a point and extended outward gradually along the interface to widen and thicken the carbon shell, resulting in different hollow structured carbon particles being formed successively by controlling the reaction time. Furthermore, the resultant hollow structured carbon particles were stable and uniform, and we made preliminary explorations on their biochemical and electrochemical performance.

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Soil moisture is an important indicator that is widely used in meteorology, hydrology, and agriculture. Two key problems must be addressed in the process of downscaling soil moisture: the selection of the downscaling method and the determination of the environmental variables, namely, the influencing factors of soil moisture. This study attempted to utilize machine learning and data mining algorithms to downscale the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) soil moisture data from 25 km to 1 km and compared the advantages and disadvantages of the random forest model and the Cubist algorithm to determine the more suitable soil moisture downscaling method for the middle and lower reaches of the Yangtze River Basin (MLRYRB). At present, either the normalized difference vegetation index (NDVI) or a digital elevation model (DEM) is selected as the environmental variable for the downscaling models. In contrast, variables, such as albedo and evapotranspiration, are infrequently applied; nevertheless, this study selected these two environmental variables, which have a considerable impact on soil moisture. Thus, the selected environmental variables in the downscaling process included the longitude, latitude, elevation, slope, NDVI, daytime and nighttime land surface temperature (LST_D and LST_N, respectively), albedo, evapotranspiration (ET), land cover (LC) type, and aspect. This study achieved downscaling on a 16-day timescale based on Moderate Resolution Imaging Spectroradiometer (MODIS) data. A comparison of the random forest model with the Cubist algorithm revealed that the R2 of the random forest-based downscaling method is higher than that of the Cubist algorithm-based method by 0.0161; moreover, the root-mean-square error (RMSE) is reduced by 0.0006 and the mean absolute error (MAE) is reduced by 0.0014. Testing the accuracies of these two downscaling methods showed that the random forest model is more suitable than the Cubist algorithm for downscaling AMSR-E soil moisture data from 25 km to 1 km in the MLRYRB, which provides a theoretical basis for obtaining high spatial resolution soil moisture data.

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