Saponins extracted from Sapindus mukorossi are natural nonionic surfactants with various biological activities and strong surface activities, which can be widely used in biomedicine and the daily cleaning chemical industry. In this paper, Sapindus pericarp was used as the raw material for the extraction of saponins. Effects of major extraction variables, including solvents, extraction methods, material-to-liquid ratios, and extraction time were investigated. A yield of 30.48% and purity of 61.38% were obtained by using water extraction for 4 h with a material-to-liquid ratio of 1 : 6. Fermentation was used to purify the saponins, and a high sugar resistant yeast performed better with an optimal amount of 5%. The saponins concentration slightly decreased from 38.86 g L⁻¹ to 37.72 g L⁻¹ after fermentation, and the purity of the saponins increased to 78.97%. Structure and performance tests of homemade saponins were also carried out. The structure of Sapindus saponins was found to be similar to that of mukurozi-saponin using FTIR and HPLC, and the foam half-life of homemade saponins was 25.82% longer than that of unfermented saponins. On comparing four chemical surfactants, the foam half-life of saponins after fermentation was found to be much longer. These results provide useful information for the development of industrial purification and application of Sapindus saponins.

Density prediction is of great significance for molecular design of energetic materials, since detonation velocity linearly with density and detonation pressure increases with the density squared. However, the accuracy and generalization of former reported prediction models need further improvement, because most of them are derived from small data sets and few molecular descriptors. As shown in this paper, for molecules presenting brick-like shape or containing more hydrogen-bond donors the predicted densities have large negative deviations from experimental values. Thus, a molecular morphology descriptor η and a hydrogen-bond descriptor H_b are introduced as correction items to build 3 new QSPR models. Besides, 3694 nitro compounds are adopted as data set by this work. The accuracies are obviously improved, and the generalizations are verified by an independent test set. At the level of B3PW91/6-31G(d,p), the effective ratios (ERs) of the 3 Equations, for Δρ < 5%, are 92.7%, 91.8%, and 93.3%; for Δρ < 2%, the values are 53.5%, 51.3%, and 54.7%. At the level of B3LYP/6-31G**, for Δρ < 5%, the values are 92.3%, 91.4% and 92.9%; for Δρ < 2%, the values are 53.7%, 51.4% and 53.2%.

Nowadays, oil pollution of water caused by illegal discharges or accidental events occurs frequently, and the waste of resources and environmental pollution cannot be ignored, so effective oil–water separation methods are needed to cope with such incidents. To solve these problems, this paper investigated an aerogel made from a plant polysaccharide, konjac glucomannan (KGM), supplemented with graphene oxide (GO), to improve the mechanical properties. Finally, a hydrophobic layer was attached to the surface and interior of the aerogel via chemical vapor deposition to improve its selectivity toward oil. Through a series of characterization methods such as infrared, X-ray photoelectron spectroscopy, and X-ray diffraction, it was demonstrated that KGM and GO were successfully cross-linked, resulting in excellent mechanical properties and directional absorption properties on oil. This composite polysaccharide aerogel could absorb oil 48 times its own weight. In addition, due to its strong mechanical properties, the gel can be reused many times, and the maximum recovery rate can be maintained at 96% after 10 cycles. Furthermore, the absorption of oil from water was conducted in a continuous mode, demonstrating the diversity of application scenarios. Generally, the results observed in this work have shown that the KGM aerogels have great potential for applications in oil–water separation.