Direct prediction of gas adsorption via spatial atom interaction learning.

Physisorption relying on crystalline porous materials offers prospective avenues for sustainable separation processes, greenhouse gas capture, and energy storage. However, the lack of end-to-end deep learning model for adsorption prediction confines the rapid and precise screen of crystalline porous materials. Here, we present DeepSorption, a spatial atom interaction learning network that realizes accurate, fast, and direct structure-adsorption prediction with only information of atomic coordinate and chemical element types. The breakthrough in prediction is attributed to the awareness of global structure and local spatial atom interactions endowed by the developed Matformer, which provides the intuitive visualization of atomic-level thinking and executing trajectory in crystalline porous materials prediction. Complete adsorption curves prediction could be performed using DeepSorption with a higher accuracy than Grand canonical Monte Carlo simulation and other machine learning models, a 20-35% decline in the mean absolute error compared to graph neural network CGCNN and machine learning models based on descriptors. Since the established direct associations between raw structure and target functions are based on the understanding of the fundamental chemistry of interatomic interactions, the deep learning network is rationally universal in predicting the different physicochemical properties of various crystalline materials.

Effects of Ethanol Treatment on Storage Quality and Antioxidant System of Postharvest Papaya.

Papaya is the fourth most favored tropical fruit in the global market; it has rich nutrition and can be used for medicine and food processing. However, it will soften and mature in a short time after harvest, resulting in a lot of economic losses. In this study, papaya fruits were soaked in 0, 12.5, 25, 50, and 100 ml/L ethanol solutions for 2 h and stored at 25°C for 14 days, by which we explored the effects of ethanol treatment in papaya after harvest. At an optimal concentration of ethanol treatment, color changing of the papaya fruits was delayed for 6 days, and decay incidence and average firmness of the fruits were shown as 20% and 27.7 N, respectively. Moreover, the effect of ethanol treatment on antioxidant systems in the papaya fruits was explored. It was observed that ethanol treatment contributed to diminish the development of malondialdehyde (MDA), ethylene, and superoxide anions. Furthermore, the activities of superoxide dismutase (SOD), catalase (CAT), and ascorbate peroxidase (APX) were promoted than those of control group, while the activities of peroxidase (POD), phenylalanine ammonia-lyase (PAL), and polyphenol oxidase (PPO) were brought down. In addition, the principal component analysis (PCA) showed that PAL, ethylene, and superoxide anions were the main contributors for the maturity and senescence of postharvest papaya. In this experiment, ethanol treatment had the potential of delaying the ripening and maintaining the storage quality of papaya fruits.