RESUMO
CoNi-LDH (layered CoNi double hydroxides) hollow nanocages with specific morphology are obtained by Ni ion etching of ZIF-67 (Zeolitic imidazolate framework-67). The structure of the layered materials is further modified by molecular intercalation. The original interlayer anions are replaced by the ion exchange effect of terephthalic acid, which helps to increase the interlayer distance of the material. The intercalated cage-like structures not only benefit for the storage of oxygen, and the discharge product reaction, but also have more support between the material layers. The experimental results show that the excessive use of intercalation agent will affect structural stability of the intercalated CoNi-LDH. By adjusting the amount of terephthalic acid, the intercalated CoNi-LDH-2 (with 0.02 mmol terephthalic acid intercalated) is not easy to collapse after 209 cycles and shows the best electrochemical performance in Li-O2 battery.
RESUMO
In MOOC learning, learners' emotions have an important impact on the learning effect. In order to solve the problem that learners' emotions are not obvious in the learning process, we propose a method to identify learner emotion by combining eye movement features and scene features. This method uses an adaptive window to partition samples and enhances sample features through fine-grained feature extraction. Using an adaptive window to partition samples can make the eye movement information in the sample more abundant, and fine-grained feature extraction from an adaptive window can increase discrimination between samples. After adopting the method proposed in this paper, the four-category emotion recognition accuracy of the single modality of eye movement reached 65.1% in MOOC learning scenarios. Both the adaptive window partition method and the fine-grained feature extraction method based on eye movement signals proposed in this paper can be applied to other modalities.
