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Cocrystallization has been applied widely for material synthesis. Recently cocrystal of organic molecules has been developing rapidly, taking the advantages of the flexibility and self-assembly of organic molecules. Here we report an experimental study of a cocrystal of copper-phthalocyanines and fluorinated ones. We have grown the samples via the vapor-phase deposition of the mixture with different mass ratios from 1:13.5 to 6:1. As suggested by our scanning electron microscopy (SEM), X-ray diffraction (XRD), and Raman spectroscopy, new crystal structures and morphologies through our novel strategy for the cocrystallization of these molecules have been found. Our work will provide a solid foundation to systematically synthesize the cocrystal of phthalocyanine molecules with new crystal structures, thus providing the opportunity to advance material properties.

The core of intelligent garbage sorting is target identification and detection. In order to achieve effective garbage sorting, on the basis of deep learning, the Faster R-CNN target detection model and ResNet50 image classification model are used to identify and train 3984 garbage images, and predict 3552 images. The results show that the accuracy of garbage recognition is 89.681%, the average accuracy of each garbage prediction is 91.68%, and the accuracy of each category of garbage image prediction is over 93.3%. Through the identification, detection and classification prediction of garbage images, it provides data support for the intelligent classification of domestic garbage.