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Dithienylethenes (DTEs) are a promising class of organic photoswitches that can be used to create crystalline solids with properties controlled by light. However, the ability of DTEs to adopt multiple conformations, only one of which is photoactive, complicates the rational design of these materials. Herein, the synthesis and structural characterization of 19 crystalline solids containing a single DTE molecule are described. A novelD–Danalysis of the molecular geometries obtained from rotational potential energy surface calculations and the ensemble of experimental structures were used to construct a crystal landscape for DTE. Of the 19 crystal structures, 17 contained photoinactive DTE rotamers and only 2 were photoactive. These results highlight the challenges associated with the design of these materials. Overall, theD–Danalysis described herein provides rapid, effective and intuitive means of linking the molecular structure to photoactivity that could be applied more broadly to afford a general strategy for producing photoactive diarylethene-based crystalline solids.

 

The organic salt ( S )-nicotinium 2,6-dihydroxybenzoate undergoes reversible single crystal to single crystal phase transition at 104 K. The phase transition was monitored using temperature dependent single crystal X-ray diffraction and was attributed to symmetry breaking translations and rotations of the crystal components. 

Emotions significantly impact human physical and mental health, and, therefore, emotion recognition has been a popular research area in neuroscience, psychology, and medicine. In this paper, we preprocess the raw signals acquired by millimeter-wave radar to obtain high-quality heartbeat and respiration signals. Then, we propose a deep learning model incorporating a convolutional neural network and gated recurrent unit neural network in combination with human face expression images. The model achieves a recognition accuracy of 84.5% in person-dependent experiments and 74.25% in person-independent experiments. The experiments show that it outperforms a single deep learning model compared to traditional machine learning algorithms.