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The non-rare-earth phosphor Ca2ZnMoO6 has a luminescence of 375–655 nm and emits near-white light. This study investigated it as a down-shifting layer to improve the efficiency of commercial flexible solar cells. The first structure was a solar cell/thick-film Ca2ZnMoO6 phosphor layer (TLDU device); a controlled light source was shone on the top, and UV light was shone on the bottom. The second structure was a solar cell/thick-film Ca2ZnMoO6 phosphor layer with an Al reflective layer (TLDR device), and only a controlled light source was used. We compared the power output efficiencies of the two structures with the efficiency of a control device. When the [Formula: see text]–[Formula: see text] properties of the three different devices were measured, the [Formula: see text] value underwent no apparent change, but the [Formula: see text] increased in the TLDU and TLDR devices. These results suggest that if placed on the bottom surface of commercial solar cells, Ca2ZnMoO6 phosphor could improve their efficiency.

Video‐based facial expression recognition (FER) models have achieved higher accuracy with more computation, which is not suitable for online deployment in mobile intelligent terminals. Facial landmarks can model facial expression changes with their spatial location information instead of texture features. But classical convolution operation cannot make full use of landmark information. To this end, in this paper, we propose a novel long short memory network (LSTM) by embedding graph convolution named GELSTM for online video‐based FER in mobile intelligent terminals. Specifically, we construct landmark‐based face graph data from the client. On the server side, we introduce graph convolution which can effectively mine spatial dependencies information in a landmark‐based facial graph. Moreover, the extracted landmark's features are fed to LSTM for temporal feature aggregation. We conduct experiments on the facial expression dataset and the results show our proposed method shows superior performance compared to other deep models.