Additive Engineering for Efficient and Stable MAPbI3-Perovskite Solar Cells with an Efficiency of over 21.

The high density of defects in MAPbI3 perovskite films brings about severe carrier nonradiative recombination loss, which lowers the performance of MAPbI3-based perovskite solar cells (PSCs). Here, methylamine cyanate (MAOCN) molecules were introduced into MAPbI3 solutions to manipulate the crystallizatsion of the MAPbI3 films. MAOCN molecules can slow down the volatilization rate of the solvent and delay the crystallization process of the MAPbI3 film. The crystal quality of the MAPbI3 films is effectively optimized without an additive residue. Perovskite films treated by MAOCN have lower defect density and longer carrier lifetime, which lowers the carrier recombination loss. Meanwhile, the MAPbI3 film based on MAOCN has a more hydrophobic surface. The final MAPbI3-based device efficiency reached 21.28% (VOC = 1.126 V, JSC = 23.29 mA/cm2, and FF = 81.13). After 30 days of storage under atmospheric conditions, the efficiency of unencapsulated MAOCN-based PSCs only dropped by about 5%.

Recognizing the Operating Hand and the Hand-Changing Process for User Interface Adjustment on Smartphones.

As the size of smartphone touchscreens has become larger and larger in recent years, operability with a single hand is getting worse, especially for female users. We envision that user experience can be significantly improved if smartphones are able to recognize the current operating hand, detect the hand-changing process and then adjust the user interfaces subsequently. In this paper, we proposed, implemented and evaluated two novel systems. The first one leverages the user-generated touchscreen traces to recognize the current operating hand, and the second one utilizes the accelerometer and gyroscope data of all kinds of activities in the user's daily life to detect the hand-changing process. These two systems are based on two supervised classifiers constructed from a series of refined touchscreen trace, accelerometer and gyroscope features. As opposed to existing solutions that all require users to select the current operating hand or confirm the hand-changing process manually, our systems follow much more convenient and practical methods and allow users to change the operating hand frequently without any harm to the user experience. We conduct extensive experiments on Samsung Galaxy S4 smartphones, and the evaluation results demonstrate that our proposed systems can recognize the current operating hand and detect the hand-changing process with 94.1% and 93.9% precision and 94.1% and 93.7% True Positive Rates (TPR) respectively, when deciding with a single touchscreen trace or accelerometer-gyroscope data segment, and the False Positive Rates (FPR) are as low as 2.6% and 0.7% accordingly. These two systems can either work completely independently and achieve pretty high accuracies or work jointly to further improve the recognition accuracy.