Currently, all‐inorganic CsPbX3 (X = Br, I, and Cl) perovskites (IPs) are emerging as excellent candidate materials for exploring optoelectronic devices, due to their superior optical/electronic performances. However, their intrinsic poor stability greatly limits their practical applications. Here, a general strategy is reported for in situ growth of all‐inorganic perovskite nanocrystals (IPNCs) in polymer fibers with highly uniform size and spatial distribution, which is based on one‐step electrospinning of solutions containing IPs precursors and polymers. It is verified that the IPNCs of CsPbX3 can be uniformly encapsulated within the polymer fibers with finely tuned compositions, by rationally adjusting the ratios of PbX2 and CsX salts in the raw solutions. Consequently, the photoluminescence (PL) emissions of CsPbX3@polymer fibers can be readily tuned to cover the whole visible range. The obtained CsPbBr3@polymer fibers exhibit fundamentally improved water/thermal stabilities with a PL quantum yield (QY) of 48%. Their PL QY retains beyond 70% of its original value after being immersed in water for 192 h and maintains over 50% after being heated at 80 °C for 120 min. Furthermore, the light emitting diodes with high brightness based on CsPbBr3@polymer fibers are constructed, suggesting their promising applications.
This paper investigates the transportation and vehicular modes classification by using big data from smartphone sensors. The three types of sensors used in this paper include the accelerometer, magnetometer, and gyroscope. This study proposes improved features and uses three machine learning algorithms including decision trees, K-nearest neighbor, and support vector machine to classify the user’s transportation and vehicular modes. In the experiments, we discussed and compared the performance from different perspectives including the accuracy for both modes, the executive time, and the model size. Results show that the proposed features enhance the accuracy, in which the support vector machine provides the best performance in classification accuracy whereas it consumes the largest prediction time. This paper also investigates the vehicle classification mode and compares the results with that of the transportation modes.
Copyright by the AIP Publishing. Shukla, Nitin C.; Liao, Hao-Hsiang; Abiade, Jeremiah T.; et al., "Thermal conductivity and interface thermal conductance of amorphous and crystalline Zr47Cu31Al13Ni9 alloys with a Y2O3 coating," Appl. Phys. Lett. 94, 081912 (2009); http://dx
Inorganic perovskites (IP) CsPbX3 (X = Cl, Br, I) have rapidly emerged as excellent candidate materials for optoelectronic devices, due to their superior optical/electronic performances. However, poor stability related to...
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