Search

Increasing the internal light extraction efficiency of organic light‐emitting diodes (OLEDs) is key to improving their performance for solid‐state lighting applications; however, it is challenging to do this in a way that is compatible with high volume manufacturing. Here, it is shown that the outcoupling efficiency of OLEDs can be improved by diluting their hole transport layer (HTL) with the low refractive index material trifluoropropyl oligomeric silsesquioxane (F‐POSS). Specifically, co‐evaporating 40 vol.% F‐POSS in the HTL of single and multi‐stack phosphorescent OLEDs decreases its refractive index by Δn ≈ 0.2, which in turn yields a ≈12% increase in their outcoupling efficiency with no impact on electrical performance or operational lifetime. This result is significant because F‐POSS is a small molecule that sublimes cleanly, does not aggregate, and is compatible with state‐of‐the‐art HTL materials, making it a realistic path to increase light extraction in commercial OLEDs manufactured on existing production lines. [ABSTRACT FROM AUTHOR]

The precise recognition of the sources of voltage sag is the basis and key to formulating a voltage sag governance plan and clarifying responsibility for an accident. Due to the complexity of grid devices and the identification of the mode of power consumption, the conventional approach to identifying voltage sags faces a new challenge. Due to the dependence on accurate voltage sag models, the traditional methods are inadequate for complex problems with multiple uncertainty factors. The random matrix theory based on a data-driven method analyzes data correlation using single characteristic statistics, which is not suitable for the dimensional change of the matrix. It is obvious that this method is not applicable to the position of the source of the voltage sag due to random sags. Therefore, this paper proposes a voltage sag source recognition method based on the combination of random matrix theory (RMT) and a convolutional neural network (CNN). In this method, the CNN optimizes the characteristic statistics of RMT and constructs new characteristic statistics such as the correlation analysis index of the voltage sag source recognition model to avoid the error caused by a single characteristic statistic. First, random matrix theory is used to extract characteristics from historical data, and characteristic statistics under different faults and different data dimension matrices are obtained and then, are used as the input characteristic sets of the CNN. Then, through training extraction, the optimized characteristic statistics that can be applied to a variety of conditions are obtained. Furthermore, a voltage sag source recognition model based on random matrix theory is constructed by using optimized characteristic statistics. The correlation analysis index is obtained to recognize voltage sag sources under different data conditions. Finally, examples are given to verify the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]