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Host material stability is essential to increase the lifetime for full-spectrum luminescence in efficient organic light-emitting diodes (OLEDs), especially red phosphorescent OLEDs (PhOLEDs). While the structural diversity of red emitters has developed rapidly in recent years, high-quality host materials are scarce and still rely heavily on traditional structures. Herein, a novel strategy is proposed by introducing heptacyclic heteroaromatics with an armor-like structure to design structurally non-traditional red hosts that are rigid enough to suppress conformational relaxation through reduced vibrations, thereby inhibiting non-radiative decay and improving stability. Accordingly, the red PhOLEDs based on the host named 2-([1,1′-biphenyl]-4-yl)-4-phenyl-1,3,5-triazine (Hept-TRZ) exhibit exceptional performance with a maximum current efficiency of 30.8 cd A−1 and the highest maximum external quantum efficiency (EQE) of 29.6% at 620 nm, as well as the high luminance of 65172 cd m−2. In particular, with satisfactory morphological stability and well-balanced hole and electron transport properties, the PhOLED device retains 95% of the luminance (LT95) even after 8777 hours of continuous operation at 1000 cd m−2, firmly establishing the negligible efficiency roll-off and superior stability with the new heptacyclic host. This work validates the superiority of this molecular design strategy and provides an instructive strategy for designing effective hosts for highly efficient and stable red PhOLEDs. [ABSTRACT FROM AUTHOR]

Targeting the subtropical climate characteristics of the Guangdong - Hong Kong - Macao Greater Bay Area, this study acquired the images of the experimental area from the TerraSAR - X Radar remote sensing satellite. Given the varying scale of the surface feature targets in the Radar satellite observation scenes, this study proposed an ENet convolution spatial pyramid pooling module ( ENet - CSPP) model for surface feature classification. Since ordinary convolution can more effectively maintain domain information than atrous convolution, this study proposed a multi - scale feature fusion module based on convolution spatial pyramid pooling. Since there were a few training samples in the SAR remote sensing image dataset, this study combined the multi - scale feature fusion module with the lightweight convolutional neural network. The encoder of the ENet - CSPP network consisted of an improved ENet network and the convolution spatial pyramid pooling module. The decoder output surface feature classification images after the fusion of deep and shalow features. The quantitative comparison experiments were conducted on the GDUT - Nansha dataset. The ENet - CSPP model outperformed other models in three performance indices, namely pixel accuracy, average pixel accuracy, and mean intersection over union. This result indicates that the multi - scale lightweight model effectively improved the accuracy of surface feature classification. [ABSTRACT FROM AUTHOR]