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To achieve efficient polymer-based room-temperature phosphorescence (RTP) materials, covalently embedding phosphors into the polymer matrix appeared as the most appealing approach. However, it is still highly challenging to fabricate RTP materials on a large scale because of the inefficient binding engineering and time-consuming covalent reactions. Here, we have proposed a scalable preparation approach for RTP materials by the facile B─O click reaction between boronic acid–modified phosphors and polyhydroxy polymer matrix. The ab initio molecular dynamics simulations demonstrated that the phosphors were effectively immobilized, resulting in the suppressed nonradiative transitions and activated RTP emission. In comparison to the reported covalent binding time of several hours, such a B─O click reaction can be accomplished within 20 s under ambient environment. The developed strategy simplified the construction of polymer-based RTP polymeric materials by the introduction of facile click chemistry. Our success provides inspirations and possibilities for the scale-up production of RTP materials.

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Vehicle detection is a significant and challenging task in aerial remote sensing applications. Most existing methods detect vehicles with regular rectangle boxes and fail to offer the orientation of vehicles. However, the orientation information is crucial for several practical applications, such as the trajectory and motion estimation of vehicles. In this paper, we propose a novel deep network, called a rotatable region-based residual network (R 3 -Net), to detect multioriented vehicles in aerial images and videos. More specially, R 3 -Net is utilized to generate rotatable rectangular target boxes in a half coordinate system. First, we use a rotatable region proposal network (R-RPN) to generate rotatable region of interests (R-RoIs) from feature maps produced by a deep convolutional neural network. Here, a proposed batch averaging rotatable anchor strategy is applied to initialize the shape of vehicle candidates. Next, we propose a rotatable detection network (R-DN) for the final classification and regression of the R-RoIs. In R-DN, a novel rotatable positionsensitive pooling is designed to keep the position and orientation information simultaneously while downsampling the feature maps of R-RoIs. In our model, R-RPN and R-DN can be trained jointly. We test our network on two open vehicle detection image data sets, namely, DLR 3K Munich Data set and VEDAI Data set, demonstrating the high precision and robustness of our method. In addition, further experiments on aerial videos show the good generalization capability of the proposed method and its potential for vehicle tracking in aerial videos. The demo video is available at https://youtu.be/xCYD-tYudN0.