Highlights Lamellar-structured graphene aerogels with vertically aligned and closely stacked high-quality graphene lamellae are fabricated. The superior thermally conductive capacity of the aerogel endows epoxy with a high through-plane thermal conductivity of 20.0 W m −1 K −1 at 2.30 vol% of graphene content. The nacre-like structure endows the epoxy composite with enhanced fracture toughness. Abstract Although thermally conductive graphene sheets are efficient in enhancing in-plane thermal conductivities of polymers, the resulting nanocomposites usually exhibit low through-plane thermal conductivities, limiting their application as thermal interface materials. Herein, lamellar-structured polyamic acid salt/graphene oxide (PAAS/GO) hybrid aerogels are constructed by bidirectional freezing of PAAS/GO suspension followed by lyophilization. Subsequently, PAAS monomers are polymerized to polyimide (PI), while GO is converted to thermally reduced graphene oxide (RGO) during thermal annealing at 300 °C. Final graphitization at 2800 °C converts PI to graphitized carbon with the inductive effect of RGO, and simultaneously, RGO is thermally reduced and healed to high-quality graphene. Consequently, lamellar-structured graphene aerogels with superior through-plane thermal conduction capacity are fabricated for the first time, and its superior through-plane thermal conduction capacity results from its vertically aligned and closely stacked high-quality graphene lamellae. After vacuum-assisted impregnation with epoxy, the resultant epoxy composite with 2.30 vol% of graphene exhibits an outstanding through-plane thermal conductivity of as high as 20.0 W m −1 K −1 , 100 times of that of epoxy, with a record-high specific thermal conductivity enhancement of 4310%. Furthermore, the lamellar-structured graphene aerogel endows epoxy with a high fracture toughness, ~ 1.71 times of that of epoxy. Electronic supplementary material The online version of this article (10.1007/s40820-020-00548-5) contains supplementary material, which is available to authorized users.

In this work, we propose an efficient and accurate monocular 3D detection framework in single shot. Most successful 3D detectors take the projection constraint from the 3D bounding box to the 2D box as an important component. Four edges of a 2D box provide only four constraints and the performance deteriorates dramatically with the small error of the 2D detector. Different from these approaches, our method predicts the nine perspective keypoints of a 3D bounding box in image space, and then utilize the geometric relationship of 3D and 2D perspectives to recover the dimension, location, and orientation in 3D space. In this method, the properties of the object can be predicted stably even when the estimation of keypoints is very noisy, which enables us to obtain fast detection speed with a small architecture. Training our method only uses the 3D properties of the object without the need for external networks or supervision data. Our method is the first real-time system for monocular image 3D detection while achieves state-ofthe-art performance on the KITTI benchmark. Code will be released at https://github.com/Banconxuan/RTM3D.

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