Ternary heterogeneous MWCNT@TiO 2-C wave absorbent was firstly prepared, using glucose, MWCNT, and titanium isopropoxide as raw materials, through the solvothermal process followed by post-heat treatment. Afterwards, MWCNT@TiO 2-C/silicone rubber wave-absorbing composites were fabricated via solution casting and subsequent curing process. XRD, Raman, XPS, and TEM analyses demonstrated the MWCNT@TiO 2-C fillers were successfully synthesized with TiO 2 and amorphous carbon coated on the surface of MWCNT. When the MWCNT@TiO 2-C/silicone rubber wave-absorbing composites contained 25 wt% MWCNT@TiO 2-C fillers and with the thickness of 2.5 mm, it displayed the minimum reflection loss of-53.2 dB and an effective absorption bandwidth of 3.1 GHz. Remarkable wave-absorbing performances for MWCNT@TiO 2-C/silicone rubber composites could be attributed to the synergetic effect of interfacial polarization loss and conduction loss.

Scene flow estimation in the dynamic scene remains a challenging task. Computing scene flow by a combination of 2D optical flow and depth has shown to be considerably faster with acceptable performance. In this work, we present a unified framework for joint unsupervised learning of stereo depth and optical flow with explicit local rigidity to estimate scene flow. We estimate camera motion directly by a Perspective-n-Point method from the optical flow and depth predictions, with RANSAC outlier rejection scheme. In order to disambiguate the object motion and the camera motion in the scene, we distinguish the rigid region by the re-project error and the photometric similarity. By joint learning with the local rigidity, both depth and optical networks can be refined. This framework boosts all four tasks: depth, optical flow, camera motion estimation, and object motion segmentation. Through the evaluation on the KITTI benchmark, we show that the proposed framework achieves state-of-the-art results amongst unsupervised methods. Our models and code are available at https://github.com/lliuz/unrigidflow.

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In this study, an innovative, facile, and low‐cost method is developed to prepare phenolic resin (PR) containing boron and silicon (BSiPR). BSiPR is synthesized by a solvent‐free, one‐pot method using boric acid as the coupling agent instead of silane, and methyltriethoxysilane as the silicon source. The results show that boron and silicon elements are introduced into PR via BOC and BOSi structures. The char yield of the resulting resin at 800 °C is improved to 76%. The reasons for higher char yield are investigated. The formation of BOC can reduce the content of phenolic hydroxyl, which helps to decrease the weight loss. B2O3 is also formed at 400 °C, and it can prevent the release of carbon oxides. Moreover, thermally stable BOSi and SiO structures remain stable during the pyrolysis. In addition, the mechanical and ablative properties of fiber‐reinforced composites are also enhanced.