The cage and ladder structured phosphorus-containing polyhedral oligomeric silsesquioxanes (DOPO-POSS) have been synthesized through the hydrolytic condensation of 9,10-dihydro-9-oxa-10-phosphenanthrene-10-oxide (DOPO)-vinyl triethoxysilane (VTES). The unique ladder and cage–ladder structured components in DOPO-POSS endowed it with good solubility in vinyl epoxy resin (VE), and it was used with tetrabutyl titanate (TBT) to construct a phosphorus-silicon-titanium synergy system for the flame retardation of VE. Thermal stabilities, mechanical properties, and flame retardancy of the resultant VE composites were investigated by thermal gravimetric analysis (TGA), dynamic mechanical analysis (DMA), three-point bending tests, limiting oxygen index (LOI) measurement, and cone calorimetry. The experimental results showed that with the addition of only 4 wt% DOPO-POSS and 0.5 wt% TBT, the limiting oxygen index value (LOI) increased from 19.5 of pure VE to 24.2. With the addition of DOPO-POSS and TBT, the peak heat release rate (PHRR), total heat release (THR), smoke production rate (SPR), and total smoke production (TSP) were decreased significantly compared to VE-0. In addition, the VE composites showed improved thermal stabilities and mechanical properties comparable to that of the VE-0. The investigations on pyrolysis volatiles of cured VE further revealed that DOPO-POSS and TBT exerted flame retardant effects in gas phase. The results of char residue of the VE composites by SEM and XPS showed that TBT and DOPO-POSS can accelerate the char formation during the combustion, forming an interior char layer with the honeycomb cavity structure and dense exterior char layer, making the char strong with the formation of Si-O-Ti and Ti-O-P structures.
In this article, a kind of polyester-type phthalonitrile cyano resin (2,2-bis (((3-((4-(3,4-dicyanophenoxy) benzoyl)oxy)-2-(hydroxymethyl)-2-methylpropanoyl)oxy)methyl) propane-1,3-diyl)bis(oxy)) bis (2-(hydroxymethyl)-2-methyl-3-oxopropane-3, 1-diyl) bis (4-(3,4-dicyanophenoxy) benzoate (hbppn)) with branched structure was introduced. The molecular structure and relative molecular mass of hbppn were characterized by nuclear magnetic resonance, Fourier transform infrared spectroscopy, and the matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF). The results showed that the synthesized HBPPN contained both polyester and hyperbranched structures. The thermal and rheological properties of HBPPN were characterized by differential scanning calorimetry and rheometer, and the results showed that HBPPN would be cured at about 322°C and the viscosity of the resin showed good processability. The results of dynamic mechanical analysis and thermogravimetric analysis showed that the synthesized resin had good heat resistance. The glass transition temperature was above 329°C, the residual weight ( C y) at 900°C was as high as 66.2% in the nitrogen atmosphere, and the temperature at which the resin lost 5 wt% of heat in air atmosphere was about 423.3°C. The synthesized HBPPN had good comprehensive properties, which could be applied to high-temperature resistant and thermal protection materials.
Most object detection methods based on remote sensing images are generally dependent on a large amount of high-quality labeled training data. However, due to the slow acquisition cycle of remote sensing images and the difficulty in labeling, many types of data samples are scarce. This makes few-shot object detection an urgent and necessary research problem. In this paper, we introduce a remote sensing few-shot object detection method based on text semantic fusion relation graph reasoning (TSF-RGR), which learns various types of relationships from common sense knowledge in an end-to-end manner, thereby empowering the detector to reason over all classes. Specifically, based on the region proposals provided by the basic detection network, we first build a corpus containing a large number of text language descriptions, such as object attributes and relations, which are used to encode the corresponding common sense embeddings for each region. Then, graph structures are constructed between regions to propagate and learn key spatial and semantic relationships. Finally, a joint relation reasoning module is proposed to actively enhance the reliability and robustness of few-shot object feature representation by focusing on the degree of influence of different relations. Our TSF-RGR is lightweight and easy to expand, and it can incorporate any form of common sense information. Sufficient experiments show that the text information is introduced to deliver excellent performance gains for the baseline model. Compared with other few-shot detectors, the proposed method achieves state-of-the-art performance for different shot settings and obtains highly competitive results on two benchmark datasets (NWPU VHR-10 and DIOR).
Temporal action detection in long, untrimmed videos is an important yet challenging task that requires not only recognizing the categories of actions in videos, but also localizing the start and end times of each action. Recent years, artificial neural networks, such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) improve the performance significantly in various computer vision tasks, including action detection. In this paper, we make the most of different granular classifiers and propose to detect action from fine to coarse granularity, which is also in line with the people’s detection habits. Our action detection method is built in the ‘proposal then classification’ framework. We employ several neural network architectures as deep information extractor and segment-level (fine granular) and window-level (coarse granular) classifiers. Each of the proposal and classification steps is executed from the segment to window level. The experimental results show that our method not only achieves detection performance that is comparable to that of state-of-the-art methods, but also has a relatively balanced performance for different action categories.
In order to expand the application of phenolic-type phthalonitrile resin in high-temperature fields, a series of organic–inorganic hybrid materials have been prepared via conventional blending and doping method. The chemical transformations were monitored by various measurements, while the curing behavior was evaluated by differential scanning calorimetry (DSC), and these new blends could be also cured under auto-catalytic process. The onset polymerization exothermic temperature shifted to lower temperatures (195.3°C). Later, the compatibility within the cured products was analyzed by using energy dispersive spectrometer (EDS) and scanning electron microscope (SEM), where no phase separation occurred between the ceramic domain and the phthalonitrile polymer. Upon curing, the thermal properties of the polymers were characterized by dynamic thermomechanical analysis (DMA) and thermogravimetric analysis (TGA), where enhanced heat resistance and thermal stability were discovered, The blends residual weight (Cy) value was 57.6% with 15 wt.% SiBCN at 1000°C. And when blended with SiBCN precursor, no peak or onset point could be observed in the temperature range (50 to 500°C), which indicated the glass transition temperature greater than 500°C. Additionally, the dielectric properties were evaluated. And when the content was 5 wt.%, the blends dielectric loss was 0.0043 and the permittivity was 4.31. The above results indicated that the introduction of ceramic precursors could enhance the thermal performance of phthalonitrile polymers, consequently the hybrid materials shown great potential in the application of higher temperature fields.
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