The promoting effect of alkali nitrates molten salt on the CO 2 capture capacity of a commercial MgO was investigated in detail. In particular, the ratio of Li/Na/K nitrates and the loading of the molten salt mixture on the MgO particles were optimized, and the influence of calcination and adsorption temperatures was evaluated. The MgO sample doped with 10 mol % (Li 0.3 Na 0.6 K 0.1 )NO 3 was demonstrated to possess the highest CO 2 uptake (up to 16.8 mmol g −1 ), which is the highest value reported for MgO based adsorbents in the literature. The CO 2 adsorption/ desorption cycling stability was studied using both temperature swing adsorption (TSA) and pressure swing adsorption (PSA). The morphology and structure of the optimized adsorbent, 10 mol % (Li 0.3 Na 0.6 K 0.1 )NO 3 • MgO, were characterized thoroughly using XRD, SEM, FTIR, and BET analyses. The thermal stability of doped alkali nitrates was investigated via temperature program desorption and XRD analysis, which indicated that the phase status of the molten salts is crucial for the marked improvement in CO 2 capture capacity of MgO.
Smart adsorbents for CO 2 capture: Making strong adsorption sites respond to visible light SCIENCE CHINA Materials A flexible metal-organic framework with double interpenetration for highly selective CO 2 capture at room temperature SCIENCE CHINA Chemistry 59, 965 (2016); Preparation and kinetic analysis of Li 4 SiO 4 sorbents with different silicon sources for high temperature CO 2 capture
CO2 reduction is crucial if the effects of this gas on global warming are to be alleviated. We report for the first time an alkali carbonate molten salt promoted CaO‐based CO2 sorbent with CO2 capture performance superior to that of neat CaO. The influences of chemical composition, loading, and melting temperature of the (Li–Na–K)2CO3 molten salts and of the calcination and adsorption temperatures on CO2 capture were evaluated systematically. The microstructural and morphological evolution of the samples during CO2 adsorption was studied by X‐ray diffraction, scanning electron microscopy, and Fourier‐transform infrared spectroscopy analyses. The (Li–K)2CO3 molten salt coating was found not only to promote CO2 uptake but also to facilitate CO2 desorption from CaO. In particular, at low temperatures of 500 and 600 °C, the CO2 capture capacity increased significantly from 1.19 and 3.26 mmol g−1 to 6.93 and 10.38 mmol g−1, respectively. The melting point of the molten salts was also a crucial factor in the improvement of CO2 uptake. Kinetic studies based on fractal‐like models indicated that the rate coefficients for (Li–K)2CO3/CaO were approximately 3.3 to 3.8 times larger than those for neat CaO. The coating of alkali carbonate molten salts is believed to prevent the formation of a rigid CaCO3 layer on the surface of the CaO particles and to provide continuous delivery of CO32− to promote CO2 capture. During the CO2 adsorption/desorption cycling tests, (Li–K)2CO3/CaO resulted in a stable and reversible CO2 uptake of 6.0–6.3 mmol g−1, which is much higher than that of neat CaO (2.0 mmol g−1).
Fine-grained image classification methods often suffer from the challenge that the subordinate categories within an entry-level category can only be distinguished by subtle differences. Crop disease classification is affected by various visual interferences, including uneven illumination, dew, and equipment jitter. It demands an effective algorithm to accurately discriminate one category from the others. Thus, the representational ability of algorithm needs to be strengthened to learn a robust domain-specific discrimination through an effective way. To address this challenge, a unified convolutional neural network (CNN) denoting the matrix-based convolutional neural network (M-bCNN) was proposed. Its hallmark is the convolutional kernel matrix, whose convolutional layers are arranged parallelly in the form of a matrix, and integrated with DropConnect, exponential linear unit, local response normalization, and so on to defeat over-fitting and vanishing gradient. With a tolerable addition of parameters, it can effectively increase the data streams, neurons, and link channels of the model compared with the commonly used plain networks. Therefore, it will create more non-linear mappings and will enhance the representational ability with a tolerable growth of parameters. The images of winter wheat leaf diseases were utilized as experimental samples for their strong similarities among sub-categories. A total of 16 652 images containing eight categories were collected from Shandong Province, China, and were augmented into 83 260 images. The M-bCNN delivered significant improvements and achieved an average validation accuracy of 96.5% and a testing accuracy of 90.1%; this outperformed AlexNet and VGG-16. The M-bCNN demonstrated accuracy gains with a convolutional kernel matrix in fine-grained image classification. INDEX TERMS Convolutional neural network, fine-grained image classification, deep learning, convolutional kernel matrix, wheat leaf diseases.
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