The change of CO2 carrying capacity of CaO sorbents prepared from different precursors has been studied using thermogravimetric analysis in a long series of isothermal recarbonation−decomposition cycles in the temperature range of 750−850 °C. The residual capacity of the CaO sorbents after a large number of cycles was found to depend on the precursor type, the experimental temperature, and the duration of the recarbonation stage. The residual capacities of the CaO derived from the powdered calcium carbonates were much higher than that of the CaO produced from the crystalline CaCO3. A simple tentative model has been suggested, according to which recarbonation−decomposition cycles result in formation of the interconnected CaO network that acts as a refractory support and determines sorption properties of the material. By using a new model, a simple synthesis procedure has been suggested that produces CaO sorbents with high residual CO2 carrying capacities.

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Identifying, counting and measuring particles is an important component of many research studies. Images with particles are usually processed by hand using a software ruler. Automated processing, based on conventional image processing methods (edge detection, segmentation, etc.) are not universal, can only be used on good-quality images and need to set a number of parameters empirically. In this paper, we present results from the application of deep learning to automated recognition of metal nanoparticles deposited on highly oriented pyrolytic graphite on images obtained by scanning tunneling microscopy (STM). We used the Cascade Mask-RCNN neural network. Training was performed on a dataset containing 23 STM images with 5157 nanoparticles. Three images containing 695 nanoparticles were used for verification. As a result, the trained neural network recognized nanoparticles in the verification set with 0.93 precision and 0.78 recall. Predicted contour refining with 2D Gaussian function was a proposed option. The accuracies for mean particle size calculated from predicted contours compared with ground truth were in the range of 0.87–0.99. The results were compared with outcomes from other generally available software, based on conventional image processing methods. The advantages of deep learning methods for automatic particle recognition were clearly demonstrated. We developed a free open-access web service “ParticlesNN” based on the trained neural network, which can be used by any researcher in the world.