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.

No abstract

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.

No abstract

No abstract

No abstract

No abstract

In this work, a series of K2CO3-containing composite materials based on alumina supports with different porous structure were synthesized and studied in a direct air capture process. Alumina supports with the modified porous structure were obtained as a result of the thermal treatment of porous γ-Al2O3 at elevated temperatures. Composite materials were synthesized by impregnating the porous support (unmodified or modified alumina) with an aqueous solution of potassium carbonate. All the K2CO3/Al2O3 sorbents were tested in the process of CO2 absorption from the air with a relative humidity of 25% followed by thermal desorption as a result of heating the material to 200 °C. The composite materials were characterized by X-ray diffraction and temperature-programmed desorption methods. Among the materials studied, the composite sorbent based on the porous alumina thermally modified at T = 750 °C demonstrated the highest dynamic CO2 absorption capacity. This composite material was later tested in a direct air capture/methanation process combining CO2 capture from ambient air and methanation via the catalytic Sabatier reaction. The process was implicated using an adsorber and a catalytic reactor connected in series. To regenerate the composite sorbent after the step of CO2 absorption from ambient air, the adsorber was heated to 200 °C in an H2 flow. The desorbed CO2 was converted into methane in the preheated catalytic reactor containing the Ru/Al2O3 methanation catalyst. The optimization of the operating conditions (namely, the catalytic reactor temperature and the inlet H2 flow rate) allowed for obtaining CH4 from carbon dioxide with a yield of 98%. The thermal energy required for heating the new CO2 sorbent from 25 to 200 °C at the desorption/methanation step of the direct air capture/methanation process was estimated to be 9 MJ per 1 m3 (STP) of produced CH4.