Recognition and measuring particles on microscopy images is an important part of many scientific studies, including catalytic investigations. In this paper, we present the results of the application of deep learning to the automated recognition of nanoparticles deposited on porous supports (heterogeneous catalysts) on images obtained by transmission electron microscopy (TEM). The Cascade Mask-RCNN neural network was used. During the training, two types of objects were labeled on raw TEM images of ‘real’ catalysts: visible particles and overlapping particle projections. The trained neural network recognized nanoparticles in the test dataset with 0.71 precision and 0.72 recall for both classes of objects and 0.84 precision and 0.79 recall for visible particles. The developed model is integrated into the open-access web service ‘ParticlesNN’, which can be used by any researcher in the world. Instead of hours, TEM data processing per one image analysis is reduced to a maximum of a couple of minutes and the divergence of mean particle size determination is approximately 2% compared to manual analysis. The proposed tool encourages accelerating catalytic research and improving the objectivity and accuracy of analysis.

Iridium and rhodium complexes anchored on silica gel surface via NH₂–(CH₂)₃– and P(Ph)₂–(CH₂)₂– linker groups achieve high (∼9%) efficiency in pairwise addition of parahydrogen to unsaturated gaseous substrates in heterogeneous hydrogenation processes.

Methane oxidative coupling (OCM) is considered a potential direct route to produce C₂ hydrocarbons. Layered perovskite-like Sr₂TiO₄ is a promising OCM catalyst. Mechanochemical activation (MA) is known to be an environmentally friendly method for perovskite synthesis. Sr₂TiO₄ were synthesized using MA of the mixtures containing SrCO₃ or SrO and TiO₂ or TiO(OH)₂ and annealing at 900 and 1100 °C. XRD and FT-IRS showed that MA leads to the starting component disordering and formation of SrTiO₃ only for SrO being pronounced when using TiO(OH)₂. After annealing at 900 °C, Sr₂TiO₄ was mainly produced from the mixtures of SrCO₃ or SrO and TiO(OH)₂. The single-phase Sr₂TiO₄ was only obtained from MA products containing SrCO₃ after calcination at 1100 °C. The surface enrichment with Sr was observed by XPS for all samples annealed at 1100 °C depending on the MA product composition. The OCM activity of the samples correlated with the surface Sr concentration and the ratio of the surface oxygen amount in SrO and perovskite (O_o/O_p). The maximal CH₄ conversion and C₂ yield (25.6 and 15.5% at 900 °C, respectively), and the high long-term stability were observed for the sample obtained from (SrCO₃ + TiO₂), showing the specific surface morphology and optimal values of the surface Sr concentration and O_o/O_p ratio.