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iOk Platform for Automatic Search and Analysis of Objects in Images Using Artificial Intelligence in the Study of Supported Catalysts

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Abstract

The iOk platform was presented, which combines the ParticlesNN web service and the DLgram and No Code ML Telegram bots for automatic search and analysis of objects in images using artificial intelligence. The platform allows one to work with any kind of images (electron, probe, and optical microscopy images and photographs) of any quality without preprocessing. Users can train a neural network on their own on their images. The results of image recognition are objects and their areas, sizes, and positions in the image. The services are in free access, and coding skills are not required to use them. The iOk platform is a user-friendly tool for working with any kind of images by automatically searching for objects and determining their parameters.

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ACKNOWLEDGMENTS

We are grateful to L.M. Kovtunova for the preparation of supported catalysts on porous supports, as well as to colleagues from the Boreskov Institute of Catalysis, Siberian Branch, Russian Academy of Sciences, Novosibirsk, Russia, in particular, E. Yu. Gerasimov for TEM imaging.

Funding

The work was supported by the Russian Science Foundation (project no. 22-23-00951, https://rscf.ru/project/22-23-00951/).

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Authors and Affiliations

  1. Boreskov Institute of Catalysis, Siberian Branch, Russian Academy of Sciences, 630090, Novosibirsk, Russia

    A. V. Nartova & N. N. Sankova

  2. Novosibirsk National Research State University, 630090, Novosibirsk, Russia

    A. V. Nartova, A. V. Matveev, M. Yu. Mashukov, V. A. Belotserkovskii, N. N. Sankova, V. Yu. Kudinov & A. G. Okunev

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  1. A. V. Nartova
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  2. A. V. Matveev
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  3. M. Yu. Mashukov
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  4. V. A. Belotserkovskii
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  5. N. N. Sankova
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  6. V. Yu. Kudinov
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  7. A. G. Okunev
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Corresponding author

Correspondence to A. V. Nartova.

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The authors declare that they have no conflicts of interest.

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Translated by V. Glyanchenko

Abbreviations and notation: TOF, turnover frequency (number of reaction cycles per active site per unit time); TEM, transmission electron microscopy; SPM, scanning probe microscopy; STM, scanning tunneling microscopy; TP, true positive (number of predicted contours that matched objects (contours labeled by the operator)); FP, false positive (number of predicted contours that do not have labeled contours); and FN, false negative (number of labeled contours that are not recognized by neural network).

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Nartova, A.V., Matveev, A.V., Mashukov, M.Y. et al. iOk Platform for Automatic Search and Analysis of Objects in Images Using Artificial Intelligence in the Study of Supported Catalysts. Kinet Catal 64, 458–465 (2023). https://doi.org/10.1134/S0023158423040092

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