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Toward Totally Defined Nanocatalysis: Deep Learning Reveals the Extraordinary Activity of Single Pd/C Particlesстатья
Статья опубликована в высокорейтинговом журнале
Статья опубликована в журнале из списка Web of Science и/или Scopus- Авторы: Eremin Dmitry B., Galushko Alexey S., Boiko Daniil A., Pentsak Evgeniy O., Chistyakov Igor V., Ananikov Valentine P.
- Журнал: Journal of the American Chemical Society
- Том: 144
- Номер: 13
- Год издания: 2022
- Издательство: American Chemical Society
- Местоположение издательства: United States
- Первая страница: 6071
- Последняя страница: 6079
- DOI: 10.1021/jacs.2c01283
- Аннотация: Homogeneous catalysis is typically considered “well-defined” from the standpoint of catalyst structure unambiguity. In contrast, heterogeneous nanocatalysis often falls into the realm of “poorly defined” systems. Supported catalysts are difficult to characterize due to their heterogeneity, variety of morphologies, and large size at the nanoscale. Furthermore, an assortment of active metal nanoparticles examined on the support are negligible compared to those in the bulk catalyst used. To solve these challenges, we studied individual particles of the supported catalyst. We made a significant step forward to fully characterize individual catalyst particles. Combining a nanomanipulation technique inside a field-emission scanning electron microscope with neural network analysis of selected individual particles unexpectedly revealed important aspects of activity for widespread and commercially important Pd/C catalysts. The proposed approach unleashed an unprecedented turnover number of 109 attributed to individual palladium on a nanoglobular carbon particle. Offered in the present study is the Totally Defined Catalysis concept that has tremendous potential for the mechanistic research and development of high-performance catalysts.
- Добавил в систему: Анаников Валентин Павлович