Do Rh-Hydride Phases\nContribute to the Catalytic Activity\nof Rh Catalysts under Reductive Conditions?

Rh-hydride phases were believed to\nbe key causes of the\nexceptional\ncatalytic ability of Rh catalysts under H<sub>2</sub> reductive conditions.\nHere, we utilize the large-scale machine-learning-based global optimization\nto explore millions of Rh bulk, surface, and nanoparticle structures\nin contact with H<sub>2</sub>, which rules out the presence of subsurface/interstitial\nH in Rh and Rh-hydride phases as thermodynamically stable phases under\nambient conditions. Instead, an exceptional Rh–H affinity is\nidentified for surface Rh atoms in Rh nanoparticles that can accommodate\na high concentration of adsorbed H, with the surface Rh to H ratio\nreaching ∼2.5, featuring stable six-H-coordinated Rh, [RhH<sub>6</sub>]. Such [RhH<sub>6</sub>] species forming at edged Rh sites\nare found to be the key intermediates in the electrochemical hydrogen\nevolution reaction (HER) on Rh. Guided by theory, our synthesized\nRh concave nanocubes with a high density of edged Rh sites achieve\na Tafel slope of 28.4 mV dec<sup>–1</sup> and a low overpotential\nof 36.1 mV at <i>j</i><sub>ECSA</sub> = 1 mA cm<sup>–2</sup>, which outperforms commercial Pt/C and other morphologies of Rh\ncatalysts. Our results clarify the active phase in Rh–H nanosystems\nand guide the catalyst design by precise morphology control of nanocatalysts

Publication Details

Journal
Figshare
Published
2024-12-13
DOI
https://doi.org/10.1021/jacs.4c14404.s002
Primary Topic
Machine Learning in Materials Science
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article
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article

Do Rh-Hydride Phases\nContribute to the Catalytic Activity\nof Rh Catalysts under Reductive Conditions?

Figshare
Machine Learning in Materials Science
article

Do Rh-Hydride Phases\nContribute to the Catalytic Activity\nof Rh Catalysts under Reductive Conditions?

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Abstract

Rh-hydride phases were believed to\nbe key causes of the\nexceptional\ncatalytic ability of Rh catalysts under H<sub>2</sub> reductive conditions.\nHere, we utilize the large-scale machine-learning-based global optimization\nto explore millions of Rh bulk, surface, and nanoparticle structures\nin contact with H<sub>2</sub>, which rules out the presence of subsurface/interstitial\nH in Rh and Rh-hydride phases as thermodynamically stable phases under\nambient conditions. Instead, an exceptional Rh–H affinity is\nidentified for surface Rh atoms in Rh nanoparticles that can accommodate\na high concentration of adsorbed H, with the surface Rh to H ratio\nreaching ∼2.5, featuring stable six-H-coordinated Rh, [RhH<sub>6</sub>]. Such [RhH<sub>6</sub>] species forming at edged Rh sites\nare found to be the key intermediates in the electrochemical hydrogen\nevolution reaction (HER) on Rh. Guided by theory, our synthesized\nRh concave nanocubes with a high density of edged Rh sites achieve\na Tafel slope of 28.4 mV dec<sup>–1</sup> and a low overpotential\nof 36.1 mV at <i>j</i><sub>ECSA</sub> = 1 mA cm<sup>–2</sup>, which outperforms commercial Pt/C and other morphologies of Rh\ncatalysts. Our results clarify the active phase in Rh–H nanosystems\nand guide the catalyst design by precise morphology control of nanocatalysts

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Machine Learning in Materials Science
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