Abstract Development of high‐performance, low‐cost catalysts for electrochemical water splitting is key to sustainable hydrogen production. Herein, ultrafast synthesis of carbon‐supported ruthenium–copper (RuCu/C) nanocomposites is reported by magnetic induction heating, where the rapid Joule's heating of RuCl3and CuCl2at 200 A for 10 s produces Ru–Cl residues‐decorated Ru nanocrystals dispersed on a CuClxscaffold, featuring effective Ru to Cu charge transfer. Among the series, the RuCu/C‐3 sample exhibits the best activity in 1 mKOH toward both the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER), with an overpotential of only −23 and +270 mV to reach 10 mA cm−2, respectively. When RuCu/C‐3 is used as bifunctional catalysts for electrochemical water splitting, a low cell voltage of 1.53 V is needed to produce 10 mA cm−2, markedly better than that with a mixture of commercial Pt/C+RuO2(1.59 V). In situ X‐ray absorption spectroscopy measurements show that the bifunctional activity is due to reduction of the Ru–Cl residues at low electrode potentials that enriches metallic Ru and oxidation at high electrode potentials that facilitates the formation of amorphous RuOx. These findings highlight the unique potential of MIH in the ultrafast synthesis of high‐performance catalysts for electrochemical water splitting.
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Interpretable design of Ir-free trimetallic electrocatalysts for ammonia oxidation with graph neural networks
Abstract The electrochemical ammonia oxidation to dinitrogen as a means for energy and environmental applications is a key technology toward the realization of a sustainable nitrogen cycle. The state-of-the-art metal catalysts including Pt and its bimetallics with Ir show promising activity, albeit suffering from high overpotentials for appreciable current densities and the soaring price of precious metals. Herein, the immense design space of ternary Pt alloy nanostructures is explored by graph neural networks trained on ab initio data for concurrently predicting site reactivity, surface stability, and catalyst synthesizability descriptors. Among a few Ir-free candidates that emerge from the active learning workflow, Pt3Ru-M (M: Fe, Co, or Ni) alloys were successfully synthesized and experimentally verified to be more active toward ammonia oxidation than Pt, Pt3Ir, and Pt3Ru. More importantly, feature attribution analyses using the machine-learned representation of site motifs provide fundamental insights into chemical bonding at metal surfaces and shed light on design strategies for high-performance catalytic systems beyond thed-band center metric of binding sites.
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- Award ID(s):
- 1845531
- PAR ID:
- 10480065
- Publisher / Repository:
- Nature
- Date Published:
- Journal Name:
- Nature Communications
- Volume:
- 14
- Issue:
- 1
- ISSN:
- 2041-1723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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