Browsing by Author "Yan, Zihao"
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- Breaking adsorption-energy scaling limitations of electrocatalytic nitrate reduction on intermetallic CuPd nanocubes by machine-learned insightsGao, Qiang; Pillai, Hemanth Somarajan; Huang, Yang; Liu, Shikai; Mu, Qingmin; Han, Xue; Yan, Zihao; Zhou, Hua; He, Qian; Xin, Hongliang; Zhu, Huiyuan (Nature Portfolio, 2022-04-29)Machine learning is a powerful tool for screening electrocatalytic materials. Here, the authors reported a seamless integration of machine-learned physical insights with the controlled synthesis of structurally ordered intermetallic nanocrystals and well-defined catalytic sites for efficient nitrate reduction to ammonia. The electrochemical nitrate reduction reaction (NO3RR) to ammonia is an essential step toward restoring the globally disrupted nitrogen cycle. In search of highly efficient electrocatalysts, tailoring catalytic sites with ligand and strain effects in random alloys is a common approach but remains limited due to the ubiquitous energy-scaling relations. With interpretable machine learning, we unravel a mechanism of breaking adsorption-energy scaling relations through the site-specific Pauli repulsion interactions of the metal d-states with adsorbate frontier orbitals. The non-scaling behavior can be realized on (100)-type sites of ordered B2 intermetallics, in which the orbital overlap between the hollow *N and subsurface metal atoms is significant while the bridge-bidentate *NO3 is not directly affected. Among those intermetallics predicted, we synthesize monodisperse ordered B2 CuPd nanocubes that demonstrate high performance for NO3RR to ammonia with a Faradaic efficiency of 92.5% at -0.5 V-RHE and a yield rate of 6.25 mol h(-1) g(-1) at -0.6 V-RHE. This study provides machine-learned design rules besides the d-band center metrics, paving the path toward data-driven discovery of catalytic materials beyond linear scaling limitations.
- Harnessing strong metal-support interactions via a reverse routeWu, Peiwen; Tan, Shuai; Moon, Jisue; Yan, Zihao; Fung, Victor; Li, Na; Yang, Shi-Ze; Cheng, Yongqiang; Abney, Carter W.; Wu, Zili; Savara, Aditya; Momen, Ayyoub M.; Jiang, De-en; Su, Dong; Li, Huaming; Zhu, Wenshuai; Dai, Sheng; Zhu, Huiyuan (2020-06-16)Engineering strong metal-support interactions (SMSI) is an effective strategy for tuning structures and performances of supported metal catalysts but induces poor exposure of active sites. Here, we demonstrate a strong metal-support interaction via a reverse route (SMSIR) by starting from the final morphology of SMSI (fully-encapsulated core-shell structure) to obtain the intermediate state with desirable exposure of metal sites. Using core-shell nanoparticles (NPs) as a building block, the Pd-FeOx NPs are transformed into a porous yolk-shell structure along with the formation of SMSIR upon treatment under a reductive atmosphere. The final structure, denoted as Pd-Fe3O4-H, exhibits excellent catalytic performance in semi-hydrogenation of acetylene with 100% conversion and 85.1% selectivity to ethylene at 80 degrees C. Detailed electron microscopic and spectroscopic experiments coupled with computational modeling demonstrate that the compelling performance stems from the SMSIR, favoring the formation of surface hydrogen on Pd instead of hydride.