A data-driven approach for the guided regulation of exposed facets in nanoparticles

ORAL

Abstract

High-index facet nanoparticles, such as tetrahexahedron (THH) and hexoctahedron (HOH), offer significant potential in catalysis due to their unique surface structures, yet their synthesis is challenging due to thermodynamic instability. We present a data-driven approach to address these challenges by incorporating high-throughput density functional theory (DFT) calculations and machine learning techniques. We explore the surface energies of low- and high-index facets for nine transition metals modified by 13 guest atoms. Machine learning focuses on understanding key factors stabilizing high-index facets, particularly {210} facets of THH, and extends material discovery from monometallic hosts to multimetallic structures. Our findings are validated through chemical synthesis, successfully producing THH nanoparticles with exposed {210} facets.

In parallel, we demonstrate a method to control the shape evolution of Cu nanoparticles between THH and HOH by tuning Te atom concentration. DFT calculations show that the surface density of Te atoms alters the stability of {210} and {421} facets. Through controlled annealing and dealloying of CuTe nanoparticles, we achieve selective synthesis of THH or HOH, driven by Te surface concentration.

*Research was sponsored by the Army Research Ofice under grants W911NF-23-1-0141 and W911NF-23-1-0285, the Toyota Research Institute, Inc., and the Sherman Fairchild Foundation, Inc. D.K. acknowledges funding from the International Institute for Nanotechnology. Z.Y. and D.K. acknowledge partial support from the Predictive Science and Engineering Design program at Northwestern University. J.S. acknowledges support from the MRSEC program (DMR-1720139) at the Materials Research Center of Northwestern University. This work made use of the EPIC and BioCryo facilities of Northwestern University's NUANCE Center, which has received support from the SHyNE Resource (NSF ECCS-2025633), the International Institute for Nanotechnology and Northwestern's MRSEC program (NSF DMR-1720139). We acknowledge the computational resources provided by the Quest high-performance computing facility at Northwestern University.

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Publication: 1. Huang, L. et al. Shape regulation of high-index facet nanoparticles by dealloying. Science 365, 1159–1163 (2019).
2. Ji, L. et al. Shape reconstruction from commercial Pt/C to high-index Pt/C for advanced electrocatalysts. ACS Catal. 13, 13846–13855 (2023).
3. Huang, L. et al. High-index-facet metal-alloy nanoparticles as fuelcell electrocatalysts. Adv. Mater. 32, e2002849 (2020).
4. Huang, L. et al. Multimetallic high-index faceted heterostructured nanoparticles. J. Am. Chem. Soc. 142, 4570–4575 (2020).
5. Shen, B. et al. Crystal structure engineering in multimetallic high-index facet nanocatalysts. Proc. Natl Acad. Sci. USA 118, e2105722118 (2021).

Presenters

  • Dohun Kang

    • Northwestern University

Authors

  • Dohun Kang

    • Northwestern University

  • Zihao Ye

    • Northwestern University

  • Bo Shen

    • Northwestern University

  • Jiahong Shen

    • Northwestern University

  • Jin Huang

    • Northwestern University

  • Zhe Wang

    • Northwestern University

  • Liliang Huang

    • Northwestern University

  • Carolin Wahl

    • Northwestern University

  • Donghoon Shin

    • Northwestern University

  • Christopher M Wolverton

    • Northwestern University

  • Chad A Mirkin

    • Northwestern University