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Can electric fields drive chemistry for an aqueous microdroplet?

Author

Listed:
  • Hongxia Hao

    (University of California
    University of California
    University of California)

  • Itai Leven

    (University of California
    University of California
    University of California)

  • Teresa Head-Gordon

    (University of California
    University of California
    University of California
    University of California)

Abstract

Reaction rates of common organic reactions have been reported to increase by one to six orders of magnitude in aqueous microdroplets compared to bulk solution, but the reasons for the rate acceleration are poorly understood. Using a coarse-grained electron model that describes structural organization and electron densities for water droplets without the expense of ab initio methods, we investigate the electric field distributions at the air-water interface to understand the origin of surface reactivity. We find that electric field alignments along free O–H bonds at the surface are ~16 MV/cm larger on average than that found for O–H bonds in the interior of the water droplet. Furthermore, electric field distributions can be an order of magnitude larger than the average due to non-linear coupling of intramolecular solvent polarization with intermolecular solvent modes which may contribute to even greater surface reactivity for weakening or breaking chemical bonds at the droplet surface.

Suggested Citation

  • Hongxia Hao & Itai Leven & Teresa Head-Gordon, 2022. "Can electric fields drive chemistry for an aqueous microdroplet?," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-021-27941-x
    DOI: 10.1038/s41467-021-27941-x
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    Cited by:

    1. Ruijuan Zhao & Lei Li & Qianbao Wu & Wei Luo & Qiu Zhang & Chunhua Cui, 2024. "Spontaneous formation of reactive redox radical species at the interface of gas diffusion electrode," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    2. Kit Joll & Philipp Schienbein & Kevin M. Rosso & Jochen Blumberger, 2024. "Machine learning the electric field response of condensed phase systems using perturbed neural network potentials," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

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