Momentum tunnelling between nanoscale liquid flows.

Coquinot, Baptiste; Bui, Anna T; Toquer, Damien; Michaelides, Angelos; Kavokine, Nikita; Cox, Stephen J; Bocquet, Lydéric

Abstract

The world of nanoscales in fluidics is the frontier where the continuum of fluid mechanics meets the atomic, and even quantum, nature of matter. While water dynamics remains largely classical under extreme confinement, several experiments have recently reported coupling between water transport and the electronic degrees of freedom of the confining materials. This avenue prompts us to reconsider nanoscale hydrodynamic flows under the perspective of interacting excitations, akin to condensed matter frameworks. Here we show, using a combination of many-body theory and molecular simulations, that the flow of a liquid can induce the flow of another liquid behind a separating wall, at odds with the prediction of continuum hydrodynamics. We further show that the range of this 'flow tunnelling' can be tuned through the solid's electronic excitations, with a maximum occurring when these are at resonance with the liquid's charge density fluctuations. Flow tunnelling is expected to play a role in global transport across nanoscale fluidic networks, such as lamellar graphene oxide or MXene membranes. It further suggests exploiting the electronic properties of the confining walls for manipulating liquids via their dielectric spectra, beyond the nature and characteristics of individual molecules. [Abstract copyright: © 2025. The Author(s), under exclusive licence to Springer Nature Limited.]

Citation

Coquinot, B., Bui, A. T., Toquer, D., Michaelides, A., Kavokine, N., Cox, S. J., & Bocquet, L. (online). Momentum tunnelling between nanoscale liquid flows. Nature Nanotechnology, https://doi.org/10.1038/s41565-024-01842-8

• Journal Article Type: Article
• Acceptance Date: Nov 26, 2024
• Online Publication Date: Jan 2, 2025
• Deposit Date: Jan 7, 2025
• Publicly Available Date: Jan 7, 2025
• Journal: Nature nanotechnology
• Print ISSN: 1748-3387
• Electronic ISSN: 1748-3395
• Publisher: Nature Research
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1038/s41565-024-01842-8
• Public URL: https://durham-repository.worktribe.com/output/3325413
• Additional Information: The simulation data that support the findings of this study are openly available at the University of Cambridge Data Repository at https://doi.org/10.17863/CAM.113204