Data-driven sparse modeling and decomposition for superspreading-wetting dynamics of a droplet

TL;DR

This paper introduces a data-driven modeling approach for droplet wetting dynamics at the nanoscale, revealing a novel transport mechanism in nanofluids that explains superspreading without surfactants.

Contribution

It develops a partial differential equation model incorporating a new transport term specific to nanofluids, supported by high-precision experimental data.

Findings

Classical lubrication physics recovered for pure solvents.

Nanofluid dynamics require a unique transport term.

The new term suggests nanoparticle-induced bias flux.

Abstract

Superspreading wetting is traditionally attributed to surfactant-driven mechanisms. However, recent observations of superspreading in surfactant-free nanofluids defy standard theoretical explanations. This study considers a data-driven approach to model droplet dynamics with the thickness of liquid films on the nanometer-micrometer scale in a compact form of a partial differential equation. We examine spatiotemporal film-thickness profiles resolved at the nanometer scale via phase-shifting imaging ellipsometry. For a pure solvent, the present governing equation recovers the classical lubrication physics driven by disjoining pressure and evaporation. In contrast, the nanofluid dynamics necessitates a unique transport term scaling with the gradient of the inverse film thickness. Theoretical analysis suggests this term represents a nanoparticle-induced bias flux, consistent with a hypothesized capillary wicking mechanism within the precursor film. The identification of the current nanofluid-specific term underscores the efficacy of integrating high-precision experimental measurements with data-driven modeling to unravel complex wetting dynamics.