Spreading fronts of wetting liquid droplets: microscopic simulations and universal fluctuations

TL;DR

This study uses kinetic Monte Carlo simulations to analyze the microscopic fluctuations and scaling behaviors of liquid droplet spreading fronts, revealing diffusive growth, temperature-dependent roughening, and anomalous scaling phenomena with connections to KPZ universality.

Contribution

It demonstrates the presence of intrinsic anomalous scaling in droplet spreading fronts and compares discrete lattice gas models with continuum equations, highlighting differences in scaling behavior.

Findings

Front radius grows as t^{1/2} across conditions.

Front fluctuations show temperature-dependent kinetic roughening.

Evidence of intrinsic anomalous scaling with different short and large scale roughness exponents.

Abstract

We have used kinetic Monte Carlo (kMC) simulations of a lattice gas to study front fluctuations in the spreading of a non-volatile liquid droplet onto a solid substrate. Our results are consistent with a diffusive growth law for the radius of the precursor layer, $R \sim t^{\delta}$, with $\delta \approx 1/2$ in all the conditions considered for temperature and substrate wettability, in good agreement with previous studies. The fluctuations of the front exhibit kinetic roughening properties with exponent values which depend on temperature $T$, but become $T$-independent for sufficiently high $T$. Moreover, strong evidences of intrinsic anomalous scaling have been found, characterized by different values of the roughness exponent at short and large length scales. Although such a behavior differs from the scaling properties of the one-dimensional Kardar-Parisi-Zhang (KPZ) universality class, the front covariance and the probability distribution function of front fluctuations found in our kMC simulations do display KPZ behavior, agreeing with simulations of a continuum height equation proposed in this context. However, this equation does not feature intrinsic anomalous scaling, at variance with the discrete model.