We investigate the evaporation dynamics of a sessile droplet of
ethanol–water binary mixtures of different compositions laden
with alumina nanoparticles and compare with the no-loading condition
at different substrate temperatures. Shadowgraphy and infrared imaging
methods are used, and the experimental images are postprocessed using
a machine learning technique. We found that the loading and no-loading
cases display distinct wetting and contact angle dynamics. Although
the wetting diameter of a droplet decreases monotonically in the absence
of loading, the droplet with 0.6 wt % nanoparticle loading remains
pinned for the majority of its lifetime. The temporal variation of
the normalized droplet volume in the no-loading case has two distinct
slopes, with ethanol and water phases dominating the early and late
stages of evaporation, respectively. The normalized droplet volume
with 0.6 wt % loading displays a nearly linear behavior because of
the increase in the heat transfer rate. Our results from infrared
imaging reveal that a nanofluid droplet displays far richer thermal
patterns than a droplet without nanoparticle loading. In nanoparticle-laden
droplets, the pinning effect, as well as the resulting thermo-capillary
and thermo-solutal convection, causes more intense internal mixing
and a faster evaporation rate. Finally, a theoretical model is also
developed that satisfactorily predicts the evaporation dynamics of
binary nanofluid droplets.