Evaporation Dynamics of a Sessile Droplet of Binary Mixture Laden with Nanoparticles

Katre, Pallavi and Balusamy, Saravanan and Banerjee, Sayak and Chandrala, Lakshmana Dora and Sahu, Kirti Chandra (2021) Evaporation Dynamics of a Sessile Droplet of Binary Mixture Laden with Nanoparticles. Langmuir, 37 (20). pp. 6311-6321. ISSN 0743-7463

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Abstract

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.

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IITH Creators:
IITH CreatorsORCiD
Sahu, Kirti Chandrahttp://orcid.org/0000-0002-7357-1141
Item Type: Article
Uncontrolled Keywords: Alumina Nanoparticle; Contact angle dynamics; Different substrates; Ethanol-water binary mixtures; Machine learning techniques; Nanoparticle loadings; Solutal convections; Theoretical modeling
Subjects: Chemical Engineering
Divisions: Department of Physics
Depositing User: . LibTrainee 2021
Date Deposited: 15 Jul 2021 05:01
Last Modified: 08 Mar 2022 10:49
URI: http://raiith.iith.ac.in/id/eprint/8330
Publisher URL: http://doi.org/10.1021/acs.langmuir.1c00806
OA policy: https://v2.sherpa.ac.uk/id/publication/7789
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