Contact-free measurement of surface tension on single droplet using machine learning and acoustic levitation
Artikel i vetenskaplig tidskrift, 2023

Hypothesis
Acoustic levitation provides the possibility to deform levitated droplets in a controllable, and quantifiable manner, thus offering a means to measure the surface tension of a liquid droplet based on its deviation from sphericity. However, for new generation of multi-source and highly stable acoustic levitators, no model relates the acoustic pressure field to the deformation and surface tension. Utilizing a machine learning algorithm is expected to identify correlations between the experimental data without any set preconditions.
Experiments
A series of aqueous surfactant solutions with a large range of surface tensions were prepared, and evaporated under levitation, while the acoustic pressure was varied. A dataset of over 50,000 images was used for the training and evaluation of the machine learning algorithm. Prior to that, the machine learning approach was validated on in silico data that also included artificial noise.
Findings
We achieved high accuracy in predicting the surface tension of single standing droplets (±0.88 mN/m), and we surpassed certain physical conditions related to the size, and shape of the suspended samples that simpler theoretical models are subject to.

Contact-free

Surface tension

Droplet

Machine learning

Levitating

Författare

Smaragda Maria Argyri

Chalmers, Kemi och kemiteknik, Tillämpad kemi

Lars Evenäs

Chalmers, Kemi och kemiteknik, Tillämpad kemi

Romain Bordes

Chalmers, Kemi och kemiteknik, Tillämpad kemi

Journal of Colloid and Interface Science

0021-9797 (ISSN) 1095-7103 (eISSN)

Vol. 640 637-646

Ämneskategorier

Fysikalisk kemi

DOI

10.1016/j.jcis.2023.02.077

PubMed

36889061

Mer information

Senast uppdaterat

2023-04-13