Open AccessDesign and applications of a neural networks assisted portable liquid surface tensiometer
Author(s) -
Tomas Drevinskas,
Jūratė Balevičiūtė,
Kristina Bimbiraitė-Survilienė,
Gediminas Dūda,
Mantas Stankevičius,
Nicola Tiso,
Rūta Mickienė,
Domantas Armonavičius,
Donatas Levišauskas,
Vilma Kaškonienė,
Ona Ragažinskienė,
Saulius Grigiškis,
Enrica Donati,
Massimo Zacchini,
Audrius Maruška
Publication year - 2021
Publication title -
chemija
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.16
H-Index - 15
eISSN - 2424-4538
pISSN - 0235-7216
DOI - 10.6001/chemija.v32i3-4.4545
Subject(s) - tensiometer (surface tension) , calibration , logarithm , instrumentation (computer programming) , polynomial , artificial neural network , pulmonary surfactant , surface tension , biological system , polynomial and rational function modeling , materials science , calibration curve , computer science , mathematics , artificial intelligence , physics , mathematical analysis , statistics , thermodynamics , detection limit , biology , operating system
In this paper, a portable instrument for surface tension measurements, characterization and applications is described. The instrumentation is operated wirelessly, and samples can be measured in situ. The instrument has changeable different size probes; therefore, it is possible to measure samples from 1 ml up to 10 ml. The response of the measured retraction force and the concentrations of measured surfactant is complex. Therefore, two calibration methods were proposed: (i) the conditional calibration using polynomial and logarithmic fitting and (ii) the neural network trained model prediction of the surfactant concentration in samples. Calibrating the instrument, the neural network trained model showed a superior coefficient of determination (0.999), comparing it to the conditional calibration using polynomial (0.992) and logarithmic (0.991) fit equations.