Design and applications of a neural networks assisted portable liquid surface tensiometer

Autor:	Tomas Drevinskas, Mantas Stankevičius, Domantas Armonavičius, Rūta Mickienė, Massimo Zacchini, Gediminas Dūda, Ona Ragažinskienė, Saulius Grigiškis, Jūratė Balevičiūtė, Kristina Bimbiraitė-Survilienė, Enrica Donati, Vilma Kaškonienė, Nicola Tiso, Donatas Levišauskas, Audrius Maruška
Rok vydání:	2021
Předmět:	Surface (mathematics) Materials science Artificial neural network Tensiometer (surface tension) Mechanical engineering General Chemistry
Zdroj:	Chemija. 32
ISSN:	2424-4538 0235-7216
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::5f6967639dd18cba9f6a4727dfc90567 https://doi.org/10.6001/chemija.v32i3-4.4545 Zobrazit plný text záznamu