Neural Network Modelling for Prediction of Zeta Potential

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17310%2F21%3AA2202BOF" target="_blank" >RIV/61988987:17310/21:A2202BOF - isvavai.cz</a>

Výsledek na webu

<a href="https://www.mdpi.com/2227-7390/9/23/3089" target="_blank" >https://www.mdpi.com/2227-7390/9/23/3089</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/math9233089" target="_blank" >10.3390/math9233089</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Neural Network Modelling for Prediction of Zeta Potential

Popis výsledku v původním jazyce

The study is focused on monitoring the influence of selected parameters on the zeta potential values of titanium dioxide nanoparticles. The influence of pH, temperature, ionic strength, and mass content of titanium dioxide in the suspension was assessed. More than a thousand samples were measured by combining these variables. On the basis of results, the model of artificial neural network was proposed and tested. The authors have rich experiences with neural networks applications and this case shows that the neural network model works with a very high prediction success rate of zeta potential. Clearly, pH has the greatest effect on zeta potential values. The influence of other variables is not so significant. However, it can be said that increasing temperature results in an increase in the value of the zeta potential of titanium dioxide nanoparticles. The ionic force affects the zeta potential depending on the pH; in the vicinity of the isoelectric point, its effect is negligible. The effect of the mass content of titanium dioxide in the suspension is absolutely minor.

Název v anglickém jazyce

Neural Network Modelling for Prediction of Zeta Potential

Popis výsledku anglicky

The study is focused on monitoring the influence of selected parameters on the zeta potential values of titanium dioxide nanoparticles. The influence of pH, temperature, ionic strength, and mass content of titanium dioxide in the suspension was assessed. More than a thousand samples were measured by combining these variables. On the basis of results, the model of artificial neural network was proposed and tested. The authors have rich experiences with neural networks applications and this case shows that the neural network model works with a very high prediction success rate of zeta potential. Clearly, pH has the greatest effect on zeta potential values. The influence of other variables is not so significant. However, it can be said that increasing temperature results in an increase in the value of the zeta potential of titanium dioxide nanoparticles. The ionic force affects the zeta potential depending on the pH; in the vicinity of the isoelectric point, its effect is negligible. The effect of the mass content of titanium dioxide in the suspension is absolutely minor.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10102 - Applied mathematics

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Mathematics

ISSN

2227-7390

e-ISSN

2227-7390

Svazek periodika

9

Číslo periodika v rámci svazku

23

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

13

Strana od-do

3089-3111

Kód UT WoS článku

000734544200001

EID výsledku v databázi Scopus

2-s2.0-85120413557