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Monitoring of cellulose oxidation level by electrokinetic phenomena and numeric prediction model (CROSBI ID 274490)

Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija

Tarbuk, Anita ; Grgić, Katia ; Toshikj, Emilija ; Domović, Daniel ; Dimitrovski, Dejan ; Dimova, Vesna ; Jordanov, Igor Monitoring of cellulose oxidation level by electrokinetic phenomena and numeric prediction model // Cellulose, 27 (2020), 3107-3119. doi: 10.1007/s10570-020-03028-6

Podaci o odgovornosti

Tarbuk, Anita ; Grgić, Katia ; Toshikj, Emilija ; Domović, Daniel ; Dimitrovski, Dejan ; Dimova, Vesna ; Jordanov, Igor

engleski

Monitoring of cellulose oxidation level by electrokinetic phenomena and numeric prediction model

Cellulose with a low level of oxidation is suitable for producing stable long-lasting materials with high added value, while extensively oxidized once is applicable for disposable products. In our previous comprehensive research, the fundamental behavior of the cotton under the action of different oxidants has been explored. Different levels of oxidation, as well as the type of functional groups, have been achieved by properly selected oxidants while controlling their concentration and treatment time. In this research, the electrokinetic ζ-potential of KIO4 and TEMPO- oxidized cotton and the isoelectric point are measured by the streaming potential method, while the surface charge is calculated from the adsorbed cationic surfactant by the back- titration method. The results of electrokinetic phenomena are compared with the amount of created carboxyl groups determined by the calcium acetate method. The machine learning algorithms Waikato Environment for Knowledge Analysis for regression analysis is employed to develop models that make numeric predictions of the ζ-potential values based on the known number of carboxyl groups. The model with the correlation coefficient between the actual and the predicted value of ζ-potential is given for the first time.

Cotton ; oxidation system ; electrokinetic phenomena, machine learning

Department of Textile Engineering, Faculty of Technology and Metallurgy, Ss. Cyril and Methodius University, Skopje, Republic of North Macedonia

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Podaci o izdanju

27

2020.

3107-3119

objavljeno

0969-0239

1572-882X

10.1007/s10570-020-03028-6

Povezanost rada

Tekstilna tehnologija

Poveznice
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