dc.contributor.author |
Zaytsev, Valeriy |
|
dc.contributor.author |
Fedorov, Fedor S. |
|
dc.contributor.author |
Goikhman, Boris |
|
dc.contributor.author |
Maslennikov, Alexander |
|
dc.contributor.author |
Mashukov, Vasilii |
|
dc.contributor.author |
Simonenko, Nikolay P. |
|
dc.contributor.author |
Simonenko, Tatiana L. |
|
dc.contributor.author |
Gabdullina, Dinara |
|
dc.contributor.author |
Kovalenko, Olga |
|
dc.contributor.author |
Simonenko, Elizaveta P. |
|
dc.contributor.author |
Kvitko, Polina |
|
dc.contributor.author |
Penkova, Olga |
|
dc.date.accessioned |
2024-09-19T07:27:36Z |
|
dc.date.available |
2024-09-19T07:27:36Z |
|
dc.date.issued |
2023-09-15 |
|
dc.identifier.issn |
0959-6526 |
|
dc.identifier.uri |
https://doi.org/10.1016/j.jclepro.2023.138042 |
|
dc.identifier.uri |
http://rep.enu.kz/handle/enu/16650 |
|
dc.description.abstract |
Plastic recycling technologies are being actively developed and implemented to cope with increasing volume of plastic. Such technologies require new analytical tools able to control the quality of the recycled polymers to be further integrated in production processes. Here, we propose a rapid and selective quality assessment method for polymer materials made of high-density polyethylene using electronic nose with aluminum doped zinc oxide sensing material in combination with the RandomForestClassifier machine learning tool. We test total content of volatile organic compounds both odor-active responsible for the smell and odorless of primary and secondary plastics, and evaluate corresponding organic vapors emitted by the plastics by headspace gas chromatography and mass-spectrometry at optimized conditions like sample temperature, sensor signal recovery time. The electronic nose demonstrated the good correlation of vector signal with the emitted volatile compounds with an accuracy more than 98.5% when discriminating between primary and secondary plastics. Addition of zeolites to the recycled plastic is shown to decrease the appearance of off-odors. |
ru |
dc.description.sponsorship |
The authors thank Dr. Vladislav Kondrashov and Mr. Andrei Starkov
for their contribution to making a gas mixing system and express a deep
gratitude to Mrs. Irina Belikova and Mrs. Evgenia F. Guschina for their
valuable comments. This research was supported by the grant of the
Russian Science Foundation N◦ 21-73-10288, https://rscf.ru/en/pr
oject/21-73-10288 in the part of material synthesis, characterization,
sensing performance evaluation, and machine learning. D.S. and Sh.S.
thank Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (grant number N◦ AP14872171) for
support of this research in part of regression model description. The
authors thank the Council on grants of the Russian Federation (grant
number НШ-1330.2022.1.3). |
ru |
dc.language.iso |
en |
ru |
dc.publisher |
Journal of Cleaner Production |
ru |
dc.relation.ispartofseries |
Volume 418;Article number 138042 |
|
dc.subject |
Al-doped zinc oxide |
ru |
dc.subject |
Electronic nose |
ru |
dc.subject |
Machine learning protocols |
ru |
dc.subject |
Plastics |
ru |
dc.subject |
Polymer odors assessment |
ru |
dc.title |
Rapid and accurate quality assessment method of recycled food plastics VOCs by electronic nose based on Al-doped zinc oxide |
ru |
dc.type |
Article |
ru |