A machine learning approach for the estimation of photocatalytic activity of ALD ZnO thin films on fabric substrates

Simple item page
buir.contributor.authorAkyıldız, Halil İbrahim
buir.contributor.orcidAkyıldız, Halil İbrahim|0000-0002-8727-5829
dc.citation.epage115308-7en_US
dc.citation.spage115308-1
dc.citation.volumeNumber448
dc.contributor.authorAkyıldız, Halil I.
dc.contributor.authorYiğit, E.
dc.contributor.authorArat, A. B.
dc.contributor.authorIslam, S.
dc.date.accessioned2024-03-22T16:54:46Z
dc.date.available2024-03-22T16:54:46Z
dc.date.issued2024-02-01
dc.departmentInstitute of Materials Science and Nanotechnology (UNAM)
dc.description.abstractResearch in the field of photocatalytic wastewater treatment is striving to enhance catalyst materials to achieve high-performance systems. A promising approach to this goal has been immobilizing photocatalytic materials on fibrous substrates via atomic layer deposition (ALD). Nevertheless, both the ALD process and the assessment of photocatalytic performance involve a multitude of parameters necessitating thorough investigation. In this study, we employ popular machine-learning algorithms, including Support Vector Regression (SVR) and Artificial Neural Networks (ANN), to predict the photocatalytic activity of ALD-coated textiles. The photocatalytic activity is evaluated through methylene blue and methyl orange degradation tests. Machine learning algorithms are tested and trained using the k-fold cross-validation technique. The findings demonstrate that the ANN and SVR methods utilized in this research can predict catalytic activity with mean absolute percentage errors (MAPE) of 2.35 and 3.25, respectively. This study illuminates that, within the defined range of process parameters, the photocatalytic activity of ALD-coated textiles can be precisely estimated with suitable machine-learning algorithms.
dc.embargo.release2026-02-01
dc.identifier.doi10.1016/j.jphotochem.2023.115308
dc.identifier.eissn1873-2666
dc.identifier.issn1010-6030
dc.identifier.urihttps://hdl.handle.net/11693/115100
dc.language.isoEnglish
dc.publisherElsevier
dc.relation.isversionofhttps://doi.org/10.1016/j.jphotochem.2023.115308
dc.rightsCC BY 4.0 DEED (Attribution 4.0 International)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.source.titleJournal of Photochemistry and Photobiology A: Chemistry
dc.subjectMachine learning
dc.subjectAtomic layer deposition
dc.subjectPhotocatalysis
dc.subjectZnO
dc.subjectFibers
dc.titleA machine learning approach for the estimation of photocatalytic activity of ALD ZnO thin films on fabric substrates
dc.typeArticle

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
A_machine_learning_approach_for_the_estimation_of_photocatalytic_activity_of_ALD_ZnO_thin_films_on_fabric_substrates.pdf
Size:
3.13 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
2.01 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Institute of Materials Science and Nanotechnology (UNAM)