Developing machine learning algorithms to predict the dissolution of zinc oxide nanoparticles in aqueous environment
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| dc.contributor.author | Yalezo, Ntsikelelo
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| dc.contributor.author | Musee, Ndeke
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| dc.contributor.author | Daramola, Michael Olawale
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| dc.date.accessioned | 2025-02-10T08:21:55Z | |
| dc.date.issued | 2024-12 | |
| dc.description | DATA AVAILABILITY : Data will be made available on request. | en_US |
| dc.description.abstract | Please read abstract in the article. | en_US |
| dc.description.department | Chemical Engineering | en_US |
| dc.description.embargo | 2025-09-05 | |
| dc.description.librarian | hj2024 | en_US |
| dc.description.sdg | SDG-09: Industry, innovation and infrastructure | en_US |
| dc.description.sponsorship | The University of Pretoria, the Water Research Commission and the National Research Foundation, South Africa. | en_US |
| dc.description.uri | https://www.elsevier.com/locate/enmm | en_US |
| dc.identifier.citation | Yalezo, N., Musee, N. & Daramola, M.O. 2024, 'Developing machine learning algorithms to predict the dissolution of zinc oxide nanoparticles in aqueous environment', Environmental Nanotechnology, Monitoring and Management, vol. 22, art. 101000, pp. 1-13, doi : 10.1016/j.enmm.2024.101000. | en_US |
| dc.identifier.issn | 2215-1532 | |
| dc.identifier.other | 10.1016/j.enmm.2024.101000 | |
| dc.identifier.other | 10.1016/j.enmm.2024.101000 | |
| dc.identifier.uri | http://hdl.handle.net/2263/100635 | |
| dc.language.iso | en | en_US |
| dc.publisher | Elsevier | en_US |
| dc.rights | © 2024 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies. Notice : this is the author’s version of a work that was accepted for publication in Environmental Nanotechnology Monitoring and Management. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. A definitive version was subsequently published in Environmental Nanotechnology Monitoring and Management, vol. 22, art. 101000, pp. 1-13, doi : 10.1016/j.enmm.2024.101000. | en_US |
| dc.subject | Machine learning | en_US |
| dc.subject | nZnO dissolution | en_US |
| dc.subject | Surface transformation | en_US |
| dc.subject | Aqueous environment | en_US |
| dc.subject | Meta-analysis | en_US |
| dc.subject | Zinc oxide nanoparticles (nZnO) | en_US |
| dc.subject | Engineered nanoparticle (ENP) | en_US |
| dc.subject | SDG-09: Industry, innovation and infrastructure | en_US |
| dc.title | Developing machine learning algorithms to predict the dissolution of zinc oxide nanoparticles in aqueous environment | en_US |
| dc.type | Postprint Article | en_US |
