Targeted boron removal from highly-saline and boron-spiked seawater using magnetic nanobeads: Chemometric optimisation and modelling studies
| dc.contributor.author | Oladipo, Akeem Adeyemi | |
| dc.contributor.author | Gazi, Mustafa | |
| dc.date.accessioned | 2026-02-06T18:37:23Z | |
| dc.date.issued | 2017 | |
| dc.department | Doğu Akdeniz Üniversitesi | |
| dc.description.abstract | Chitosan-based magnetic nanobeads (CBN) were facilely synthesised and applied for selective recovery of boron from highly-saline wastewater. The CBN was characterised, and the results showed that the nanobeads are thermally and mechanically stable to withstand deterioration, had a saturation magnetisation of 65.25 emu/g, the surface area of 635 m(2)/g and nanobeads size of similar to 2.6-3.8 nm. An artificial neural network (ANN) model and Box-Behnken design (BBD) were developed and applied to study the relative effect and significance of input variables affecting boron sorption. Under optimised conditions, 89, 78 and 70% boron removal efficiencies were recorded at pH 7.0 in distilled water, highly-saline water and boron-spiked seawater respectively. The combination of intraparticle diffusion model and pseudo-second-order rate equation was applicable to describe the sorption kinetics. (C) 2017 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved. | |
| dc.description.sponsorship | Scientific and Technical Research Council of Turkey (TUBITAK 1001 Project) [114Z461] | |
| dc.description.sponsorship | This work is fully funded by the Scientific and Technical Research Council of Turkey (TUBITAK 1001 Project no: 114Z461). | |
| dc.identifier.doi | 10.1016/j.cherd.2017.03.024 | |
| dc.identifier.endpage | 338 | |
| dc.identifier.issn | 0263-8762 | |
| dc.identifier.issn | 1744-3563 | |
| dc.identifier.orcid | 0000-0001-7736-752X | |
| dc.identifier.orcid | 0000-0003-3715-5922 | |
| dc.identifier.scopus | 2-s2.0-85017298217 | |
| dc.identifier.scopusquality | Q2 | |
| dc.identifier.startpage | 329 | |
| dc.identifier.uri | https://doi.org/10.1016/j.cherd.2017.03.024 | |
| dc.identifier.uri | https://hdl.handle.net/11129/12459 | |
| dc.identifier.volume | 121 | |
| dc.identifier.wos | WOS:000401201100028 | |
| dc.identifier.wosquality | Q2 | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | en | |
| dc.publisher | Inst Chemical Engineers | |
| dc.relation.ispartof | Chemical Engineering Research & Design | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.snmz | KA_WoS_20260204 | |
| dc.subject | Boron removal | |
| dc.subject | Chitosan nanobeads | |
| dc.subject | Artificial neural network modelling | |
| dc.subject | Chemometric kinetics | |
| dc.title | Targeted boron removal from highly-saline and boron-spiked seawater using magnetic nanobeads: Chemometric optimisation and modelling studies | |
| dc.type | Article |










