Carbamazepine Adsorption onto Giant Macroporous Silica and Adaptive Neuro-Fuzzy Inference System Modeling
| dc.authorid | 0000-0003-2734-8544 | |
| dc.authorid | 0000-0001-9760-9856 | |
| dc.authorid | 0000-0001-9943-771X | |
| dc.authorid | 0000-0002-1628-5026 | |
| dc.authorid | 0000-0001-6954-2703 | |
| dc.authorid | 0000-0002-8440-2546 | |
| dc.contributor.author | Alver, Alper | |
| dc.contributor.author | Yılmaz, Bahar Akyüz | |
| dc.contributor.author | Bilican, Behlül Koç | |
| dc.contributor.author | Baştürk, Emine | |
| dc.contributor.author | Kaya, Murat | |
| dc.contributor.author | Işık, Mustafa | |
| dc.date.accessioned | 2024-07-02T12:43:34Z | |
| dc.date.available | 2024-07-02T12:43:34Z | |
| dc.date.issued | 2024 | |
| dc.department | Teknik Bilimler Meslek Yüksekokulu | |
| dc.description.abstract | There is an imperative need to eliminate pharmaceutical residues from aquatic environments due to their hazardous properties, including toxicity, mutagenicity, and carcinogenicity, particularly when present in water sources. Conventional water treatment methods have proven insufficient in addressing nano-pollutants such as pharmaceutical residues. Consequently, the ongoing quest for economically viable, sustainable, and environmentally friendly removal mechanisms persists. In this particular study, we employed Giant Macroporous Silica (GMS) derived from marine sponges as a promising biosorbent. GMS exhibits commendable characteristics, including a high specific surface area, swift mass transfer capabilities, and non-discriminatory adsorption qualities. The efficacy of GMS in adsorbing carbamazepine (CBZ), a common drug residue, was scrutinized under diverse experimental conditions, including a sorbate/sorbent ratio ranging from 0.005 to 1.500 weight ratio, contact times spanning from 0 to 240 min, and initial pH values ranging from 5 to 9. Remarkably, at a concentration of 1000 µg L?1, GMS demonstrated an attractive adsorption rate (98.88%) of carbamazepine at pH 7.07, within 90 min. To enhance our understanding, we developed an ANFIS model utilizing the experimental parameters as inputs. The developed model exhibited a high correlation coefficient of 0.9944% and a root mean square error (RMSE) of 1.6693, indicating its dependability in accurately predicting the adsorption of CBZ on GMS. The results of our study highlight the efficacy of GMS in adsorbing CBZ, suggesting its considerable potential for adsorbing other pharmaceutical residues and nano-pollutants. | |
| dc.identifier.doi | 10.1007/s13369-024-09032-3 | |
| dc.identifier.endpage | 8524 | en_US |
| dc.identifier.issn | 2193-567X | |
| dc.identifier.issue | 6 | en_US |
| dc.identifier.scopusquality | Q1 | |
| dc.identifier.startpage | 8509 | en_US |
| dc.identifier.uri | https:/dx.doi.org/10.1007/s13369-024-09032-3 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12451/12021 | |
| dc.identifier.volume | 49 | en_US |
| dc.identifier.wosquality | N/A | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | en | |
| dc.publisher | Springer Nature | |
| dc.relation.ispartof | Arabian Journal for Science and Engineering | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.subject | Adaptive Neuro-Fuzzy Inference System | |
| dc.subject | Adsorption | |
| dc.subject | Carbamazepine | |
| dc.subject | Giant Macroporous Silica | |
| dc.subject | Modeling | |
| dc.title | Carbamazepine Adsorption onto Giant Macroporous Silica and Adaptive Neuro-Fuzzy Inference System Modeling | |
| dc.type | Article |
