Molecularly Imprinted Polymers for Selective Extraction and Determination of Toxic Herbicide Bentazon in Water Samples Using Liquid Chromatography and Assessment of Mean Square Error Using Artficial Neural Network Model
Publication type: Journal Article
Publication date: 2023-05-30
scimago Q3
wos Q4
SJR: 0.252
CiteScore: 1.9
Impact factor: 1.1
ISSN: 10619348, 16083199
Analytical Chemistry
Abstract
In this article, a molecularly imprinted polymer-solid phase extraction (SPE)-liquid chromatography method was developed to isolate toxic bentazon in surface water. The molecularly imprinted polymer (MIP) consisted of methacrylic acid as a functional monomer, ethylene glycol dimethacrylate as a crosslinking monomer, and α,α′-azoisobutyronitrile as an initiator for polymer preparation. To evaluate the applicability of the imprinted polymer as a selective sorbent, general parameters, such as pH, amount of loading solvents, washing solution, eluent, and time, were optimized following a step-by-step approach. Under the optimum conditions, the linear range was between 0.05 and 1.0 µg/L. The standard deviation of 2.2% and the method detection limit of 0.05 µg/L were obtained. The recoveries up to approximately 97.0% from spiked surface water samples could be obtained. The observed outcomes confirmed the suitability of the artificial neural network model as a tool for mean square error of bentazon on MIP-SPE (0.018) and non-imprinted polymer-SPE (0.029) selectivity and permeability. The proposed molecularly imprinted polymer-solid phase extraction-liquid chromatography method could be applied to the direct determination of toxic bentazon in water samples.
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Total citations:
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Citations from 2024:
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Geramizadegan A., Niknam L., Pournamdari E. Molecularly Imprinted Polymers for Selective Extraction and Determination of Toxic Herbicide Bentazon in Water Samples Using Liquid Chromatography and Assessment of Mean Square Error Using Artficial Neural Network Model // Journal of Analytical Chemistry. 2023. Vol. 78. No. 5. pp. 572-581.
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Geramizadegan A., Niknam L., Pournamdari E. Molecularly Imprinted Polymers for Selective Extraction and Determination of Toxic Herbicide Bentazon in Water Samples Using Liquid Chromatography and Assessment of Mean Square Error Using Artficial Neural Network Model // Journal of Analytical Chemistry. 2023. Vol. 78. No. 5. pp. 572-581.
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TY - JOUR
DO - 10.1134/s1061934823050052
UR - https://doi.org/10.1134/s1061934823050052
TI - Molecularly Imprinted Polymers for Selective Extraction and Determination of Toxic Herbicide Bentazon in Water Samples Using Liquid Chromatography and Assessment of Mean Square Error Using Artficial Neural Network Model
T2 - Journal of Analytical Chemistry
AU - Geramizadegan, Alireza
AU - Niknam, Leila
AU - Pournamdari, Elham
PY - 2023
DA - 2023/05/30
PB - Pleiades Publishing
SP - 572-581
IS - 5
VL - 78
SN - 1061-9348
SN - 1608-3199
ER -
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BibTex (up to 50 authors)
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@article{2023_Geramizadegan,
author = {Alireza Geramizadegan and Leila Niknam and Elham Pournamdari},
title = {Molecularly Imprinted Polymers for Selective Extraction and Determination of Toxic Herbicide Bentazon in Water Samples Using Liquid Chromatography and Assessment of Mean Square Error Using Artficial Neural Network Model},
journal = {Journal of Analytical Chemistry},
year = {2023},
volume = {78},
publisher = {Pleiades Publishing},
month = {may},
url = {https://doi.org/10.1134/s1061934823050052},
number = {5},
pages = {572--581},
doi = {10.1134/s1061934823050052}
}
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Geramizadegan, Alireza, et al. “Molecularly Imprinted Polymers for Selective Extraction and Determination of Toxic Herbicide Bentazon in Water Samples Using Liquid Chromatography and Assessment of Mean Square Error Using Artficial Neural Network Model.” Journal of Analytical Chemistry, vol. 78, no. 5, May. 2023, pp. 572-581. https://doi.org/10.1134/s1061934823050052.