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izvor podataka: crosbi

Prediction of key structural features responsible for aromaticity of single-benzene ring pollutants and their photooxidative intermediates (CROSBI ID 218242)

Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija

Juretić, Daria ; Kušić, Hrvoje ; Dionysiou, Dionysios D. ; Rasulev, Bakhtiyor ; Peternel, Igor ; Lončarić Bozić, Ana Prediction of key structural features responsible for aromaticity of single-benzene ring pollutants and their photooxidative intermediates // Chemical engineering journal, 276 (2015), 261-273. doi: 10.1016/j.cej.2015.04.082

Podaci o odgovornosti

Juretić, Daria ; Kušić, Hrvoje ; Dionysiou, Dionysios D. ; Rasulev, Bakhtiyor ; Peternel, Igor ; Lončarić Bozić, Ana

engleski

Prediction of key structural features responsible for aromaticity of single-benzene ring pollutants and their photooxidative intermediates

The study was aimed at the prediction of structural factors influencing the aromaticity of typical single-benzene ring water pollutants, as well as their photooxidative intermediates, using structure-activity relationship modeling. UV absorbance (A) at 254 nm and 280 nm, along with the total organic carbon (TOC) values, were determined for 30 compounds structured of single benzene ring possessing different types, numbers, and positions of substitutes. These single-benzene ring pollutants were afterwards submitted to photooxidative treatment by UV-C/H2O2 process, where A and TOC values in reference treatment periods (RTPs) were determined in order to estimate the aromaticity of formed intermediates. The aromaticity of both parent pollutants and their photooxidative intermediates formed in RTPs were expressed by means of specific UV absorbance (SUVA) values. Such calculated SUVA values were taken as response values in QSPR modeling in order to establish key structural features of studied single-benzene ring pollutants contributing to the aromaticity changes during applied UV-C/H2O2 process. Within QSPR modeling, a genetic algorithm and multiple linear regression analysis were applied for the selection of descriptors representing structural characteristics of the studied compounds, and to generate the correlation models. The 3- and 4-variable models were found to be most predictive for SUVA254 and SUVA280 values, clearly reflecting contributing structural characteristics of the studied compounds toward aromaticity and corresponding changes occurring during their photooxidative treatment.

wastewater ; organic pollutants ; photooxidation ; aromaticity ; structure-activity relationship

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Podaci o izdanju

276

2015.

261-273

objavljeno

1385-8947

1873-3212

10.1016/j.cej.2015.04.082

Povezanost rada

Interdisciplinarne tehničke znanosti, Kemijsko inženjerstvo

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