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The Internal Relation between Quantum Chemical Descriptors and Empirical Constants of Polychlorinated Compounds

Quantum chemical descriptors and empirical parameters are two different types of chemical parameters that play the fundamental roles in chemical reactivity and model development. However, previous studies have lacked detail regarding the relationship between quantum chemical descriptors and empirica...

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Detalles Bibliográficos
Autores principales: Fei, Jiangchi, Mao, Qiming, Peng, Lu, Ye, Tiantian, Yang, Yuan, Luo, Shuang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6278375/
https://www.ncbi.nlm.nih.gov/pubmed/30423794
http://dx.doi.org/10.3390/molecules23112935
Descripción
Sumario:Quantum chemical descriptors and empirical parameters are two different types of chemical parameters that play the fundamental roles in chemical reactivity and model development. However, previous studies have lacked detail regarding the relationship between quantum chemical descriptors and empirical constants. We selected polychlorinated biphenyls (PCBs) as an object to investigate the intrinsic correlation between 16 quantum chemical descriptors and Hammett constants. The results exhibited extremely high linearity for [Formula: see text] with Q(xx/yy/zz), α and E(HOMO) based on the meta-position grouping. Polychlorinated dibenzodioxins (PCDDs) and polychlorinated naphthalenes (PCNs) congeners, as two independent compounds, validated the reliability of the relationship. The meta-substituent grouping method between [Formula: see text] and α was successfully used to predict the rate constant (k) for (•)OH oxidation of PCBs, as well as the octanol/water partition coefficient (logK(OW)) and aqueous solubility (−logS(W)) of PCDDs, and exhibited excellent agreement with experimental measurements. Revealing the intrinsic correlation underlying the empirical constant and quantum chemical descriptors can develop simpler and higher efficient model application in predicting the environmental behavior and chemical properties of compounds.