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Bhhatarai, B.; Gramatica, P. Prediction of Aqueous Solubility, Vapor Pressure and Critical Micelle Concentration for Aquatic Partitioning of Perfluorinated Chemicals. Environ. Sci. Technol. 2011, 45, 8120–8128.

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Bhhatarai, B.; Gramatica, P. Prediction of Aqueous Solubility, Vapor Pressure and Critical Micelle Concentration for Aquatic Partitioning of Perfluorinated Chemicals. Environ. Sci. Technol. 2011, 45, 8120–8128.

QDB archive DOI: 10.15152/QDB.173   DOWNLOAD

QsarDB content

Property logVP: Vapour pressure as logVP [log(mm Hg)]

Compounds: 35 | Models: 1 | Predictions: 3

Eq2: Model for perfluorinated chemicals

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set i training 35 0.909 0.848
Testing set (inside of the AD) i testing 172 N/A N/A
Testing set (outside of the AD) i testing 13 N/A N/A

Property logCMC: Critical micelle concentration as logCMC [log(mol/L)]

Compounds: 10 | Models: 1 | Predictions: 3

Eq3a: Model for perfluorinated chemicals

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set i training 10 0.973 0.159
Testing set (inside of the AD) i testing 159 N/A N/A
Testing set (outside of the AD) i testing 51 N/A N/A

Property logAqS: Aqueous solubility as logAqS [log(mg/L)]

Compounds: 20 | Models: 1 | Predictions: 3

Eq1: Model for perfluorinated chemicals

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set i training 20 0.758 0.901
Testing set (inside of the AD) i testing 173 N/A N/A
Testing set (outside of the AD) i testing 27 N/A N/A

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Title: Bhhatarai, B.; Gramatica, P. Prediction of Aqueous Solubility, Vapor Pressure and Critical Micelle Concentration for Aquatic Partitioning of Perfluorinated Chemicals. Environ. Sci. Technol. 2011, 45, 8120–8128.
Abstract: The majority of perfluorinated chemicals (PFCs) are of increasing risk to biota and environment due to their physicochemical stability, wide transport in the environment and difficulty in biodegradation. It is necessary to identify and prioritize these harmful PFCs and to characterize their physicochemical properties that govern the solubility, distribution and fate of these chemicals in an aquatic ecosystem. Therefore, available experimental data (10−35 compounds) of three important properties: aqueous solubility (AqS), vapor pressure (VP) and critical micelle concentration (CMC) on per- and polyfluorinated compounds were collected for quantitative structure−property relationship (QSPR) modeling. Simple and robust models based on theoretical molecular descriptors were developed and externally validated for predictivity. Model predictions on selected PFCs were compared with available experimental data and other published in silico predictions. The structural applicability domains (AD) of the models were verified on a bigger data set of 221 compounds. The predicted properties of the chemicals that are within the AD, are reliable, and they help to reduce the wide data gap that exists. Moreover, the predictions of AqS, VP, and CMC of most common PFCs were evaluated to understand the aquatic partitioning and to derive a relation with the available experimental data of bioconcentration factor (BCF).
URI: http://hdl.handle.net/10967/173
http://dx.doi.org/10.15152/QDB.173
Date: 2016-02-01


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    Uni. Insubria (Italy), QSAR Research Unit in Environmental Chemistry and Ecotoxicology

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