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Extension of a prediction model to estimate vapor pressures of perfluorinated compounds (PFCs)
Linnaeus University, Faculty of Science and Engineering, School of Natural Sciences.ORCID iD: 0000-0001-9382-9296
Linnaeus University, Faculty of Science and Engineering, School of Natural Sciences.
2011 (English)In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 107, no 1, 59-64 p.Article in journal (Refereed) Published
Abstract [en]

Perfluorinated compounds (PFCs) are persistent and have been found globally as environmental contaminants. Release into the environment can occur from manufacturing, industrial and consumer uses. The vapor pressure is an important physical property influencing both the release and the environmental partitioning, but few reliable experimental determinations are available. Here we update a previous PLS regression model to cover also this compound class, using only a few calibration compounds. The recalibration is accomplished by applying a leverage-based weighting scheme that is generally applicable in updating structure–property relationships. The predictive performance is validated with an external validation set and is considerably better than for other standard estimation software, both with regard to accuracy and precision. The model can be given a chemical interpretation and the prediction error for the liquid vapor pressure is within 0.2 log units of Pa. Finally, the model is applied and vapor pressure estimates are reported for more than 200 PFCs where no reliable experimental data are available.

Place, publisher, year, edition, pages
2011. Vol. 107, no 1, 59-64 p.
National Category
Natural Sciences
Research subject
Natural Science, Environmental Science
Identifiers
URN: urn:nbn:se:lnu:diva-10952DOI: 10.1016/j.chemolab.2011.01.009OAI: oai:DiVA.org:lnu-10952DiVA: diva2:400598
Projects
Chemoinformatics for green chemistry
Available from: 2011-02-26 Created: 2011-02-26 Last updated: 2016-11-15Bibliographically approved
In thesis
1. Chemoinformetics for green chemistry
Open this publication in new window or tab >>Chemoinformetics for green chemistry
2010 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis focuses on the development of quantitative structure-activity relationship (QSPR) models for physicochemical properties, e.g., vapor pressure and partitioning coefficients. Such models can be used to estimate environmental distribution and transformation of the pollutants or to characterize solvents properties. Here, chemoinformatics was used as an efficient tool for modeling to produce safe chemicals based on green chemistry principles.

Experimental determinations are only available for a limited number of the chemicals; however, theoretical molecular descriptors can be used for modeling of all organic compounds. In this thesis, we developed and validated a global and local QSPR model for vapor pressure of liquid and subcooled liquid organic compounds, in which perfluorinated compounds (PFCs) as outliers appeared in the model due to their molecular properties. Subsequently, after the update of the previous model, the vapor pressure of perfluorinated compounds (PFCs) for which no reliable experimental data are available was successfully predicted. At the same time, we used partitioning between n-octanol/water (Kow) and water solubility (Sw) to investigate the similarities and differences between linear solvation energy relationship (LSER) and partial least square projection to latent structures (PLS) models. Further, we developed QSPR model for prediction of melting points and boiling points of PFCs using multiple linear regression (MLR), PLS and associative neural networks (ASNN) approaches, meanwhile, the applicability domain of PFCs was also investigated.

Experimental, semi-empirical and theoretical quantitative structure-retention relationship (QSRR) models were used to accurately predict retention factors (logk) in reversed-phase liquid chromatography (RPLC). These models are useful to characterize solvents for determination of the behavior and interactions of molecular structure and develop chromatographic methods. In both of QSPR and QSRR models using the PLS method, the first and second components captured main information which is related to van der Waals forces and polar interactions, and their results coincide with those from LSER.

The results showed that the models of physicochemical properties and retention factors (logk) in chromatographic system can be successfully developed by the PLS method. PLS models were able to predict physicochemical properties of organic compounds directly from theoretical descriptors without prior synthesis, measurement or sampling. Further, the PLS method could overcome colinearity in data sets, and it is therefore a rapid, cheap and highly efficient approach

Place, publisher, year, edition, pages
Växjö, Kalmar: Linnaeus University Press, 2010. 164 p.
Series
Linnaeus University Dissertations, 26/2010
Keyword
QSPR, PLS, Chemometrics, Chemoinformatics
National Category
Environmental Sciences
Research subject
Environmental Science, Environmental Chemistry
Identifiers
urn:nbn:se:lnu:diva-8634 (URN)978-91-86491-40-6 (ISBN)
Public defence
2010-10-22, A137, Landgången 4, Kocken, 09:30 (English)
Opponent
Supervisors
Available from: 2010-10-26 Created: 2010-09-24 Last updated: 2013-11-04Bibliographically approved

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Citation style
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