Oxidation potentials of phenols and anilines: correlation analysis of electrochemical and theoretical values†
Phenols and anilines have been studied extensively as reductants of environmental oxidants (such as manganese dioxide) and as reductates (e.g., model contaminants) that are transformed by environmental oxidants (ozone, triple organic matter, etc.). The thermodynamics and kinetics of these reactions have been interpreted using oxidation potentials for substituted phenols and anilines, often using a legacy experimental dataset that is of uncertain quality. Although there are many alternative oxidation potential data, there has been little systematic analysis of the relevance, reliability, and consistency of the data obtained by different methods. We have done this through an extensive correlation analysis of kinetic data for phenol or aniline oxidation by manganese oxide—compiled from multiple sources—and oxidation potentials obtained from (i) electrochemical measurements using cyclic and square wave voltammetry and (ii) theoretical calculations using density functional theory. Measured peak potentials (Ep) from different sources and experimental conditions correlate very strongly, with minimal root mean squared error (RMSE), slopes ≈ 1, and intercepts indicative of consistent absolute differences of 50–150 mV; whereas, one-electron oxidation potentials (E1) from different sources and theoretical conditions exhibit large RMSE, slopes, and intercepts vs. measured oxidation potentials. Calibration of calculated E1 data vs. measured Ep data gave corrected values of E1 with improved accuracy. For oxidation by manganese dioxide, normalization of rate constants (to the 4-chloro congener) allowed correlation of phenol and aniline data from multiple sources to give one, unified quantitative structure–activity relationship (QSAR). Comparison among these QSARs illustrates the principle of matching the observational vs. mechanistic character of the response and descriptor variables.
- This article is part of the themed collection: QSARs and computational chemistry methods in environmental chemical sciences