Estimating flavonoid oxidation potentials: mechanisms and charge-related regression models

Area of Science:

• Computational Chemistry
• Electrochemistry
• Organic Chemistry

Background:

• Flavonoids are important natural compounds with diverse biological activities.
• Understanding their oxidation potentials is crucial for predicting their behavior and applications.
• Existing models require validation on larger, diverse datasets.

Purpose of the Study:

• To test and refine quadratic regression models for estimating flavonoid oxidation potentials.
• To expand the calibration set of flavonoids to 35 compounds.
• To identify the most effective molecular descriptors for predicting oxidation potentials.

Main Methods:

• Utilized quadratic regression analysis on a dataset of 35 flavonoids.
• Employed spin populations and differences in net atomic charges as molecular descriptors.
• Included six new flavonoids to enhance the calibration set.
• Measured oxidation potentials under consistent experimental conditions.

Main Results:

• The best performing model achieved a high coefficient of determination (R² = 0.970) and low standard error (S.E. = 0.043).
• Differences in net atomic charges proved to be the most effective predictors of oxidation potentials.
• The model's performance indicates strong correlations with single electron transfer-proton transfer (SET-PT), sequential proton loss electron transfer (SPLET), and hydrogen atom transfer (HAT) mechanisms.

Conclusions:

• Quadratic regression models based on net atomic charge differences provide a highly accurate method for estimating flavonoid oxidation potentials.
• The findings support the utility of these models across various electrochemical oxidation mechanisms.
• This work provides a robust computational tool for flavonoid research.