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Related Experiment Video

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Hyperpolarized 13C Metabolic Magnetic Resonance Spectroscopy and Imaging

Hyperpolarized ¹³C Metabolic Magnetic Resonance Spectroscopy and Imaging

Published on: December 30, 2016

Predicting organic hydrogen atom transfer rate constants using the Marcus cross relation.

Jeffrey J Warren¹, James M Mayer

¹Department of Chemistry, University of Washington, Box 351700, Seattle, WA 98107-1700, USA. jjw82@u.washington.edu

Proceedings of the National Academy of Sciences of the United States of America

|March 11, 2010

Summary

This study presents a new model to predict hydrogen atom transfer (HAT) reaction rates for oxyl radicals. The CR/KSE model accurately calculates rate constants across diverse chemical and biological systems.

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Area of Science:

Physical Chemistry
Chemical Kinetics
Biophysical Chemistry

Background:

Hydrogen atom transfer (HAT) reactions are fundamental in chemistry and biology.
Predicting HAT reaction rates is crucial for understanding processes like antioxidant activity and catalysis.

Purpose of the Study:

To develop and validate a predictive model for hydrogen atom transfer (HAT) reaction rate constants of oxyl radicals.
To incorporate solvent effects on kinetics and thermodynamics for improved accuracy.

Main Methods:

Utilized the Marcus cross relation (CR) combined with models for kinetic (Ingold's) and thermodynamic (Abraham's) solvent effects.
Developed the combined CR/KSE model.
Experimentally validated the model using specific oxyl radicals and hydroxylamine in various solvents.

Main Results:

The CR/KSE model demonstrated excellent agreement between calculated and experimental cross rate constants.
The model accurately predicted rate constants for over 30 HAT reactions involving oxyl radicals.
Predictions were within a factor of 5 for nearly all surveyed HAT reactions.

Conclusions:

The CR/KSE model offers a robust and remarkably predictive tool for HAT reactions of oxyl radicals.
Accurate prediction of HAT kinetics is achievable by accounting for solvent effects.
This model has broad applicability in chemistry and biology, including antioxidant and catalytic processes.