Probing Antioxidant-Related Properties for Phenolic Compounds.
Iuri N SoaresKirk A PetersonGabriel L C de SouzaPublished in: The journal of physical chemistry. A (2024)
In this work, properties related to antioxidant-potential mechanisms (such as the bond dissociation enthalpy, BDE, for the homolytic cleavage of the O-H bond and ionization energies, IEs) were determined for phenol, pyrocatechol, and gallic acid (GA). Both the protonated and deprotonated forms of GA were investigated. The Feller-Peterson-Dixon (FPD) composite method was employed with a variety of computational approaches, i.e., density functional theory, Möller-Plesset perturbation theory, and coupled-cluster-based methods, in combination with large correlation consistent basis sets with extrapolation to the complete basis set limit and consideration of core electron correlation effects. FPD results were compared to experimental and computational data available in the literature, presenting good agreement. For example, the FPD BDE (298 K) obtained for phenol, which was based on valence-correlated MP2/CBS calculations with contributions from correlating all electrons, was determined to be 87.56 kcal/mol, a value that is 0.42 kcal/mol lower than the result obtained in the most recent experiments, 87.98 ± 0.62. Calibration against coupled-cluster calculations was also carried out for phenol. We expect that the outcomes gathered here may help in establishing a general protocol for computational chemists that are interested in determining antioxidant-related properties for phenolic compounds with considerable accuracy as well as to motivate future IE measurements (particularly for GA) to be accomplished in the near future.
Keyphrases
- density functional theory
- pet ct
- molecular dynamics
- oxidative stress
- anti inflammatory
- systematic review
- molecular dynamics simulations
- randomized controlled trial
- current status
- risk assessment
- climate change
- human health
- type diabetes
- mass spectrometry
- skeletal muscle
- weight loss
- adipose tissue
- low cost
- simultaneous determination
- deep learning
- electron microscopy