Quantitative structure-chromatographic retention relationship for polycyclic aromatic sulfur heterocycles

Area of Science:

• Environmental Chemistry
• Petroleum Geochemistry
• Computational Chemistry

Background:

• Polycyclic aromatic sulfur heterocycles (PASHs) are significant in petroleum geochemistry and environmental studies.
• Accurate prediction of their chromatographic properties is crucial for analysis and understanding.
• Existing methods may lack efficiency or predictive power for diverse PASH structures.

Purpose of the Study:

• To establish a quantitative structure-retention relationship (QSRR) model for 114 PASHs.
• To identify key molecular descriptors that govern the gas-chromatographic retention index (RI) of PASHs.
• To validate the robustness and predictive capability of the developed QSRR model.

Main Methods:

• Performed geometrical optimization and electrostatic potential calculations for 114 PASHs using HF/6-31G*.
• Extracted 25 statistically based molecular parameters.
• Employed stepwise linear regression analysis to establish relationships between RI and structural descriptors, including electrostatic potentials, molecular volume, and HOMO energy.

Main Results:

• Developed a QSRR model using V(s)(+), sigma(+)(2), V(mc), and E(HOMO) to predict PASH retention indices.
• Achieved a high cross-validated correlation coefficient (R(CV) = 0.992), demonstrating excellent predictive accuracy.
• Validated the model's robustness through calibration/test set splitting and prediction of external PASH samples.

Conclusions:

• The established QSRR model provides a powerful and accurate method for predicting the chromatographic properties of PASHs.
• This approach can significantly aid in the analysis and characterization of aromatic organosulfur compounds in complex mixtures.
• The findings offer a valuable tool for environmental and geochemical research involving PASHs.