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This study introduces an automated R language workflow to model interconverting enantiomers in chiral chromatography. The method extracts kinetic and thermodynamic data directly from chromatographic profiles, enhancing chiral analysis.

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Area of Science:

  • Analytical Chemistry
  • Chromatography
  • Chemical Kinetics

Background:

  • On-column enantiomerization poses challenges for quantitative chiral analysis.
  • Dynamic chromatographic profiles (Batman peaks) offer opportunities for mechanistic insights.
  • Understanding interconverting enantiomers is crucial for chiral separations.

Purpose of the Study:

  • To develop an automated workflow for modeling dynamic chromatographic profiles of interconverting enantiomers.
  • To extract kinetic and thermodynamic information directly from liquid chromatography data.
  • To distinguish nonselective and enantioselective interactions on chiral stationary phases.

Main Methods:

  • Utilized an extended automated workflow in R language for statistical computing.
  • Applied a two-site stochastic formulation to model interconverting enantiomers.
  • Employed competitive bi-Langmuir isotherm for overload experiments and empirical Bayesian inference for rate constant decomposition.

Main Results:

  • Successfully obtained forward and reverse interconversion rate constants across various conditions.
  • Decomposed apparent rates into mobile and stationary phase contributions using a mixed-effect model.
  • Demonstrated consistent Eyring-Polányi behavior and agreement with off-column kinetics.

Conclusions:

  • Detailed kinetic and thermodynamic characterization of enantiomerization is achievable directly from routine liquid chromatography.
  • The automated workflow enhances the analytical utility of dynamic chiral separations.
  • The platform is broadly applicable to systems exhibiting on-column interconversion.