Calibration-free reaction yield quantification by HPLC with a machine-learning model of extinction coefficients.

ABSTRACT: Reaction optimization and characterization depend on reliable measures of reaction yield, often measured by high-performance liquid chromatography (HPLC). Peak areas in HPLC chromatograms are correlated to analyte concentrations by way of calibration standards, typically pure samples of known concentration. Preparing the pure material required for calibration runs can be tedious for low-yielding reactions and technically challenging at small reaction scales. Herein, we present a method to quantify the yield of reactions by HPLC without needing to isolate the product(s) by combining a machine learning model for molar extinction coefficient estimation, and both UV-vis absorption and mass spectra. We demonstrate the method for a variety of reactions important in medicinal and process chemistr

SUBMITTER: McDonald MA 

PROVIDER: S-EPMC11220585 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature