An Automated Tool for Predicting Ion-Molecule Reaction Rates in CI-MS: Application to PCDDs and PCDFs

Polychlorinated dibenzo-p-dioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs) are persistent environmental pollutants that bioaccumulate through the food chain, posing serious health risks to humans and animals. Dioxins and Furans are persistent, ultra-trace semi-volatile organic compounds (SVOCs) characterized by exceptionally low vapor pressures. Their quantification using chemical ionization mass spectrometry (CI-MS) and gas chromatography mass spectrometry (GC-MS) critically depends on their intrinsic physicochemical properties. In this work, density functional theory (DFT) calculations were employed to determine key molecular parameters of analytical relevance, including proton affinity, ionization energy, dipole moment, and polarizability of the 20 most important PCDDs and PCDFs. Furthermore, rate coefficients for ion-molecule reactions with the reagent ions H₃O ${}^{+}$ and NO ${}^{+}$ were evaluated via classical collision models. The thermodynamic feasibility of proton- and charge-transfer pathways was systematically examined under elevated temperature and energetic conditions typical of CI-MS. In particular, the enthalpy of reaction with NO ${}^{+}$ ions was computed to confirm the viability of charge transfer mechanisms. Additionally, we present a Python-based automated utility and database of over 200 VOCs with DFT-derived descriptors for rapid estimation of reaction rate coefficients, ion kinetic energies, effective temperatures, and center-of-mass energies in CI-MS processes. (Link: https://github.com/Manjeetkb/rate-cal).