1. Unveiling the electron-induced ionization cross sections and fragmentation mechanisms of 3,4-dihydro-2H-pyran.
- Author
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Wasowicz, Tomasz J., Jurkowski, Michal K., Harris, Allison L., and Ljubić, Ivan
- Subjects
- *
MACHINE learning , *APPLIED sciences , *MASS spectrometry , *RADIATION damage , *CYCLIC ethers - Abstract
The interactions of electrons with molecular systems under various conditions are essential to interdisciplinary research fields extending over the fundamental and applied sciences. In particular, investigating electron-induced ionization and dissociation of molecules may shed light on the radiation damage to living cells, the physicochemical processes in interstellar environments, and reaction mechanisms occurring in combustion or plasma. We have, therefore, studied electron-induced ionization and dissociation of the gas phase 3,4-dihydro-2H-pyran (DHP), a cyclic ether appearing to be a viable moiety for developing efficient clinical pharmacokinetics and revealing the mechanisms of biofuel combustion. The mass spectra in the m/z = 10–90 mass range were measured at several different energies of the ionizing electron beam using mass spectrometry. The mass spectra of DHP at the same energies were simulated using on-the-fly semi-classical molecular dynamics (MD) within the framework of the QCxMS formalism. The MD settings were suitably adjusted until a good agreement with the experimental mass spectra intensities was achieved, thus enabling a reliable assignment of cations and unraveling the plausible fragmentation channels. Based on the measurement of the absolute total ionization cross section of DHP (18.1 ± 0.9) × 10−16 cm2 at 100 eV energy, the absolute total and partial ionization cross sections of DHP were determined in the 5–140 eV electron energy. Moreover, a machine learning algorithm that was trained with measured cross sections from 25 different molecules was used to predict the total ionization cross section for DHP. Comparison of the machine learning simulation with the measured data showed acceptable agreement, similar to that achieved in past predictions of the algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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