What is it about?

In the present work, we have investigated how the heterocyclic molecule dihydropyran behaves when colliding with electrons. In particular, we examined the different ways in which this molecule decomposes after collisions. DHP is a ring-shaped molecule that provides a promising scaffold for the synthesis of new anti-cancer medicines and biofuels. First, we carried out an experiment in which we crossed an electron beam with the outgoing beam of dihydropyran gas. When an electron interacts with a molecule, it can knock an electron out of its electronic structure. This usually makes the molecule unstable and leads to its dissociation into ionized and neutral fragments. Charged fragments of dihydropyran were recorded at different electron energies using mass spectrometry to create the mass spectra. We then used two state-of-the-art, sophisticated computational techniques. Molecular dynamics (MD) methods were used to simulate and confirm the fragmentation pathways, thus aiding in interpreting and assigning the mass spectra. Finally, we used an artificial intelligence-based machine learning technique to estimate the probability of each given dissociation pathway.

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Why is it important?

Heterocycles contain atoms of at least two different elements in their ring structures. The dihydropyran ring, for example, consists of five carbon atoms bonded to an oxygen atom. More than half of all known chemical compounds are heterocyclic. They are naturally present in all nucleic acids, most pharmaceuticals, amino acids, biomass, alkaloids, enzymes, vitamins, and many natural and synthetic pigments. As such, they appear in different physicochemical environments that can be exposed to various forms of radiation. Therefore, investigating the radiation-induced fragmentation pathways of these compounds is crucial for gaining a deeper understanding of fundamental processes, such as radiation damage to living cells, physicochemical processes in interstellar environments, and reaction mechanisms occurring in combustion or plasma.

Perspectives

Ivan Ljubić: This article gave me a nice opportunity to apply relatively recently developed methods for computing mass spectra by combining molecular dynamics with first-principles quantum chemical methods. Working with our expert international team was a deeply rewarding experience. I learned a lot about recording and analyzing mass spectral data in detail and the application of original machine learning (i.e., artificial intelligence-based) techniques for predicting ionization cross sections. Allison Harris: This work shows the power of collaboration between experiment, theory, and simulation to better understand fundamental processes. By combining state-of-the art measurements with cutting-edge tools such as machine learning and molecular dynamics simulations, we were able to identify the physical mechanisms by which DHP may break apart. It was a pleasure to be involved this project, in which people with different expertise came together to improve our understanding of important physical mechanisms. Tomasz J. Wasowicz: For me, it has been an immensely satisfying voyage, starting from setting research goals, carrying out experiments, networking with excellent theorists, and ending with writing a paper. I have learned a lot from my collaborators and attained greater insight into the processes underlying the decay of heterocyclic molecules.

Tomasz Wasowicz
Institute of Physics and Applied Computer Science, Faculty of Applied Physics and Mathematics, Gdansk University of Technology

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This page is a summary of: Unveiling the electron-induced ionization cross sections and fragmentation mechanisms of 3,4-dihydro-2H-pyran, The Journal of Chemical Physics, August 2024, American Institute of Physics,
DOI: 10.1063/5.0218160.
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