Unimolecular Fragmentation Properties of Thermometer Ions from Chemical Dynamics Simulations

Abstract

Thermometer ions are widely used to calibrate the internal energy of the ions produced by electrospray ionization in mass spectrometry. Typically, benzylpyridinium ions with different substituents are used. More recently benzhydrylpyridinium ions were proposed for their lower bond dissociation energies. Direct dynamics simulations using M06-2X/6-31G(d), DFTB, and PM6-D3 are performed to characterize the activation energies of two representative systems: para-methyl-benzylpyridinium ion (p-Me-BnPy + ) and methyl,methyl-benzhydrylpyridinium ion (Me,Me-BhPy + ). Simulation results are used to calculate rate constants for the two systems. These rate constants and their uncertainties are used to find the Arrhenius activation energies and RRK fitted threshold energies which give reasonable agreement with calculated bond dissociation energies at the same level of theory. There is only one fragmentation mechanism observed for both systems, which involves C-N bond dissociation via a loose transition state, to generate either benzylium or benzhydrylium ion and a neutral pyridine molecule. For p-Me-BnPy + using DFTB and PM6-D3 the formation of tropylium ion, from rearrangement of benzylium ion, was observed but only at higher excitation energies and for longer simulation times. These observations suggest that there is no competition between reaction pathways that could affect the reliability of internal energy calibrations. Finally, we suggest using DFTB with modified-Arrhenius model in future studies.

Efficient and Accurate Description of Diels‐Alder Reactions Using Density Functional Theory**

“…[20,21] Of course these two options do not cover all the spectrum of possibilities, for example tight-binding DFT shows recently that it is able to deal with chemical reaction dynamics. [22,23] Another possibility is also to train and/or use a machine learning algorithm for chemical reactions. [24] While some attempts to use this technique have been performed to predict activation energies, [25] it is at a preliminary stage and will need a huge data-set.…”

“…Of course these two options do not cover all the spectrum of possibilities, for example tight‐binding DFT shows recently that it is able to deal with chemical reaction dynamics [22,23] . Another possibility is also to train and/or use a machine learning algorithm for chemical reactions [24] .…”

Quantum versus Classical Unimolecular Fragmentation Rate Constants and Activation Energies at Finite Temperature from Direct Dynamics Simulations

“…From the ensemble of trajectories and associated reaction times it is thus possible to reconstruct the number of trajectories which are in the reactant state at each time frame. If the resulting curve follows a single exponential decay (as it was generally observed in previous studies 18,20,21,[57][58][59] ) then we can directly obtain τ and thus k.…”

Quantum versus classical unimolecular fragmentation rate constants and activation energies at finite temperature from direct dynamics simulations

“…From the ensemble of trajectories it is thus possible to reconstruct the number of trajectories which are in the reactant state at each time frame. If the resulting curve follows a single exponential decay (as it was generally observed in previous studies 18,20,21,64–66 ) then we can directly obtain τ and thus k .…”