Login / Signup

The temperature variation of the CH + + H reaction rate coefficients: a puzzle finally understood?

Rafael A Jara-ToroOctavio RonceroFrancois Lique
Published in: Physical chemistry chemical physics : PCCP (2024)
CH + was the first molecular ion identified in the interstellar medium and is found to be ubiquitous in interstellar clouds. However, its formation and destruction paths are not well understood, especially at low temperatures. A new theoretical approach based on the canonical variational transition state theory was used to study the H + CH + reactive collisions. Rate coefficients for formation of C + ions are calculated as a function of temperature. We considered the participation of a direct path and an indirect path in which the reactants should overcome an entropic barrier to form a van der Waals complex or pass through a CH 2 + intermediate complex, respectively. We show that the contribution of both pathways to the formation of C + has to be taken into account. The new reactive rate coefficients for the title reaction, complemented by reactive data for CH + /CH 2 + in the H/H 2 /He mixture, have been used to simulate the corresponding kinetics experimentally measured using an Atomic Beam 22 Pole Trap apparatus at low temperature. A good agreement with the experimental findings was found at 50 K. At a lower temperature, the model overestimates the formation of C + . This shows that secondary reactions are not responsible for the weak C + production in the experiments at such temperature. Then, we discuss the possible impact of non-adiabatic effects in the study of the H + CH + reactive collisions and we found that such effects can be responsible for the decrease of the H + CH + rate coefficients at low temperature. This study offers an explanation for the disagreement between H + CH + theoretical and experimental rate coefficients which has been going on for 20 years and highlights the need for performing non-adiabatic studies for this simple chemical reaction.
Keyphrases
  • room temperature
  • physical activity
  • machine learning
  • ionic liquid
  • deep learning
  • quantum dots