Abstract

In low temperature plasmas, energy transfer between asymmetric stretching excited CO2 molecules can be highly efficient, which leads to further excitation (and de-excitation) of the CO2 molecules: CO2(vas) + CO2(vas) → CO2(vas + 1) + CO2(vas - 1). Through such a vibrational ladder climbing mechanism, CO2 can be activated and eventually dissociates. To gain mechanistic insight of such processes, a full-dimensional accurate potential energy surface (PES) for the CO2 + CO2 system is developed using the permutational invariant polynomial-neural network method based on CCSD(T)-F12a/AVTZ energies at about 39 000 geometries. This PES is used in quasi-classical trajectory (QCT) studies of the vibrational energy transfer between CO2 molecules excited in the asymmetric stretching mode. A machine learning algorithm is used to determine state-specific rate coefficients for the vibrational transfer processes from a limited data set. In addition to the CO2(vas + 1) + CO2(vas - 1) channel, the QCT simulations revealed significant contributions from the CO2(vas + 2,3) + CO2(vas - 2,3) channels, particularly at low collision energies/temperatures. These multi-vibrational-quantum processes are attributed to enhanced energy flow in the collisional complex formed by enhanced dipole-dipole interaction between asymmetric stretching excited CO2 molecules.

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