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Ab Initio Study on the Oxidation of NCN by O (3P):  Prediction of the Total Rate Constant and Product Branching Ratios.

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Title: Ab Initio Study on the Oxidation of NCN by O (3P):  Prediction of the Total Rate Constant and Product Branching Ratios.
Authors: R. S. Zhu1, M. C. Lin1
Source: Journal of Physical Chemistry A. Jul2007, Vol. 111 Issue 29, p6766-6771. 6p.
Subjects: Physical & theoretical chemistry, Science, Chemistry, Physical sciences
Abstract: The reaction of NCN with O is relevant to the formation of prompt NO according to the new mechanism, CH N2→ cyclic-C(H)NN− → HNCN → H NCN. The reaction has been investigated by ab initio molecular orbital and transition state theory calculations. The mechanisms for formation of possible product channels involved in the singlet and triplet potential energy surfaces have been predicted at the highest level of the modified GAUSSIAN-2 (G2M) method, G2M (CC1). The barrierless association/dissociation processes on the singlet surface were also examined with the third-order Rayleigh−Schrödinger perturbation (CASPT3) and the multireference configuration interaction methods including Davidson's correction for higher excitations (MRCIQ) at the CASPT3(6,6)/6-311G(3df)//UB3LYP/6-311G(d) and MRCIQ(6,6)/6-311G(3df)//UB3LYP/6-311G(d) levels. The rate constants for the low-energy channels producing CO N2, CN NO, and N(4S) NCO have been calculated in the temperature range of 200−3000 K. The results show that the formation of CN NO is dominant and its branching ratio is over 99% in the whole temperature range; no pressure dependence was noted at pressures below 100 atm. The total rate constant can be expressed by:  kt4.23 × 10-11T0.15exp(17/T) cm3molecule-1s-1. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Physical Chemistry A is the property of American Chemical Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Ab Initio Study on the Oxidation of NCN by O (3P):  Prediction of the Total Rate Constant and Product Branching Ratios.
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  Data: The reaction of NCN with O is relevant to the formation of prompt NO according to the new mechanism, CH N2→ cyclic-C(H)NN− → HNCN → H NCN. The reaction has been investigated by ab initio molecular orbital and transition state theory calculations. The mechanisms for formation of possible product channels involved in the singlet and triplet potential energy surfaces have been predicted at the highest level of the modified GAUSSIAN-2 (G2M) method, G2M (CC1). The barrierless association/dissociation processes on the singlet surface were also examined with the third-order Rayleigh−Schrödinger perturbation (CASPT3) and the multireference configuration interaction methods including Davidson's correction for higher excitations (MRCIQ) at the CASPT3(6,6)/6-311G(3df)//UB3LYP/6-311G(d) and MRCIQ(6,6)/6-311G(3df)//UB3LYP/6-311G(d) levels. The rate constants for the low-energy channels producing CO N2, CN NO, and N(4S) NCO have been calculated in the temperature range of 200−3000 K. The results show that the formation of CN NO is dominant and its branching ratio is over 99% in the whole temperature range; no pressure dependence was noted at pressures below 100 atm. The total rate constant can be expressed by:  kt4.23 × 10-11T0.15exp(17/T) cm3molecule-1s-1. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Group: Ab
  Data: <i>Copyright of Journal of Physical Chemistry A is the property of American Chemical Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1021/jp068991b
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        Text: English
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