(336e) Discovering Kinetic Solvent Effects in Hydrogen Abstraction Reactions for Use in Automatic Mechanism Generation | AIChE

(336e) Discovering Kinetic Solvent Effects in Hydrogen Abstraction Reactions for Use in Automatic Mechanism Generation


Slakman, B. - Presenter, Northeastern University
West, R. H., Northeastern University

Liquid phase reaction mechanisms are of great interest within areas of biochemistry and chemical technology. Solvation affects reaction rates, not just due to the solvent’s electrostatic properties, but because of the differential solvation of reactants and transition states. Thus, for different reactions of the same class, kinetic solvent effects will depend on the reacting species’ functional groups. For radical-radical and radical-molecule reactions, it is difficult to measure all of the necessary rates experimentally, and furthermore, the reaction networks can contain hundreds to thousands of chemical species and reactions. Therefore, we would like to estimate kinetic solvent effects computationally. The Reaction Mechanism Generator (RMG) software [1] automatically generates detailed kinetic models in the gas phase and currently has capabilities for liquid-phase thermodynamics and diffusion in 25 solvents [2]. This work adds intrinsic kinetic corrections to gas phase rates in RMG to account for solvation.

A set of representative hydrogen abstraction reactions was investigated with Gaussian to find reactant and transition state geometries and energies using Density Functional Theory (DFT) and the SMD solvation algorithm. These reactions included OH·, ·OOH, and CH3· abstracting hydrogen atoms from alkanes and alcohols, which are reactions particularly relevant in both lipid peroxidation and in liquid fuel oxidation. For each reaction, the difference in barrier height between solution phase and gas phase was calculated in eight solvents. Trends were elucidated from this data based upon solvent and molecular structure.

The relationships found were implemented into RMG by adding a solvation module into its kinetics database. Hierarchical trees based on molecular structure, which are already used in RMG for thermodynamic and kinetic data calculations, aid in this effort. The results show promise that kinetic solvent effects can be estimated for other reaction classes in liquid-phase mechanisms. Finding trends for other reaction families, specifically intra-hydrogen migration, is in progress.


  1. [1]  Green WH, Allen JW, Bhoorasingh P, Buesser BA, Ashcraft RW, Beran GJ, Class CA, Gao C, Goldsmith CF, Harper MR, Jalan A, Khanshan FS, Magoon GR, Matheu DM, Merchant SS, Mo JD, Petway S, Raman S, Sharma S, Slakman B, Song J, Geem KMV, Wen J, West RH, Wong A, Wong HW, Yelvington PE, Yee N, Yu J. RMG — Reaction Mechanism Generator-Python. 2013. URL rmg.mit.edu

  2. [2]  Jalan A, West RH, Green WH. An extensible framework for capturing solvent effects in computer generated kinetic models. J Phys Chem B. 2013;117(10):2955–70. doi:10.1021/jp310824h.