The role of stereochemistry in combustion processes

Abstract

Stereochemical effects significantly influence chemical processes, yet it is not well understood if they are a leading source of uncertainty in combustion modeling. Stereochemistry influences a combustion model (i) at the earliest stage of its construction when mapping the reaction network, (ii) in the computation of individual thermochemical and rate parameters, and (iii) in the prediction of combustion observables. The present work reviews the importance of enumerating stereochemical species and reactions at each of these steps. Further, it analyzes the separate influence of several types of stereochemistry, including geometric, optical, and fleeting transition state diastereomers. Three reaction networks serve to examine which stages of low‐temperature oxidation are most affected by stereochemistry, including the first and second oxidation of n‐butane, the third oxidation of n‐pentane, and the early stages of pyrolysis of 1‐ and 2‐pentene. The 149 reactions in the n‐butane mechanism are expanded to 183 reactions when accounting for diastereomerism. Each of these 183 reactions is parameterized with ab initio kinetics computations to determine that, for the n‐butane mechanism, the median factor of diastereomeric deviation is 3.5 at 360 K for rate constants and as high as 1.6 for mechanism reactivity, in terms of ignition delay times, as opposed to a mechanism without stereochemical expansion.This article is categorized under: Theoretical and Physical Chemistry > Reaction Dynamics and Kinetics Theoretical and Physical Chemistry > Thermochemistry Software > Simulation Methods

Radical Stereochemistry: Accounting for Diastereomers in Kinetic Mechanism Development

“…The TS AMChI also describes the configuration of what we call “fleeting” stereocenters, which are only present in the TS and result in multiple TS stereoisomers connecting the same reactants and products. In forthcoming work, we show that such fleeting TS stereochemistry has a non-negligible impact on model predictions.…”