A theoretical and experimental study of 2-ethylfuran plus OH reaction

2-Ethylfuran (EF2), having similar thermophysical properties as gasoline, can be produced from biomass and has received increasing attention in recent years. The reaction of EF2 with OH radical plays a fundamental role in combustion chemistry of furan fuels. This work provides a comprehensive investigation on the reaction kinetics of the title reaction. The performance of several density functional theory methods is evaluated against the DLPNO-CCSD(T)/CBS(T-Q) benchmark method. The results show that M08-HX/jun-cc-pVTZ is the best one for both abstraction and addition reactions with the lowest average unsigned deviations of 0.61 and 0.63 kcal mol-1, respectively. Multi -structural variational transition state theory combined with small -curvature tunneling approximation (MS-CVT/SCT) is employed to calculate the high-pressure limit (HPL) rate constants over a wide temperature range of 500-2000 K. The system -specific quantum Rice-Ramsperger-Kassel (SS-QRRK) method is used to obtain the pressure -dependent rate constants for addition reactions at several selected pressures from 0.01 to 100 atm. We have measured the rate constants of EF2 + OH reaction behind reflected shock wave at temperatures of 985-1342 K and pressure around 1 bar. The measured rate constants exhibit a positive temperature dependence and may be described by a three -parameter Arrhenius expression: k = 1.28 x 10-10 x T6.89exp ?5318 ) (cm3 mol- 1 s-1). The calculated results are in good agreement with measurements and the total T rate constants exhibit a non -monotonic temperature dependence due to the competition between the abstraction and addition channels. At the HPL, the addition reaction is dominant, and the branching ratio is larger than abstraction reaction at 500-1800 K and is 0.92 at 500 K. Finally, to evaluate the performance of our calculations on the ignition delay time of EF2, three available kinetic models (Somers et al., Xu et al. and Li et al.) of EF2 are updated and the results show that the predictions of Li's model are improved.