Abstract
Cycloalkanes serve as an important class of chemical components in both fossil and alternative transportation fuels and have attracted considerable attention from the combustion community. Hydrogen abstractions from cycloalkanes by hydroxyl radicals initiate the fuel decomposition process and trigger off the subsequent chain reactions and thus play an important role in both combustion and atmospheric chemistry. The target of this study is to fill the vacancy in kinetics data toward the H-abstraction reactions by hydroxyl radical from three typical dimethylcyclohexane isomers through first-principles and direct dynamics. The rate constants involving 18 elementary reactions in total were accurately determined by the multipath canonical variational transition state theory with the multidimensional small-curvature correction for tunneling (MP-CVT/SCT), over a broad temperature range of 200-2000 K. The significant roles of multistructural torsional anharmonicity and recrossing effects were stressed per abstraction site, while the quantum tunneling effect was found to be slight at temperatures of interest in combustion. The discrepancies observed among different reaction systems at a similar abstraction site highlight the fuel molecular effects on site-specific rate constants. The comparison results of total rate constants given by different dynamics approaches prove the importance of considering the torsional anharmonicity, recrossing, and tunneling effects, and the robust feature of the simplified MS-CVT/SCT. The calculated total constants for dimethylcyclohexane isomers by OH are consistent with those measured for methylcyclohexane and 1,4-dimethylcyclohexane at low temperatures. The branching ratio analysis confirms the predominant role of the tertiary abstraction at low-to-intermediate temperatures and its growing competition with distinct secondary abstractions as temperature increases. The calculated rate constants were eventually fitted into the analytical expressions and incorporated into the kinetic models to learn about the influences on modeling performance.
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