Using predictive modeling to improve catalyst performance in low temperature diesel combustion



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Diesel engine emissions are a major cause of air pollution producing carbon monoxide (CO), hydrocarbons, nitrogen oxide molecules (NOx), and particulate matter (PM). Low temperature combustion engines offer a promising solution for reducing NOx and PM emissions, but in turn, the lower temperature interferes with the diesel oxidation catalyst (DOC) causing an increase in CO and hydrocarbon emissions. To combat this, the development of new catalysts is critical. Recent studies by Song et. all show that a Pd/Cu alloy could lead to inhibition-free low temperature oxidation reactions. By modifying a python modeling package, Surface EP, a number of variations of Pd/Cu surfaces can be predicted quickly and with relative accuracy. The program develops a three by three surface cell and can test for binding energies with Oxygen at the three possible site locations (Top, Bridge, and Hollow). These variations are then screened for the best possible matches. Further testing can then be conducted for additional binding energies in the oxidation reaction using Density Functional Theory (DFT) calculations.