|Capability Title||Physical and chemical fuel property prediction|
|Laboratories||National Laboratory (LLNL), National Renewable Energy Laboratory (NREL), Pacific Northwest National Laboratory (PNNL), Lawrence Livermore, Sandia National Laboratories (SNL)|
|Capability experts||Tim Bays (PNNL), Anthe George (SNL), Seonah Kim (NREL), Scott Wagnon (LLNL)|
|Limitations||All techniques developed and used within the Co-optima program have limitations; Co-optima researchers are experts in understanding which techniques will give the most appropriate and high impact results for a given application.|
|Unique aspects||Computational tools and techniques have been specifically developed within Co-optima to enable the identification of the promising fuel targets for a given combustion approach, by understanding which molecules and molecular classes have the most advantageous properties. Once properties have been predicted and molecules identified using a variety of computational techniques, the retrosynthesis approach can give optimal production pathways to be tested and validated.|
|Availability||Staff availability determines availability of individual capabilities. Contact Co-Optima for more information.|
RetSynth: determining all optimal and sub-optimal synthetic pathways that facilitate synthesis of target compounds in chassis organisms, Whitmore et al., BMC Bioinformatics volume 20, Article number: 461 (2019).
Development of a data-derived sooting index including oxygen-containing fuel components, Peter St. John, Seonah Kim, Robert L. McCormick, Energy & Fuels 2019 (accepted) https://doi.org/10.1021/acs.energyfuels.9b02458.
Measuring and Predicting Sooting Tendencies of Oxygenates, Alkanes, Alkenes, Cycloalkanes, and Aromatics on a Unified Scale, Dhruhajyoti D. Das, Peter St. John et al., Combustion and Flame, 190, 349-364 (2018).
A quantitative model for the prediction of sooting tendency from molecular structure, Peter C. St John et al., Energy & Fuels, 31 (9), 9983-9990 (2017).
Measuring and predicting the vapor pressure of gasoline containing oxygenates, Gaspar et al., https://doi.org/10.1016/j.fuel.2019.01.137.
Functional Group Analysis for Diesel-like Mixing-Controlled Compression Ignition Combustion Blendstocks, Gaspar et al., https://doi.org/10.2172/1430464.
Mapping chemical performance on molecular structures using locally interpretable explanations, Whitmore et al., arXiv preprint arXiv:1611.07443.
RON prediction models for new fuel and vehicle systems, Hudson et al., https://www.osti.gov/servlets/purl/1366835.
Measuring and predicting sooting tendencies of oxygenates, alkanes, alkenes, cycloalkanes, and aromatics on a unified scale, Das et al., https://doi.org/10.1016/j.combustflame.2017.12.005.