Determining the properties of biofuels during early-stages of development is difficult because of the small sample volumes that are typically produced. <sup>13</sup>C NMR offers a low-volume analytical technique for quantifying the various chemical substructures within a complex fuel mixture, providing a formulation based upon the relative populations of each carbon type, averaged together from the hundreds to thousands of unique chemicals making up a fuel. These carbon type distributions can be empirically correlated to fuel properties, like octane or cetane number, through relatively simple models. In this work, models for derived cetane number and key points in the simulated distillation curve, T10, T50, and T90, were built using fuel blendstocks derived from thermochemical conversion of biomass. These models are comprised of 5-7 terms representing the most influential chemical substructures for a property. Differences among models within a set for each fuel property show the complex interactions among chemical substructures that impact fuel properties. Further refinement of the substructures and models should more fully describe these interactions. The tool allows properties to be predicted from only 50 – 200 µL of blendstock, and will accelerate early-stage blendstock development. Fuel blends optimized using chemical structures identified with these models will advance new formulations from both fossil and renewable sources having fuel properties tailored for future engine designs.

Fuel property modeling from chemical structures identified using <sup>13</sup>C NMR spectroscopy

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

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Fuel property modeling from chemical structures identified using 13C NMR spectroscopy

Fuel property modeling from chemical structures identified using ¹³C NMR spectroscopy