The resolution and dispersion of features in 2-D 1H-13C HSQC NMR spectra of liquid fuels derived from fossil hydrocarbons or biomass afford a signature-like representation of the major chemical components. The features (resonance peaks) represent semi-quantitatively the specific carbon types and functional groups present in the molecules comprising the complex mixture. Insofar as the structures of these molecules determine their properties and reactivity and, collectively, that of the mixture, it may be possible for such spectra to be used to build a model based on a set of samples with known fuel properties (derived cetane number, research octane number, etc.), that predicts the same properties for an unknown sample, provided the unknown lies generally within the feature space of the known samples. The critical spectral features for the model can then be identified as specific types of carbons present in the component molecules from the same NMR data. Compared to similar models developed using 1-D 13C NMR spectra, models built from 2-D HSQC spectra should be inherently more discriminating, provided the correct set of features can be identified. Likewise, the 1H-13C correlation information in the 2-D data is more valuable for identifying specific chemical components associated with properties, particularly when accurate chemical shift values for component standards are available in a database. Toward this goal, a chemical shift database comprising correlated one-bond 1H-13C chemical shift values for several hundred fuel-like hydrocarbons, as well as some additional oxygenated compounds that may be present in bio-derived fuels, in deuterated chloroform solvent has been assembled and annotated according to several dozen applicable carbon types, and is being made available. An iterative cycle comprising data collection, characterization within one or more predictive models, identification of key features and the compounds or carbon types they represent, and fuel formulation or multicomponent blending, would constitute a valuable practical tool for fuel design.

Fuel component composition and predictive property models from 2-D 1H-13C HSQC NMR spectra

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

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