SEARCH · Search NASA
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Production of an oleo-furan surfactant precursor from biomass-derived furoic acid and fatty acid via cross-ketonization.
The rising demand for linear alkylbenzene surfactants (LAS) poses an environmental threat as LAS are industrially produced from petroleum using corrosive acid catalysts. Cross ketonization is a promising route of converting furans and fatty acids to oleo-furansulfonates, precursors for renewable, performance-enhanced LAS replacements. Here, we explore various alkaline earth metals and mixed metal oxides for the cross-ketonization reaction and find that MgO achieves ~90% yield by minimizing the decarboxylation side reaction. Here, we illustrate the role of molecular structure on the cross-ketonization reaction and the role of complex formation over alkaline-earth metal oxides in minimizing side reactions. We overcome catalyst deactivation by regeneration over multiple runs. This research demonstrates the effectiveness of heterogeneous catalysts for cross ketonization toward developing renewable surfactants.
Discerning networks of many reactions among multiple interconverting species is challenging. Here, we present a reaction network identification methodology. Our methodology enumerates all stoichiometrically and chemically feasible reactions and requires statistical evidence from effluent concentrations for the inclusion or exclusion of each from the reaction network, contrasting with the commonly seen incremental approach and other work of relying heavily upon chemical intuition and assuming the reactions occurring. Using graph theory alongside an active learning design of experiments that propose maximally informative feeds, we identify the underlying reaction network with minimal laboratory runs. Here, we introduce chemistry-probabilistic graphical modeling and Boolean reaction circuits to statistically quantify which reactions occur from effluent concentrations. Our methodology accurately discerns active reactions, as showcased upon a laboratory network of cross-ketonization of furoic and lauric acid and validated upon simulated networks of thermal and CO 2 -assisted ethane dehydrogenation.