Selective C–C coupling of oxygenates is pertinent to the manufacture of fuel and chemical products from biomass and from derivatives of C1 compounds (i.e., oxygenates produced from methane and CO2). Here we report a combined experimental and theoretical study on the temperature-programmed reaction (TPR) of acetaldehyde (AcH) on a partially reduced CeO2–x (111) thin film surface. The experiments have been carried out under ultra-high-vacuum conditions without continuous gas exposure, allowing better isolation of active sites and reactive intermediates than in flow reaction conditions. AcH does not undergo aldol condensation in a typical TPR procedure, even though the enolate form of AcH (CH2CHO) is readily produced on CeO2–x (111) with oxygen vacancies. We find however that a tailored “double-ramp” TPR procedure is able to successfully produce an aldol adduct, crotonaldehyde (CrA). Using density functional theory calculations and microkinetic modeling we explore several possible C–C coupling pathways. We conclude that the double-ramp procedure allows surface oxygen vacancy dimers, stabilized by adsorbate occupation, to form dynamically during the TPR. The vacancy dimers in turn enable C–C coupling to occur between an enolate and an adjacent AcH molecule via a bifunctional enolate–keto mechanism that is distinct from conventional acid- or base-catalyzed aldol condensation reactions. The proposed mechanism indicates that CrA desorption is rate-limiting while C–C coupling is facile.

No abstract

The emphasis on pipeline safety through reliability analysis strategies in the pipeline industry has pushed the process of uncertainty quantification towards advanced statistical techniques. Probabilistic modeling of pipeline threats and inline inspection (ILI) validation/tool performance assessment are areas that have all benefited from advances in uncertainty quantification. More recently, the statistical calibration of measurement uncertainty has developed as a promising approach for reducing the effect of measurement errors that exist in both ILI and field non-destructive examination (NDE) measurement readings. Using traditional methods for estimating measurement error quantities in ILI data, calibration provides a robust and simple approach towards incorporating the error information towards obtaining an improved estimate of the true value. Since calibration is performed at the individual feature-level, its combination with probabilistic modeling using statistical distributions at the parameter-level can potentially provide an opportunity to understand its effect on the probabilistic analysis (leak or burst limit state). In this work, the impact of applying statistical calibration towards identifying/utilizing ILI and NDE measurement uncertainty in conjunction with probabilistic modelling is explored through an investigated problem.