Mixed-metal cobalt oxide, namely cobalt aluminate and titanate, were shown to be water gas shift inactive.

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Partial Least Squares (PLS) was used to develop Quantitative Structure -Property Relationships (QSPRs) to analyze substrate structures in asymmetric ketone hydrogenation reactions with Noyoris catalyst. For a set of benzophenone substrates, PLS models were obtained which could correlate the substrate structure to the product enantiomeric excess (e.e.). The structural conformation used for calculating some of the descriptors was found to be important for obtaining satisfying model properties. Acceptable R 2 and Q 2 values could be obtained despite the small training data set of only 13 benzophenones. The model was subsequently used for the systematic estimation of product e.e. values for a series of other benzophenone substrates. Reaction results for two benzophenones show that the model can correctly indicate a low, medium or high expected e.e. value.

The use of high throughout and combinatorial experimentation is becoming commonplace in catalytic research. The benefits of parallel experiments are not only limited to reducing the time-to-market, but also give an opportunity to study processes in more depth, by generating more data. To investigate the complete parameter space, multiple experiments must be performed and the variability between these experiments must be quantifiable. In this project, the reproducibility and variance in high throughput catalyst preparation and parallel testing were determined. High-performance equipment was used in a catalyst development program for fuel processing, the production of fuel cell-grade hydrogen from hydrocarbon fuels. Four studies, involving water-gas shift conversion and high-temperature steam methane reforming, were performed to determine the reproducibility of the workflow from automated catalyst preparation to parallel activity testing. Statistical analyses showed the standard deviation in catalytic activities as determined by conversion, to be less than 6% of the average value.

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