On Pd(111), thermal activation of ethylene has been reported to yield ethylidyne. Using more approximate models, a plausible three-step mechanism, ethylene f vinyl f ethylidene f ethylidyne, was recently proposed for this process on the basis of DFT calculations. We employed more elaborate computational models and characterized the thermodynamics and kinetics of the mechanism of ethylene conversion to ethylidyne on Pd(111). We carried out density functional slab-model studies for three coverages of the adsorbate, 1/3, 1/4, and 1/9. The resulting refined potential energy landscape turned out to differ notably from that reported previously: our calculated barriers for the various elementary steps are significantly lower than those of previous studies, and we determined the overall process to be exothermic, in contrast to earlier computational results. We show that the three-step mechanism is thermodynamically and kinetically feasible on Pd(111), with the dehydrogenation of ethylene to vinyl being the rate-limiting step at all coverages considered. Direct conversion of ethylene to ethylidene is unlikely due to a very high barrier. Coverage effects have been found important. At high coverage, the rate-limiting first reaction barrier is ∼50 kJ mol -1 above the desorption energy of ethylene, whereas at low coverages the two energies become comparable.

Quality by Design (QbD) is of paramount importance not only for patient safety but also for the timely and uninterrupted supply of products at affordable prices into the market. Both of these objectives can be achieved only through a robust process, and one of the major obstacles for developing a robust process is the quality of input materials and reagents used for the manufacture of active pharmaceutical ingredients (APIs). This article demonstrates the use of QbD methodology to optimize the quality of input materials and make the process more consistent, thereby reducing the variation in the quality of API produced. This article highlights the use of failure mode and effect analysis (FMEA) for the unbiased identification of critical process parameters and critical material attributes associated with the manufacturing of key starting materials, which are later used as input for the design of experiments (DoE) study that is used for the optimization.