Rational design of heterogeneous catalysts from first principles [electronic resource]

The increasing global energy demand and its corresponding stress on the environment requires catalysts for improving the efficiency of existing industrial processes as well as enabling new ones that harness sustainable sources of energy. To this end, an efficient means of discovering improved catalysts is needed. The rational design of catalysts is one such approach: physical insights are combined with data to construct predictive models, which lead to design principles for identifying promising candidate catalysts. Systematic experiments are then performed for validating the activity of a predicted catalyst, providing further physical insights and helping to refine the predictive models and design principles. With the recent advances in available computing power, computational approaches have emerged as an efficient means of obtaining information regarding catalytic systems. In this thesis, we seek the rational design of catalysts from first principles by using computational results to construct predictive models for evaluating a catalyst's activity. Density functional theory (DFT) calculations are used to obtain information about the energetics in a catalytic reaction, and the correlations between molecules and catalysts are combined with microkinetic models to form a model for predicting catalytic activity. These models prescribe a set of requirements on the chemical descriptors for creating improved catalysts. In all cases, we aim to design catalysts that are comparable to the state-of-the-art in terms of activity and are more abundant. For the hydrogen evolution reaction (HER), we examine earth abundant MoS2 and transition-metal phosphide catalysts as alternatives to the precious-metal catalysts. Starting with hydrogen stability as the descriptor for predicting HER activity, we extract design principles that involve support interactions, transition metal dopants, alloys, elastic strain, and surface vacancy generation. These concepts are validated through a series of experimental studies. For the production of alcohols from syngas (CO2/CO/H2), we mapped out the energetic requirements for a catalyst that is as active as the state-of-the-art while also being resistant to surface-poisoning by formate. The catalyst should have an intermediate binding strength for oxygen and carbon species, while also circumventing a transition-metal scaling limitation involving formate. We identified the molybdenum phosphide (MoP) catalyst as a candidate that fulfills these criteria by having a favorable active site geometry. Through experimental studies, we show that MoP is comparable to the commercial Cu/ZnO/Al2O3 catalyst in activity but also robust to varying feed compositions. We also illustrate the challenges in correlating experiments with theoretical model systems through an example involving the oxygen reduction reaction (ORR). A design principle for improving ORR involves compressively straining Pt catalyst to weaken the binding strength on its surfaces. In order to create a direct comparison, an inert but electrochemically tunable battery electrode was devised as a catalyst support. The platinum surface could then be directly and continuously strained, leading to changes in ORR activity that compare favorably to the predictions. Finally, we examine the limitations in the predictive power of our models. In most cases, the models are based on the scaling relationships established for one surface facet of the d-block transition-metals. Design principles for improved catalysts are thus often identified around breaking a fundamental scaling limitation in the transition-metals. However, since these limitations were used to construct the predictive models, the prediction accuracy for dissimilar systems is unknown. We succeed in generalizing the transition-metal scaling models to non-metal promoted metal surfaces and transition metal sulfides, while also finding that more general and complex models may be needed going forward.

Keywords: Chemical Engineering.
Advisor: Nørskov Jens K.
Abild-Pedersen Frank
Jaramillo Thomas Francisco advisor. Thesis advisor
Zheng Xiaolin