Both scientific discovery and technological development are at some point faced with thequestionof how toprogress from a trial-and-error approach to ahighly controlled design process. In heterogeneous catalysis, the search for the optimal active site of a catalyst for a given chemical reaction has been the central objective of research for almost a century. In 1925, Taylor put forward the idea that on a solid catalyst ‘there will be all extremes between the case in which all the atoms in the surface are active and that in which relatively few are so active’ [1]. Ever since the formulation of the Taylor concept of active sites, the quest for observing, identifying, modifying, and designing active sites of heterogeneous catalysts has been on. Heterogeneous catalysis involves an extremely complex set of phenomena, and in order to develop catalyst design strategies, an identification of key parameters, that are principally responsible for the catalytic rate and selectivity (lumped together as ‘activity’ in the following) is needed. A simple approach in this direction has been developed recently for transition-metal surface catalysis [2,3]. The central concept is known as energy scaling relations [4], which together with activity maps and the d-band model have made it possible to develop a quantitative understanding of trends in transition-metal catalysis and enabled prediction of new catalysts [3,5]. These scaling relations are correlations between surface bond energies of different adsorbed species including transition states. Correlations between activation energies and reaction energies, Bronsted–Evans–Polanyi relations, are known throughout chemistry [6,7] and have for long been assumed in heterogeneous catalysis [8]. With the advance of computational methods, it has been discovered that scaling relations are much more general and for a number of reactions over transition-metal surfaces they have been shown to include essentially all intermediates and transition states.The scaling relations enable amapping of the many energetic parameters determining the rate of a catalytic reaction onto a reduced phase space spanned by a few energy parameters known as descriptors [2,3]. As a result, a catalytic activity map can be constructed defining the rate and/or selectivity in the relevant descriptor space i.e. the relevant bond strength between one or more of the reactants or intermediates and the catalyst surface. The activity map exhibits at least one maximum for the optimal bond strength(s), which defines the optimum catalyst. This approach is illustrated for a simple model of the ammonia synthesis process in Fig. 1a. Following [9], the rate of ammonia synthesis at industrial conditions is calculated in a model that assumes N2 dissociation to be rate limiting and that adsorbed N atoms are the main intermediate covering the surface. In such a model, the rate can be calculated as a function of the transition-state energy for N2 dissociation, EN-N, and the N adsorption energy, EN.The two energy parameters are seen to scale verywell, and hence a single parameter, EN, is a good descriptor of the catalytic activity (see Fig. 1b). This is a very simple example of complexity reduction from two to one descriptor.The quantitative aspect of the descriptor approach provides new possibilities in catalyst design. This is also illustrated in Fig. 1b. Knowing which descriptor value defines the highest activity allows for searches for new catalysts with close to optimum properties. While the concept of scaling relations has proven extremely useful by providing an understanding of trends as well as new catalyst design criteria, it has also helped us identify some of the limitations on the performance of large classes of catalysts [2]. Fig. 1a illustrates the point. Clearly, a much better catalyst for ammonia synthesis could be devised if we could find effective ways of circumventing the scaling relations that we know for (stepped) transition-metal surfaces, or, equivalently, find catalystswith active site motifs that obey a different lower lying scaling relation than the so far identified ones. There are several other cases where scaling relations have been suggested to impose limitations on the performance of catalysts. Fig. 1c and d show two such examples: it has proven very difficult to find electrocatalysts that bring down the overpotential for O2 reduction significantly below that for Pt, making lowtemperature fuel cells less efficient than wewould like, and similarly, it has proven difficult, so far to find electrocatalysts that can reduce CO2 to form hydrocarbons and alcohols without substantial overpotentials. In both cases, this can be traced
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Oct 23, 2015
Qian Yang + 5
Open Access