316
J. Handzlik
how they are formed, is even less understood. On the other hand, knowledge about
the catalyst structure at the atomic level is extremely important for studying the catalytic reaction mechanisms and necessary in effective designing of new catalysts.
Coordination environment of the transition metal is a key factor influencing the catalytic properties of the surface metal species. Especially, the local properties of the
support, which can be considered as a multidentate ligand coordinated by the metal
centre, can dramatically affect the reactivity. However, establishing the structure–activity relationships for silica-supported metal oxide catalysts is still a challenging
task, mainly due to heterogeneity of the metal species on amorphous surface and low
fraction of the active sites formed in situ.
Computational modelling, taking advantage of growing computer power, enables
for development of advanced models representing surface metal species. Density
functional theory (DFT) methods, offering a good balance between the accuracy and
the cost, are nowadays commonly used in the field of computational catalysis. Theoretical investigations are helpful in interpretation of spectroscopic data concerning
supported transition metal oxide species. Quantum chemistry methods, especially
when combined with advanced models of the surface, can also provide complementary information about the catalytic system, not accessible at this moment by
experimental techniques. Better understanding the nature of the active sites and their
precursors, as well as mechanisms of the catalytic reactions and the role of the amorphous surface, is possible owing to computations. Many examples of such computational studies, concerning silica-supported chromium, molybdenum and tungsten
oxide systems, are presented in this chapter.
2 Surface Modelling
An adequate model representing surface is crucial for realistic description of the
supported metal oxide systems. Especially, modelling amorphous materials with
inhomogeneous distribution of surface metal sites is challenging. There are two main
approaches to simulate surface of solid, cluster approximation [1–6] and periodic
slab models [1–5, 7–10]. A cluster model is a finite fragment cut off from the solid,
with dangling bonds usually saturated by hydrogen atoms. Consequently, long-range
interactions are neglected, which can be justified for covalent amorphous materials
like silica. Small clusters, often used in the past due to limitations of computer
power, cannot reproduce the complexity of the amorphous surface, although they are
handy for efficient computations of reaction mechanisms. Nowadays, it is possible
to employ very big cluster models, containing dozens or even hundreds of atoms [3,
4]. To reduce the computational cost or enlarge the model, partitioning into layers
treated at different levels of theory can be done [11]. The active site and its close
vicinity are then calculated at a higher level of theory (inner layer), while the rest of
the system (outer layer) is described by a computationally less demanding method.
An example of such hybrid method is the ONIOM partitioning scheme [11] (Fig. 1).
J. Handzlik
how they are formed, is even less understood. On the other hand, knowledge about
the catalyst structure at the atomic level is extremely important for studying the catalytic reaction mechanisms and necessary in effective designing of new catalysts.
Coordination environment of the transition metal is a key factor influencing the catalytic properties of the surface metal species. Especially, the local properties of the
support, which can be considered as a multidentate ligand coordinated by the metal
centre, can dramatically affect the reactivity. However, establishing the structure–activity relationships for silica-supported metal oxide catalysts is still a challenging
task, mainly due to heterogeneity of the metal species on amorphous surface and low
fraction of the active sites formed in situ.
Computational modelling, taking advantage of growing computer power, enables
for development of advanced models representing surface metal species. Density
functional theory (DFT) methods, offering a good balance between the accuracy and
the cost, are nowadays commonly used in the field of computational catalysis. Theoretical investigations are helpful in interpretation of spectroscopic data concerning
supported transition metal oxide species. Quantum chemistry methods, especially
when combined with advanced models of the surface, can also provide complementary information about the catalytic system, not accessible at this moment by
experimental techniques. Better understanding the nature of the active sites and their
precursors, as well as mechanisms of the catalytic reactions and the role of the amorphous surface, is possible owing to computations. Many examples of such computational studies, concerning silica-supported chromium, molybdenum and tungsten
oxide systems, are presented in this chapter.
2 Surface Modelling
An adequate model representing surface is crucial for realistic description of the
supported metal oxide systems. Especially, modelling amorphous materials with
inhomogeneous distribution of surface metal sites is challenging. There are two main
approaches to simulate surface of solid, cluster approximation [1–6] and periodic
slab models [1–5, 7–10]. A cluster model is a finite fragment cut off from the solid,
with dangling bonds usually saturated by hydrogen atoms. Consequently, long-range
interactions are neglected, which can be justified for covalent amorphous materials
like silica. Small clusters, often used in the past due to limitations of computer
power, cannot reproduce the complexity of the amorphous surface, although they are
handy for efficient computations of reaction mechanisms. Nowadays, it is possible
to employ very big cluster models, containing dozens or even hundreds of atoms [3,
4]. To reduce the computational cost or enlarge the model, partitioning into layers
treated at different levels of theory can be done [11]. The active site and its close
vicinity are then calculated at a higher level of theory (inner layer), while the rest of
the system (outer layer) is described by a computationally less demanding method.
An example of such hybrid method is the ONIOM partitioning scheme [11] (Fig. 1).