Abstract
We report the results of a computational study of TiO2 nanoclusters of various sizes as well as of complex systems with various molecules adsorbed onto the clusters to set the ground for the modeling of charge transfer processes in hybrid organic–inorganic photovoltaics or photocatalytic degradation of pollutants. Despite the large number of existing computational studies of TiO2 clusters and in spite of the higher computing power of the typical available hardware, allowing for calculations of larger systems, there are still studies that use cluster sizes that are too small and not appropriate to address particular problems or certain complex systems relevant in photovoltaic or photocatalytic applications. By means of density functional theory (DFT) calculations, we attempt to find acceptable minimal sizes of the TinO2n+2H4 (n = 14, 24, 34, 44, 54) nanoclusters in correlation with the size of the adsorbed molecule and the rigidity of the backbone of the molecule to model systems and interface processes that occur in hybrid photovoltaics and photocatalysis. We illustrate various adsorption cases with a small rigid molecule based on coumarin, a larger rigid oligomethine cyanine dye with indol groups, and the penicillin V antibiotic having a flexible backbone. We find that the use of the n = 14 cluster to describe adsorption leads to significant distortions of both the cluster and the molecule and to unusual tridentate binding configurations not seen for larger clusters. Moreover, the significantly weaker bonding as well as the differences in the density of states and in the optical spectra suggest that the n = 14 cluster is a poor choice for simulating the materials used in the practical applications envisaged here. As the n = 24 cluster has provided mixed results, we argue that cluster sizes larger than or equal to n = 34 are necessary to provide the reliability required by photovoltaic and photocatalytic applications. Furthermore, the tendency to saturate the key quantities of interest when moving from n = 44 to n = 54 suggests that the largest cluster may bring little improvement at a significantly higher computational cost.
Highlights
Nanostructured titanium dioxide is well-known for its numerous and diverse applications [1,2]
In order to ensure the charge neutrality in the presence of under-coordinated Ti atoms [48,59,60], four hydrogen atoms were used to solve the dangling bonds of the oxygen atoms bound to the two Ti atoms at the periphery of the Ti14O30H4 cluster, or to the three-fold coordinated Ti atoms in the corners of Ti24O50H4, Ti34O70H4, Ti44O90H4, and Ti54O110H4 clusters. These structures, for the molecules investigated, C343, OMCD1, and penicillin V, both isolated and adsorbed on the cluster, were energy minimized by Density functional theory (DFT) [12,13,14] calculations using the hybrid B3LYP exchange-correlation functional [61,62,63] and the split valence 3-21G(d) basis set with polarization functions [64,65]
Our results showed that the optical spectra, provided by Time-Dependent DFT (TD-DFT) calculations for clusters of various sizes, indicate significant changes in both wavelength and intensity of the electronic transitions in the visible range, where the solar intensity reaches its maximum
Summary
Nanostructured titanium dioxide is well-known for its numerous and diverse applications [1,2] Examples of such new uses are in photocatalytic degradation of pollutants [2,3,4], in dye-sensitized solar cells, for energy conversion [5,6], in sensor electronics (for detection of gases, chemicals, or biological materials) [7], in coatings with antibacterial, anticorrosion, antifogging, or self-cleaning properties [8], in drug delivery [9], etc. Understanding the structure-property correlations for TiO2 nanostructured materials is crucial for optimizing their use in practical applications; it remains a challenging task even after decades of extensive studies. Despite some inherent difficulties [14] and limitations [15], DFT methods have been able to treat molecular systems of relatively large sizes, of up to several hundreds of atoms, with moderate computational costs [16,17]
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