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Abstract
A promising solar-energy storage approach comprises of hydrogen fuel generation by photocatalytic water splitting under sunlight. For this scheme to proceed, efficient and inexpensive photocatalytic materials are needed. Solid solutions of binary semiconductors represents a promising approach to achieve this goal by improving the photocatalytic properties of semiconductor-based materials. Nonetheless, the effects of site-occupancy disorder on the photocatalytic properties of solid solutions are difficult to predict and consequently numerous and convoluted experimental trials may be required before attaining any significant enhancement. This problem keeps progress in the field of solar-energy storage limited.
In this thesis, a theoretical solution to the design challenge of efficient and economically affordable photocatalytic materials based on binary semiconductors is presented. This solution consists of (1) applying first-principles computational methods to estimate the mixing free energy and structural and electronic properties of solid solutions and (2) perform a rigorous statistical treatment of site-occupancy disorder through the multi-configurational supercell approach. This method is applied to the study of the thermodynamic and electronic properties of (GaP)x(ZnS)1−x solid solutions, a promising material for photocatalytic watersplitting applications.
These computational results represent an overall excellent agreement with the available experimental data, namely: 1) the zinc-blende polymorph is more favorable than the wurtzite polymorph at any composition, 2) the solid solution energy band gap lies within the visible light range with a band gap within 2-3 eV for 25 % < 75%, and 3) the energy band gap is direct for compositions x ≤ 75%. It is found that at ambient conditions, (GaP)x(ZnS)1−x solid solutions with x ≈ 25%, 50% and 75% render efficient hydrogen evolution photocatalysts for water splitting under visible light, owing to their optimal energy band gaps and band levels relative to vacuum. This theoretical approach is computationally affordable and has been proved to be accurate, hence it has the potential to advance the field of solar-energy storage.