Electronic transport and the thermoelectric properties of donor-doped SrTiO3
Source abstract
Abstract
Abstract Strontium titanate (SrTiO3) is widely recognised as an environmentally-benign perovskite material with potential for thermoelectric applications. In this work we employ a systematic modelling approach to study the electronic structure and thermoelectric power factor (PF) of pure SrTiO3 and donor-doped Sr(Ti0.875M0.125)O3 (M = Cr, Mo, W, V, Nb, Ta). We find that the carrier concentration required to optimise the PF of SrTiO3 is on the order of 1021 cm−3, in line with experimental studies. Substitution at the Ti (B) site with 12.5 mol% Nb or Ta is predicted to yield the best PF among the six Group V/VI dopants examined, balancing the Seebeck coefficient and electrical conductivity, and doping with the more abundant Nb would likely give the best price/performance ratio. Although W doping can significantly improve the electrical conductivity, this is at the expense of a reduced Seebeck coefficient. The first-row elements V and Cr have a significantly different impact on the electrical properties compared to the other dopants, forming resonant levels or creating hole carriers and leading to poor thermoelectric performance compared to the second- and third-row dopants. However, the reduction in the bandgap due obtained with these dopants may make the materials suitable for other applications such as photovoltaics or photocatalysis. Our modelling reveals the critical carrier concentrations and best B-site dopants for optimising the electrical properties of SrTiO3, and our predictions are supported by good agreement with available experimental data. The work therefore highlights avenues for maximising the thermoelectric properties of this archetypal oxide material.
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Research context
OpenAlex topics: Advanced Thermoelectric Materials and Devices · Thermal Expansion and Ionic Conductivity · Magnetic and transport properties of perovskites and related materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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