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Dye-sensitised nanostructured TiO2 for high sensitivity photodetectors

Freeman, Justin (2018) Dye-sensitised nanostructured TiO2 for high sensitivity photodetectors. Honours thesis, Murdoch University.

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We have developed a proof-of-concept high gain spectrally selective photodetector composed of dye sensitised nanostructured TiO2 film with silver electrodes, and used phototransient methods for measuring electron mobility, charge separation efficiency and recombination. The measured electron mobility of our nanostructured TiO2 was 10-4 cm2/Vs – two orders of magnitude lower than published values, and charge carrier separation efficiency was 0.1%. Despite these low values, we obtained photoconductive gain of 10 and measured a bimolecular recombination rate (β) 106 – 108 times lower than the Langevin-type rate (βL) – even less than crystalline silicon with βL/β = 106. These values indicate that mobility and charge separation efficiency were the limiting factors in the photoconductive gain of our device. We have identified simple methods, such as an improved TiO2 sintering regime, and dye solution additives that could improve mobility and charge separation efficiency and achieve ultrahigh gains ~105.

The FWHM of our narrowband cyanine sensitiser was measured 37 nm in methanol solution; however, the spectral response on TiO2 was ~180 nm FWHM. We account for this discrepancy by dye aggregation which is known to exhibit red and blue shifting of the absorption spectrum due to exciton coupling. We have identified materials such as dye solution additives and co-adsorbants which are known to minimise aggregation and could improve spectral selectivity in our device.

Our innovative light sensing device opens the way for a new family of high-sensitivity photodetectors utilising dye-sensitised nanostructured semiconductors composed of cheap, solution processable and environmentally friendly materials. Furthermore, we demonstrated that spectrally selective high-gain photoconductors with FWHM <100 nm – a requirement for full colour detection and machine vision – are achievable.

Item Type: Thesis (Honours)
Murdoch Affiliation(s): School of Engineering and Information Technology
Supervisor(s): Pivrikas, Almantas
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