Mechanistic model of p-type semiconducting hydrocarbon sensors

Title data

Sahner, Kathy ; Moos, Ralf:
Mechanistic model of p-type semiconducting hydrocarbon sensors.
2007
Event: International Conference on Electroceramics 2007 , 31.07.-03.08.2007 , Arusha, Tanzania.
(Conference item: Conference , Other Presentation type)

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Abstract in another language

The gas sensitive properties of a n-type semiconductor, eg. tin oxide, are usually attributed to a catalyzed oxidation between reducing gases and chemisorbed oxygen. Although this mechanism has been successfully studied in the literature for n-type semiconductors, it presented several deficiencies when transferred to p-type materials. In the present contribution, a novel mechanistic model for semiconducting hydrocarbon sensors is proposed. The p-type conducting perovskite family SrTi1-xFexO3-d; (STF) is chosen as a model substance. In general, p-type semiconductors at least partially exchange lattice oxygen when catalyzing oxidation of a reducing gas. If the semiconductor can accommodate a large oxygen non-stoichiometry d, the participation of lattice oxygen in reaction may be promoted. In the case of STF, the oxidation reaction very probably is not limited to adsorbed oxygen species, but involves the bulk of the semiconductor. Assuming that a reducing gas A attacks the semiconducting lattice consuming near-surface lattice oxygen, this would lead to the consumption of holes and the formation of near-surface oxygen vacancies. Assuming a fast bulk diffusion, which is valid for STF, bulk defect concentrations in presence of a reducing gas thus differ from their equilibrium values in pure air. In particular, hole concentration changes, which immediately affects conductivity of the material. This leads to a set of defect chemical equations, which are solved for steady-state conditions in order to express hole concentration as a function of the reducing gas concentration, which is then combined with a conventional diffusion-reaction model proposed for porous sensor devices, which describes the local concentration profile as a function of the film penetration depth. In the experimental section, numerical calculations were compared with experimental response of sensor samples towards a variety of test gases. For data fitting, the diffusion coefficient was estimated using microstructure data from SEM images. Kinetic data, i.e., the reaction constant, was determined in a separate measurement series. A very good correlation was observed for the basic sensor characteristics. Different methods for selectivity enhancement (variation of film thickness, use of nanosized specimens) were included in the model. The calculated results were validated by experimental data sets.