Login / Signup

Photocatalytic degradation of gaseous pollutants on nanostructured TiO 2 films of various thickness and surface area.

Michal BaudysEleonore BerthetJan M MacakMiloslav LhotkaJosef Krýsa
Published in: Photochemical & photobiological sciences : Official journal of the European Photochemistry Association and the European Society for Photobiology (2023)
This work deals with the preparation of TiO 2 nanoparticulate layers of various mass (0.05 mg/cm 2 to 2 mg/cm 2 ) from three commercial nanopowder materials, P90, P25 and CG 300, their characterisation (profilometry, BET and SEM) and evaluation of their photocatalytic activity in the gaseous phase in a flow-through photoreactor according to the ISO standard (ISO 22197-2). Hexane was chosen as a single model pollutant and a mixture of four compounds, namely acetaldehyde, acetone, heptane and toluene was used for the evaluation of the efficiency of simultaneous removal of several pollutants. A linear dependence between the layer mass and the layer thickness for all materials was found. Up to a layer mass 0.5 mg/cm 2 , the immobilisation P90 and P25 powder did not result in a decrease in BET surface area, whereas with an increase in layer mass to 1 mg/cm 2 , a decrease of the BET surface was observed, being more significant in the case of P90. The photocatalytic conversion of hexane was comparable for all immobilised powders up to a layer mass of 0.5 mg/cm 2 . For higher layer mass, the photocatalytic conversion of hexane on P25 and P90 differ; the latter achieved about 30% higher conversion. In the case of the simultaneous degradation of four compounds, acetaldehyde was degraded best, followed by acetone and toluene; the least degraded compound was heptane. The measurement of released CO 2 revealed that 90% of degraded hexane was mineralised to CO 2 and water while for a mixture of 4 VOCs, the level of mineralisation was 83%.
Keyphrases
  • visible light
  • reduced graphene oxide
  • highly efficient
  • optical coherence tomography
  • heavy metals
  • high resolution
  • neural network