influence of titanium dioxide activated under visible light on survival of mold fungi
Clicks: 306
ID: 156571
2018
Background
In public and residential buildings, fungi are usually found in the dust or growing on building materials medium
such. It has been known that a number of their spores may contaminate the indoor environment and deteriorate air quality in
accommodation spaces. Previously designed air cleaning systems do not guarantee a complete removal of agents harmful to humans
and animals. Therefore, there is a great need to develop a new solution to remove molds from indoor air. In recent years,
photocatalysis based on titanium dioxide (TiO 2 ) has been proposed as an effective method for air pollutants removal. The aim of
the study was to determine the effect of TiO 2 activated under artificial sun light (UV-VIS – ultraviolet – visible spectroscopy) on
survival of fungi Penicillium chrysogenum and Aspergillus niger.
Material and Methods
The commercial P 25 (Aeroxide P 25, Evonik,
Germany) and nitrogen modified titanium dioxide (N-TiO 2 ) were used. The microbiological study was performed using Penicillium
chrysogenum and Aspergillus niger fungi. The survival of fungi was determined on the basis of changes in their concentration.
Results
It was found that N-TiO 2 has a stronger antifungal activity against P. chrysogenum and A. niger than P 25. For N-TiO2, the
complete elimination of molds was possible after 3 h under artificial solar light activation. The minimal concentration of photocatalyst
was 0.01 g×dm –3 ( P. chrysogenum ) and 0.1 g×dm –3 ( A. niger ).
Conclusions
The nitrogen modification of titanium dioxide produced
expected results and N-TiO 2 presented good antifungal activity. The findings of the presented investigation can lead to the development
of air filter to be used for removal of harmful agents (including molds) from indoor environment. Med Pr 2018;69(1):59–65
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Authors | ;Daria Kądziołka;Paulina Rokicka;Agata Markowska-Szczupak;Antoni W. Morawski |
Journal | journal of bioinformatics and computational biology |
Year | 2018 |
DOI | 10.13075/mp.5893.00652 |
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