Tio2 Photocatalytic Biocidal Activity on Escherichia Coli and On Aspergillus Niger under Different Methodological Conditions
Bibliographic record
Abstract
Biological pollution is one major cause of the degradation of indoor air quality.It was shown that microbial communities from outdoor might impact significantly the communities detected indoor.In addition, microbial contamination of the surfaces of building materials and their release into the indoor air also significantly affect indoor air quality.Preventing the growth or at least reducing the amounts of microorganisms growing on indoor building materials is essential for reducing health risks for building occupiers.Photoactive TiO2 has been widely studied as a photocatalyst that enable the inactivation of various bacterial strains.In this paper, we compare the antifungal activity of nanoparticles of TiO2 on Aspergillus niger spores and its antibacterial activity on Escherichia coli under low light irradiation, near to common indoor values.The antimicrobial activity of TiO2, expressed as log reduction, was assessed under UV irradiation in a sludge mixture of sterile water, suspension and nanoparticles of TiO2.The results showed a strong bactericidal activity of TiO2 on E. coli and a weak fungicidal activity against A. niger.Different parameters including concentration of TiO2, intensity of light, and duration of contact between TiO2 and microbial cells and spores, were investigated and significantly affected the antibacterial activity of TiO2 while poorly affected its antifungal activity.Results of this study confirmed previous investigations on antibacterial activity of TiO2 on E. coli and bring new insight on antifungal activity on the spores of A. niger.The effectiveness of the antimicrobial activity is enhanced by the duration of contact between suspension and TiO2 nanoparticles through the stirring experiments for 2H, 4H and 24H.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".