Photocatalytic Inactivation of Bioaerosols by TiO <sub>2</sub> Coated Membrane
Bibliographic record
Abstract
Indoor air pollution by microbial contaminants is increasingly receiving attention as a public health problem. Under a suitable environment, such as in heating, ventilation and air conditioning (HVAC) system, airborne bacteria are able to proliferate and grow causing various allergies and illnesses. This can be particularly serious in tropical regions due to high relative humidity and warm temperatures all round the year. Application of photocatalysis using UV-A and TiO2 to inactivate air-borne bacteria is relatively new and systematic parametric study is required for the engineering design of a process based on this technology. This study investigates the effects of TiO2 mediated inactivation of various bacterial species in batch and continuous systems using different TiO2 loadings and radiation intensities. Gram-negative bacteria, E. coli and two Gram-positive bacteria, Microbacterium sp. and Bacillus subtilis were used for the inactivation studies. In both systems, inactivation rates of Gram-negative E. coli are higher than the Gram-positive Bacillus subtilis and Microbacterium sp. and the inactivation rates increased in presence of TiO2 for all bacteria. Depending on the type of bacteria, TiO2 loading and light intensity, an increase of 1.3-5.8 times in the inactivation rates was obtained from those in the absence of TiO2. The inactivation rates in the batch and continuous systems were reasonably comparable. Inactivation rates in the continuous system are somewhat higher than those in the batch system due to the unaccounted loss of bacteria via adsorption and settling on the reactor walls in the flow system. The study demonstrates an approach that can be used for the designing of large scale systems for the treatment of bioaerosol.
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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) | 0.000 | 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".