Photochemical Degradation of Some Halogenated Anesthetics in Air
Bibliographic record
Abstract
Anesthetic gases enter the environment primarily through patient exhalation and venting from scavenging systems directly into the atmosphere. Emissions of halogenated anesthetic gases like halothane, isoflurane and sevoflurane are of concern due to their high global warming potential, highlighting the need to mitigate their environmental impact. Photocatalytic oxidation has been proposed as a potential option for emission control and indoor air treatment, but data on its use for various halogenated anesthetics is very limited. In this work, photocatalytic oxidation efficiency for the degradation of halothane was studied by varying the method for catalyst support and catalyst mass loading. Approximately 99.9% of halothane (1296 mg/m3) in air was degraded with a TiO2 photocatalyst under UVC light (254 nm) in 35 min in a recirculating batch photoreactor. The optimized conditions for halothane demonstrated a similar although faster photocatalytic degradation efficiency for isoflurane (99.8% in 20 min, 911 mg/m3) and sevoflurane (>98% in 10 min, 847 mg/m3). The results presented here suggest that a UV–photocatalysis is a promising technique to treat such anesthetic gases before being released into the environment by scavenging systems, although significant work remains to identify the potential by-products and optimal photoreactor designs for efficient long-term operation.
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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) | 0.001 | 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".