Determination of hydrogen peroxide on N95 masks after sanitization using a colorimetric method
Bibliographic record
Abstract
Hydrogen peroxide is commonly used as a sterilizing agent for medical devices and its use has recently been extended to N95 masks during PPE shortages as a result of the COVID-19 pandemic. The hydrogen peroxide remaining on the masks after sterilization could potentially pose a health hazard to the mask users. In the present study a colorimetric method was optimized for the determination of hydrogen peroxide on N95 masks following chemical sanitizations. The developed analytical method demonstrated an overall recovery of 98% ± 7%. The limit of detection ranged from 0.16 to 0.25 mg/mask, depending on the type of mask. The expanded measurement uncertainty was 13% (at a 95% confidence interval). The sanitization process itself introduced a significant variation in hydrogen peroxide load between masks. The ozone used in the sanitization process had no significant impact on analytical performance. Stamped and printed marks on the mask surfaces could induce biased readings. Hydrogen peroxide decomposes quickly on the mask surfaces so timing of analysis is an important factor in method standardization.•The validation data demonstrated that the in-house method is reliable and fit for the intended purpose, offering a sensitive, simple, rapid, and inexpensive method of residue monitoring.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".