Determination of hydrogen peroxide on N95 masks after sanitization using a colorimetric method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it