Evaluation of Personal Protective Equipment Using Low-cost Aerosol Monitors
Bibliographic record
Abstract
Rapid, low-cost evaluation of personal protective equipment (PPE) is important for providing widespread and easy-to-access testing of aerosols through and round masks and shields. Most related literature has focused on how well PPE protects the wearer, not reducing aerosol transmission to the environment. Few studies have compared the efficacy for particle escape at exhalation or inhalation of face masks and face shields. Measurements of particulate matter escaping through PPE could provide information regarding the efficacy on the wearer and on the surrounding of the PPE and guide the selection of appropriate PPE to wear in different conditions. Research grade particle technology devices are not widely available. Low-cost options which are simple to use may provide a practical alternative. In this study, we measured particulate matter with a diameter less than 2.5 µm (PM2.5) emitted by a manikin placed upright at the head of a stretcher. Measurements were made using three low-cost sensors and an Optical Particle Sizer (OPS) at distances of 2, 4, and 6 feet repeated at 0, 45, and 90°, given at a horizontal plane, with respect to the mannikin. The low-cost sensors correlated well with the OPS used as a reference method and may provide a simple, low-cost, widely available alternative.
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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 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".