Towards the World’s Smallest Gravimetric Particulate Matter Sensor: A Miniaturized Virtual Impactor with a Folded Design
Bibliographic record
Abstract
The increasing air pollution across the globe has given rise to a global health crisis that is increasing at an alarming rate. Every year, millions of people lose their lives due to health risks caused by air pollutants. Hence, there is a pressing need for better solutions to accurately measure the amount of air pollution. This work is aimed at designing a highly compact, accurate, low-cost, self-resettable, and easy-to-use gravimetric-based particulate matter sensor solution for portable applications. Previous attempts have failed to realize true miniaturization, due to the size constraints of the virtual impactor needed—a mechanism that segregates the particulate matters based on their sizes. Our complete particulate matter sensor solution consists of three components (i) a piezoelectric resonating membrane, (ii) a virtual impactor, and (iii) a thermophoretic mechanism to reset the sensor. This paper presents a novel design of the virtual impactor, based on a folded configuration. This helps realize the entire system in a volume of 20 mm × 20 mm × 10 mm. We report here the design, working principles, fabrication, and experimental results of the virtual impactor.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".