Cyclone Separator for Air Particulate Matter Personal Monitoring: A Patent Review
Bibliographic record
Abstract
Currently, air pollution is a critical public health problem, which means that the daily measurement of urban air quality can be enriched if measured in a personalized way. Personal environmental monitoring devices can guide the population to take action. They can track their daily activities, avoiding situations that could affect their health, allowing them to precisely know the air quality they breathe in real-time in various microenvironments. In this work, we present a review of cyclonic separation technology patents, such as pre-separators in monitoring devices. We focused on the state-of-the-art commercially available personal monitoring devices, the classification of kinds of patents, and a review of cyclone patents and gas–particle separation behaviors. The World Intellectual Property Organization IP’s portal and Google Patents search engine were used, using international patent classification plus mesh terms involving a cyclone in an air particulate monitor after predefining inclusion and exclusion criteria such as gas–air cyclones, high efficiency, and fine particle separation. Twenty-nine patents were analyzed according to the main characteristics (e.g., cut point, flow rate, and cyclone improvement) available in the patent document. The wide range of cyclones indicates a maximum flow rate of between 0.5 and 4.5 Lpm and a lower cyclone cut point of 0.8 μm. This review includes a discussion of the most relevant features of the patent documents (flow rate, particle cut point, some cyclone improvements, and technology detection). This paper aims to give an overview of the use of cyclones as pre-separators for personal air monitoring devices and to acknowledge the patented improvements of new inventors or developers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".