Separation Enhancement of Mechanical Filters by Adding Negative Air Ions
Bibliographic record
Abstract
The purpose of this work is to combine negative air ions (NAIs) and mechanical filters for removal of indoor suspended particulates. Various factors, including aerosol size (0.05-0.45 μm), face velocity (10 and 20 cm/s), species of aerosol (potassium chloride and dioctyl phthalate), relative humidity (30% and 70%), and concentrations of NAIs (2 ´ 104, 1 ´ 105, and 2 ´ 105 NAIs/cm3) were considered to evaluate their effects on the aerosol collection characteristics of filters. Results show that the aerosol penetration through the mechanical filter is higher than that through the mechanical filters cooperated with NAIs. This finding implies that the aerosol removal efficiency of mechanical filters can be improved by NAIs. Furthermore, the aerosol penetration through the mechanical filters increased with the aerosol size when NAIs were added. That is due to that the aerosol is easier to be charged when its size gets larger. The results also indicate the aerosol penetration decreased with the NAIs concentration increased. Reversely, aerosol penetration through the mechanical filters increased with the face velocity under the influence of NAIs. The aerosol penetration through the filter with NAIs was no affected with relative humidity. Finally, The penetration through the filter with NAIs against solid aerosol was lower than that against liquid aerosol.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".