An Overview of Airborne Nanoparticle Filtration and Thermal Rebound Theory
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
This paper provides an overview of recent studies on the filtration of airborne nanoparticles. Classical filtration theory assumes that the efficiency of nanoparticle adhesion is at unity when nanoparticles strike a filter with a Brownian motion. However, it has been pointed out that small nanoparticles may have a sufficiently high impact velocity to rebound from the surface upon collision, a mechanism called thermal rebound. According to thermal rebound theory, the adhesion efficiency of nanoparticles decreases if their size is reduced. However, this phenomenon has not yet been clearly observed in experimental studies; there are still a number of uncertainties associated with the concept of thermal rebound, which is yet to be either proven or disproven. This review paper discusses the findings in the current literature related to thermal rebound theory.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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