Removal of fine and ultrafine particles by means of a condensational growth assisted bubble column
Bibliographic record
Abstract
The paper shows experimental findings aimed to prove the effectiveness of a concept design for fine and ultrafine particle capture called condensational growth assisted bubble column. Experiments were carried out with a gas at ambient temperature and pressure polluted with calibrated polystyrene nanoparticles (mean numeric diameter 113 nm, standard deviation 120 nm). The laboratory scale equipment included the sequence of a growth tube and a bubble column. In the growth tube, the heterogeneous condensation of water vapour took place over the particles, producing a liquid‐solid aerosol of size larger than the original particles. Experiments showed that the condensational growth pre‐treatment improved the bubble column removal efficiency from nearly 25 % up to 90 %. The growth tube also contributed to particle capture so that the overall system reached particle removal efficiency above 95 %. The particle removal efficiency of the entire unit was higher than the sum of the single growth tube and bubble column contributions, suggesting the occurrence of favourable synergic effect between them.
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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) | 0.000 | 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 | 0.001 | 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".