Removal of Carbon Nanoparticle Using NO<sub>2</sub> Oxidation Technique
Bibliographic record
Abstract
In diesel engine, generally, the removal of carbon nanoparticles has been based on a filtration system or oxidation reactor with O2 at high temperatures of above 800 °C. Recently, NO2 has been found to be a more efficient oxidant than O2 at lower temperatures in the range of 200~500 °C. Small amounts of NO2 in the range of a few hundreds of ppm by volume can promote the continuous oxidation of carbon particulates. Thus far, experiments involving diesel PM (particulate matter) oxidation by NO2 are only practiced as regards the soot deposited on filters or plates. However, in aerosol state, depending on the surrounding temperature and the NO2 concentration, the carbon nanoparticle removal rate is significantly different. Therefore, the study of nano-sized carbon aerosol oxidation in various gas circumstances is required. In this study, the oxidation characteristics of nano-sized carbon aerosol particles in NO2 condition are investigated.
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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 | 0.000 | 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".