Modelling the capture of gasoline engine exhaust particulate matter in three-way catalytic converters
Bibliographic record
Abstract
The aim of this work was to numerically model the effect of a three-way catalytic converter on nano-scale particulate matter from a gasoline direct injection engine. The work used a deep bed filtration model to simulate and validate experimental findings. A 1.6L, gasoline direct injection, spark ignition, turbocharged and intercooled Euro IV engine was used for experimentation. The capture efficiency was experimentally determined by measuring pre and post-catalyst particulate matter numbers using a DMS-500 differential mobility spectrometer at different dilution ratio settings. Despite variable experimental results at lower engine speeds and loads, the model was capable of predicting catalytic converter particle capture efficiency in the majority of cases. This indicates that deep bed filtration theory can indeed be used to explain nano-scale particle capture within three-way catalytic converters. The results also suggest that there is no significant agglomeration of particles within the catalytic converter. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".