Performance of a NOX Adsorber and Catalyzed Particle Filter System on a Light-Duty Diesel Vehicle
Bibliographic record
Abstract
A prototype emissions control system consisting of a close-coupled lightoff catalyst, catalyzed diesel particle filter (CDPF), and a NOX adsorber was evaluated on a Mercedes A170 CDI. This laboratory experiment aimed to determine whether the benefits of these technologies could be utilized simultaneously to allow a light-duty diesel vehicle to achieve levels called out by U.S. Tier 2 emissions legislation. This research was carried out by driving the A170 through the U.S. Federal Test Procedure (FTP), US06, and highway fuel economy test (HFET) dynamometer driving schedules. The vehicle was fueled with a 3-ppm ultra-low sulfur fuel. Regeneration of the NOX adsorber/CDPF system was accomplished by using a laboratory in-pipe synthesis gas injection system to simulate the capabilities of advanced engine controls to produce suitable exhaust conditions. The results show that these technologies can be combined to provide high pollutant reduction efficiencies in excess of 90% for NOX and PM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.002 | 0.001 |
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".