NH3 exhaust gas fuel reforming tor diesel engine decarbonisation & lean NOx abatement over Silver/Alumina catalyst
Bibliographic record
Abstract
The thesis is focusing on the potential roles and applications of NH\(_3\) in transportation area, where ammonia is applied i) as a hydrogen carrier involved in a catalytic reforming process tor H\(_2\) production, ii) in its reformed form i.e. H\(_2\) -NH\(_3\) mixture for improved NH\(_3\) combustion in CI engines (as a carbon-free energy carrier), and iii) as a reductant in engine emission abatement under the incorporation of the NH\(_3\) reforming mechanism and catalytic aftertreatment systems. To implement the above studies, a prototype reformer system and a catalytic reaction mechanism were designed and proved capable of producing Hz - contended reformate. The reformed NH\(_3\) i.e. H\(_2\) - NH\(_3\) mixture was later applied in diesel operation and demonstrated successful engine decarbonisation. With a further incorporation of a Silver/Alumina catalyst in the engine exhaust system, the reformate was revealed as viable reductant for low temperature NOx abatement. Therefore, a combination of the studied systems show a great potential in simultaneous diesel engine emissions reductions, which include CO\(_2\), CO, HC, PM and NOx.
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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) | 0.003 | 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".