Effect of Preparation Method and Calcination Temperature on LaCoO3 Perovskite Catalyst for Diesel Soot Oxidation
Bibliographic record
Abstract
The present work attempts to scan the effect of preparation methods (co-precipitation, sol-gel and solution combustion synthesis) and calcination temperature (600-800 o C) on the efficiency of LaCoO3 catalysts for soot oxidation. All the catalysts were highly selective towards CO2 as no CO was detected in the flue gas. Catalyst produced following co-precipitation method revealed total soot oxidation at the lowest temperature (Tf = 370 o C) than other two catalysts prepared by sol-gel (Tf = 420 o C) and solution combustion synthesis (Tf = 456 o C) methods. Irrespective of the preparation methods, 750 o C was the optimum calcination temperature of the precursors resulting LaCoO3 catalysts which exhibited maximum activity for soot oxidation. In addition, the results showed that the specific surface areas of the catalysts decreased with increasing calcination temperature beyond 750 o C and consequently decreasing the activity of the catalyst.
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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) | 0.000 | 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".