Investigation of Co3O4 and LaCoO3 Interaction by Performing N2O Decomposition Tests under Co3O4-CoO Transition Temperature
Bibliographic record
Abstract
The research presented in this paper addresses the question: How does the addition of a small amount of LaCoO3 impact the activity of a Co3O4 catalyst? By testing such a catalyst in N2O decomposition under conditions at which the thermal decomposition of Co3O4 to CoO is possible, one gains unique insight into how the two phases interact. The activity of such a catalyst increases in the entire studied temperature range, unlike the activity of the undoped cobalt catalyst which is lower at 850 °C than at 800 °C due to the reduction of Co3O4 to CoO. XRD measurements showed that CoO was also the main cobalt oxide present in the Co3.5La catalyst after operating at 850 °C, as did the XPS measurements, but there was no drop of activity associated with this change. The influence of NO, O2 and H2O on the activity of the new catalyst, Co3.5La, was determined. Lack of positive effect of NO, a known oxygen scavenger, on the activity was noticed at all temperatures, showing that the effect of LaCoO3 is probably due to increased oxygen desorption. Temperature programed oxidation (TPO) tests showed that the beneficial effects of the presence of LaCoO3 on the activity of cobalt oxide at 850 °C were probably caused by enhanced diffusion of O2− anions through the entire catalyst, which facilitates desorption of oxygen molecules from the surface.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".