Properties evolution of LaCoO3 Perovskite synthesized by reactive grinding – Application to the toluene oxidation reaction
Bibliographic record
Abstract
Perovskites are well known materials that are considered as viable alternative to noble metal-based catalysts in the environmental field. To be competitive, major issues such as unattractive textural properties and lower intrinsic activities than those of noble metal counterparts must be first addressed. On the other hand, reactive grinding is a versatile method to efficiently synthesize materials with improved textural properties, depending on the sequence and parameters. LaCoO3 perovskites were synthesized by a three-step reactive grinding top-down process: (i) solid-state reaction – SSR; (ii) high energy ball milling – HEBM; (iii) low energy ball milling – LEBM. The physicochemical properties evolution of perovskite materials over the reactive grinding process was investigated by XRD, N2-physisorption, ICP-OES, XPS, H2-TPR, O2-TPD and OIE, while their catalytic performances were evaluated for the toluene total oxidation reaction in dry and wet conditions. Each successive reactive grinding step allowed us to optimize the catalysts textural properties, with respectively the obtention of a microcrystalline material, a drastic reduction of crystal size to a nanometric scale along with formation of dense particles and then a significant increase of specific surface area up to 50 m2·g−1 by particles deagglomeration. The reactive grinding sequence presented here also deeply impacted the redox properties of LaCoO3 catalysts, leading to increased performances in the toluene total oxidation reaction (SSR < HEBM < LEBM). A special care was paid to the impact of Fe contamination over grinding steps and time, on redox and catalytic properties of LaCoO3 samples.
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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) | 0.000 | 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".