TPO/TPD study on the activation of silica supported cobalt catalyst
Bibliographic record
Abstract
Silica supported cobalt catalysts have been prepared using urea containing cobalt nitrate solution impregnation method, which can give high loading Co 3 O 4 with relatively smaller crystallite size. Compared to the conventional impregnation method, the catalyst prepared with urea added to the cobalt nitrate solution provides higher activity for CO conversion and C 5 + selectivity. To optimize the catalyst activation and simulate the industrial activation, TPO–TPR cycle treatment with or without steam has been applied to the prepared catalysts under various conditions. It is shown that reduction of pure hydrogen with high flow rate leads to a sharp temperature rise in the catalyst bed, which results in the sintering of the cobalt particles. A slow temperature rise with dilute hydrogen helps stabilize the cobalt particles, and the cycle treatment of the catalyst using TPO–TPR without steam induction has little effect on the size of cobalt particles. When steam is included in the reduction stream with hydrogen the TPR–TPO cycle treatment can help increase the Co dispersion, which increases the catalyst activity and selectivity to C 5 + .
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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 | 0.000 |
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".