Morphology and deactivation behaviour of Co–Ru/γ‐Al<sub>2</sub>O<sub>3</sub> Fischer–Tropsch synthesis catalyst
Bibliographic record
Abstract
Abstract Deactivation of Co–Ru/γ‐Al2O3 Fischer–Tropsch (FT) synthesis catalyst along the catalytic bed over 850 h of time‐on‐stream (TOS) was investigated. Catalytic bed was divided into four parts and structural changes of the spent catalysts collected from each catalytic bed after FT synthesis were studied using BET, ICP, XRD, TPR, carbon determination, H2 chemisorption and oxygen titration techniques. Rapid deactivation was observed during first 200 h of FT synthesis. In this case, the deactivation rate was not dependent on the number of the catalyst active sites. It was zero order to CO conversion and independent of the size of active sites. Beyond the TOS of 200 h, the deactivation could be simulated with a power law expression: $- {\rm d}X_{{\rm CO}} /{\rm d}t = kX_{{\rm CO}}^{28.5}$ . The physical properties of the catalyst charged in 1st half of the reactor did not change significantly. Interaction of cobalt with alumina and formation of mixed oxides of the form xCoO·yAl2O3 and CoAl2O4 was increased along the catalytic bed. Percentage reducibility and dispersion decreased by 2.4–25.5% and 0.5–8.8% for the catalyst in the beds 1 and 4, respectively. Particle diameter increased by 0.8–6.1% for the catalyst in the beds 1 and 4 respectively suggesting higher rate of sintering at last catalytic bed. The amount of coke formation in the 4th catalytic bed was 6 times more than that of in bed 1.
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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) | 0.001 | 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".