Comparison of MoS<sub>2</sub> Catalysts Prepared from Mo-Micelle and Mo-Octoate Precursors for Hydroconversion of Cold Lake Vacuum Residue: Catalyst Activity, Coke Properties and Catalyst Recycle
Bibliographic record
Abstract
The hydroconversion of cold lake vacuum residue (CLVR) in a semibatch, slurry-phase reactor was studied at 415–445 °C, 13.8 MPa, and a reaction time of 1 h, using MoS 2 catalysts prepared from Mo-octoate and Mo-micelle precursors. Both precursors yielded MoS 2 with the same activity in terms of coke suppression, residue conversion, and hydrogen uptake. The coke yield decreased from 22 wt % in the absence of a catalyst, to 4.8 wt % in the presence of 100 ppm Mo. A Mo concentration of 600 ppm was found to be optimum in terms of maximizing the residue conversion (84 wt %) and minimizing the coke yield (2.9 wt %). The characteristics of the recovered coke as a function of catalyst concentration and age in the reactor were also investigated. At high catalyst concentrations (>600 ppm Mo) and short reaction times, the generated coke was relatively amorphous, with a high H/C ratio. The solid catalyst–coke recovered from the reactor was recycled without further treatment. The catalytic activity of the recycled catalyst was the same as the fresh catalyst, and no catalyst deactivation was observed under the hydroconversion conditions of the present study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".