Effects of the Preparation Variables on the Synthesis of Nanocatalyst for in Situ Upgrading Applications
Bibliographic record
Abstract
In situ upgrading technology (ISUT) is a patented process based on the use of hydroprocessing with ultradispersed catalyst (UDC) for in-reservoir upgrading of heavy oil and bitumen to reach transportability specifications. The properties of the UDC play a crucial role in the performance of the process. The focus of this work was to study the effects of different operating variables during the catalyst synthesis (such as type of mixer, stirring speed, sulfiding agent, and catalyst formulation) on the particle size in a customized experimental setup built for this purpose. Additionally, the effect of the sulfiding agents on the composition of the active phases present in the catalyst surface was investigated. It was possible to synthesize catalysts with nanometric dimensions, and the variables with significant effects on the particle size were found to be the type of mixer and the sulfiding agent. Nanometric scale was reached using ammonium sulfide and high-shear mixing. Moreover, advantages to the use of thiourea as the sulfur source during the catalyst sulfiding stage were observed. Finally, a NiMoS phase was observed for preparations with both sulfiding agents (i.e., ammonium sulfide and thiourea).
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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) | 0.001 | 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".