Hydrocracking of Heavy Oil by Means of In Situ Prepared Ultradispersed Nickel Nanocatalyst
Bibliographic record
Abstract
This work adopts a water-in-oil (w/o) microemulsion method for the in situ preparation of ultradispersed metallic nickel (Ni 0 ) nanocatalyst in heavy oil and assesses its hydrocracking activity. Catalyst preparation involved reducing Ni 2+ added to the water pools of the heavy oil matrix in the form of aqueous Ni(NO 3 ) 2 solution using hydrazine. The volume of the aqueous precursors was limited to values which corresponded to visually stable single heavy oil phase. The product particles were collected by addition of toluene and characterized using XRD, TEM, and EDX. These techniques confirmed the formation of nickel nanoparticles of 22 ± 5 nm mean diameter. The hydrocracking activity of the as-prepared ultradispersed catalyst was evaluated using a semibatch reactor setup under 110 bar of hydrogen and 370 °C. Although no presulfiding was performed, XRD of the spent catalyst confirmed the formation of Ni 3 S 2 nanoparticles with a mean particle size of the same range as the Ni 0 particles. Results showed 2-fold improvement in the gaseous fractions, around 47% conversion of the residue, more than 70% reduction in the resins, around 50% reduction in the asphaltenes and an increase in aromatics and saturates in the presence of the ultradispersed catalyst.
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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".