Experimental Investigation of Metal-Based Calixarenes as Dispersed Catalyst Precursors for Heavy Oil Hydrocracking
Bibliographic record
Abstract
Slurry-phase hydrocracking utilizing metal-containing oil-soluble compounds as precursors of dispersed catalysts is an effective approach for heavy oil upgrading. We propose applying metal-based p-tert-butylcalix[6]arene (TBC[6]s) organic species as dispersed catalyst precursors to enhance catalytic hydrogenation reactions involved in the upgrading of vacuum gas oil (VGO). Co- and Ni-based TBC[6]s were synthesized and characterized by SEM-EDX, ICP, XRD, and FT-IR. The thermogravimetric and calorimetric behaviors of the synthesized complexes, which are key properties of dispersed hydrocracking catalysts, were also explored. The experimental evaluation of the synthesized catalyst precursors show that the synthesized metal-based TBC[6] catalyst precursors improved the catalytic hydrogenation reactions. A co-catalytic system was also investigated by adding a commercial, first-stage hydrocracking supported catalyst in addition to the dispersed catalysts. The naphtha yields increased from 10.7 wt.% for the supported catalyst to 11.7 wt.% and 12 wt.% after adding it along with Ni-TBC[6] and Co-TBC[6], respectively. Mixing the metal-based precursors resulted in elevated yields of liquid products due to the in situ generation of highly active Co–Ni bimetallic dispersed catalysts.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".