Synthesis and Characterization of NiMo Catalysts Supported on Fine Carbon Particles for Hydrotreating: Effects of Metal Loadings in Catalyst Formulation
Bibliographic record
Abstract
The by-products collected during the synthesis of carbon nanohorns via the arc discharge synthesis method is comprised of other carbon particles (OCP). At a hydrotreating operating temperature of 370°C, preliminary investigations using a bimetallic catalyst with support originating from the fine fractions of other carbon particles (OCP f ) and containing 13 wt% Mo and 2.5 wt% Ni resulted in an HDS and HDN conversion of 78 and 25%, respectively. Variation of metal compositions in catalyst formulation and its impact on hydrotreating activity was therefore considered in this study to enhance the hydrotreating activity of OCP f –supported catalyst, and to determine if the best NiMo/OCP f catalyst achieved from this study could be a viable catalyst for hydrotreating applications. The co-incipient wetness impregnation was used in preparing series of hydrotreating catalysts with Ni and Mo loadings within the range of (2.5–5.0 wt%) and (13–26 wt%) respectively. Overall, the catalyst samples with maximum Ni loading of 5.0 wt% and Mo loadings of either 13 or 19 wt% showed higher dispersion and the ability to form a Type II Ni-Mo-S phase with enhanced activity. The effects of metal compositions on both HDS and HDN activities were correlated with their physicochemical properties.
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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) | 0.000 | 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".