Catalytic Hydrodeoxygenation of Bio-Crude and Heavy Gas Oil Blends Using Carbon-Supported Molybdenum Catalysts
Bibliographic record
Abstract
The present study focused on decreasing the amount of oxygen present in hydrothermal liquefaction (HTL) biocrude via catalytic hydrodeoxygenation. To serve the purpose, carbon-supported molybdenum carbide catalysts were synthesized via carbothermal hydrogen reduction method using three different carbon supports, commercial activated carbon (AC), commercial multi-walled carbon nanotubes, and bioresidue (BR) obtained via solvent-extraction from a HTL product mixture. The catalysts were screened for their oxygen reduction efficiency using a blend of HTL biocrude in hydrotreated heavy gas oil. The BR-based catalyst was identified as the best-performing catalyst at the screening conditions because it exhibited a higher oxygen reduction percentage (49.2 wt %) than the catalysts synthesized using carbon nanotubes (21.5 wt %) and AC (22.4 wt %). The synthesized catalysts were characterized in order to explain their oxygen reduction percentages, and a parametric study was carried out for the best-performing catalyst to determine the effects of process parameters such as temperature, pressure, reaction time, and catalyst loading on oxygen reduction efficiency. The characterization results revealed that the BR-supported molybdenum catalyst had the highest number of strongly acidic sites, the highest concentration of β-Mo 2 C on its surface, a molybdenum dispersion of 2.4 wt %, a BET surface area of 118 m 2 /g, and an average pore size of 9.7 nm. The oxygen reduction percentage for the BR-based catalyst improved and reached the maximum value of 59.8% for a reaction that was carried out at 325 °C and 5 MPa for 2 h with a catalyst loading of 4% w/w.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".