Novel Carbon-supported Transition Metal Phosphide Catalysts for Hydrodeoxygenation of Fast Pyrolysis Oil
Bibliographic record
Abstract
Catalytic hydrodeoxygenation (HDO) is considered to be the most promising route to upgrade pyrolysis oil to liquid transportation fuels. The objective of this research was to explore inexpensive supported metal oxides/metal phosphide catalysts for the upgrading of pyrolysis oil into advanced drop-in hydrocarbon liquid fuels by HDO.\nThe first stage of this thesis project investigates a series of molybdenum oxides/phosphide catalysts on different supports that were prepared in-house, such as Al2O3, activated carbon (AC), MgAl2O4 and Mg6Al2(CO3)(OH)16. The HDO activity of these catalysts were investigated using a 100 mL bench-scale reactor operating at 300 °C with an initial hydrogen pressure of 50 bar for 3 h. The catalytic efficiency were compared with a commercially available Ru/C catalyst, and the most active catalyst was selected, i.e., MoP/AC. Two other transition metal phosphides Nickel and Cobalt were also prepared in the same manner using AC support, and their catalytic activities for HDO of pyrolysis oil were compared. The activities of the three catalysts were found to be in the order of NiP/AC > CoP/AC > MoP/AC.\nThe second stage of the project aimed to optimize the addition of phosphorous. A series of supported nickel and cobalt phosphide catalysts with different metal/ phosphorous (M/P) ratios (mol/mol) were prepared and their catalytic activities studied for HDO of pyrolysis oil. Addition of phosphorous to the both Nikel and Cobalt at certain M/P ratios greatly influenced H2 consumption, oil yield, the degree of deoxygenation and HHV values of the upgraded bio-oils. The fresh/spent catalysts were characterized by BET, XPS, TEM and XRD, and the upgraded bio-oils were characterized using Karl Fischer titration, GPC, elemental analysis and GC/MS.
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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".