Guaiacol <scp>HDO</scp> on La‐modified Pt/ <scp> Al <sub>2</sub> O <sub>3</sub> </scp> : Influence of rare‐earth loading
Bibliographic record
Abstract
Abstract The stability of the catalyst used in hydrodeoxygenation (HDO) of biomass‐derived oils needs improvement. La has been applied in delaying Al 2 O 3 phase‐change under reaction conditions. Lanthanum (0.5–8 wt.%)‐γ‐alumina was studied as Pt (1 wt.%) carrier aimed at guaiacol (GUA) HDO. Materials characterization included N 2 physisorption, X‐ray diffraction (XRD), thermal analysis, FTIR, UV–vis, and TPR. Solids pore size (~8–10 nm) was suitable for GUA (kinetic diameter~0.668 nm) hydrotreating. Mixed carriers were amorphous (XRD), suggesting well‐dispersed La domains; meanwhile, carbonates/bicarbonates were formed (from CO 2 ) due to the basic surface properties of modified supports (FTIR). That could impart catalyst stability by inhibiting coking through the passivation of Lewis acidity on Al 2 O 3 . Pt reducibility increased with La loading in various formulations. However, that was not reflected in enhanced GUA HDO (T = 488 K and P = 3.2 MPa, batch reactor), presumably due to the strong metal–support interaction (SMSI), where LaO x covered the metallic Pt particle surface. GUA HDO on various catalysts was approximated by pseudo‐first‐order kinetics (integral regime, k ), where deviations were observed as La loading increased, presumably by an SMSI state that could affect the rate‐determining step of the reaction mechanism. Basic sites provided by rare‐earth could contribute to altering HDO reaction pathways as well. At 1 wt.% rare‐earth, GUA HDO was maximized ( k ~25% higher than that on Pt/Al 2 O 3 ), with that material also exhibiting similar deoxygenation (85%–90% at total GUA conversion) to the latter Pt over pristine alumina. Conversely, both parameters significantly diminished over the catalyst of the highest La content. Materials at low rare‐earth concentrations deserve further studies focused on catalyst stability under HDO conditions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".