Evaluation of diesel fuel production from bio-oils hydrodeoxygenation using unsupported MoS2 catalysts
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Bibliographic record
Abstract
Diesel production from lignocellulosic biomass-derived fast pyrolysis bio-oil (FPBO) and catalytic pyrolysis bio-oil (CPBO) was investigated with an upgrading approach using unsupported MoS 2 catalysts generated in situ. Hydrodeoxygenation of FPBO and CPBO was evaluated in a continuous-flow reactor system using feed blends containing 18 wt% bio-oil in fuel oil. For FPBO, 92.9 % deoxygenation was achieved with 0.51 wt% O in oil products, resulting in low acidity (0.32 mg KOH/g), while 74.8 % deoxygenation was obtained for CPBO with 1.24 wt% O and 0.48 mg KOH/g acidity in oil products. The lower deoxygenation of CPBO suggests that oxygenates in CPBO are less reactive than those in FPBO. In both cases, low solid yields were observed from 1.2 to 2.0 g/100 g bio-oil. XRD and HRTEM detected few-layer stacked structure for the in-situ formed MoS 2 catalysts. The oil product from CPBO retained more biogenic carbon than from FPBO, with the diesel fraction from CPBO exhibiting a higher biogenic carbon content and yield. Both diesel cuts meet almost all ASTM D975 specifications, except for S content, resulting from the high S/Mo feed ratio used in the tests. Evaluation results demonstrated great potential for producing specifications-conforming diesel fractions from FPBO and CPBO upgrading using unsupported MoS 2 catalyst. • Unsupported MoS 2 is effective in HDO of both FPBO and CPBO evidenced by O removal. • Solid yields from FPBO and CPBO upgrading were low, from 1.2 to 2.0 g/100 g bio-oil. • Few-layer stacked structure of MoS 2 formed in situ was influenced by the feedstock. • FPBO upgrading exhibited higher DOD; more biocarbon retention from CPBO upgrading. • FPBO and CPBO-derived diesels meet most ASTM D975 specs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 it