Bifunctional hydrogen buffer catalytic system forenhanced hydrodeoxygenation of guaiacol under mild conditions
Bibliographic record
Abstract
Developing stable, high-performance catalysts for hydrodeoxygenation (HDO) reactions under mild conditions is crucial for advancing biomass conversion into high-value chemicals. This study introduces a bifunctional catalytic system consisting of a glucose-modified Ni-MOF-74-derived carbon catalyst (Ni/C-G) and silicotungstic acid (SiW 12 ) as a hydrogen buffer and carrier. Compared to phosphotungstic acid (PWA) and phosphomolybdic acid (PMO), SiW 12 exhibits superior performance, attributed to its reversible redox cycle, stability, and efficient hydrogen transfer capacity. The glucose modification of Ni-MOF-74 enhances the dispersion of nickel nanoparticles (Ni NPs), reduces agglomeration, enhances the hydrogen spillover, and introduces additional porosity while forming protective carbon layers (∼3.072 nm thick) around the active Ni NPs. These layers stabilize the catalyst in aqueous environments, enabling consistent catalytic performance over five cycles without activity loss. The system achieved a guaiacol HDO conversion of 74.93 % and cyclohexanol selectivity of 66.03 % under mild reaction conditions (95 °C, 1 MPa H 2 , 1 h). The key reaction pathways for SiW 12 -induced HDO, including demethoxylation and aromatic ring hydrogenation, facilitate selective cyclohexanol production. The synergistic interaction between Ni/C-G and SiW 12 boosts hydrogen transfer and enhances the catalyst’s stability, making this system a highly effective solution for upgrading bio-oil and enabling sustainable biomass conversion under mild conditions.
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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".