Selective C–C Bond Cleavage in Diols and Lignin Models: High-Throughput Screening of Metal Oxide-Anchored Vanadium in Mesoporous Silica
Bibliographic record
Abstract
The development of selective and robust heterogeneous oxidation catalysts is an enabling technology for conversion of biomass-derived platform chemicals. Vanadium active sites were incorporated into the structure of mesoporous silica via an ultra-fast, one-pot synthesis method based on microwave-assisted heating. In addition, Al/Ti/Zr/Ce anchoring ions were introduced in order to minimize vanadium leaching and better control its dispersion. The supported V-(Al/Ti/Zr/Ce)-MCM-41 composite materials were assessed as catalysts for aerobic C–C bond cleavage of simple models for lignin (1,2-diphenyl-2-methoxyethanol) and sugar-derived polyalcohols (meso-hydrobenzoin). The TiIV ions proved to be the best anchors to prevent V leaching, while AlIII and ZrIV ions were the best to improve selective conversion of the substrates. The active sites in these catalysts are shown to be 2D VOx layers stabilized on the anchors. In a screen of twelve solvents, weakly polar solvents like toluene were found to be most suitable for this reaction, as was environmentally friendly ethyl acetate. The above properties, together with the high selectivity for C–C bond cleavage, advocate for a heterogeneous catalytic pathway, intrinsically different from that reported previously for molecular oxovanadium (V) catalysts.
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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) | 0.000 | 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 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".