Reductive Degradation of Lignin and Model Compounds by Hydrosilanes
Bibliographic record
Abstract
The exploitation of lignin, the second most abundant naturally occurring polymer on earth, has been hampered by its network structure, which makes it difficult to process. Hydrosilanes have previously been shown to convert aryl ethers to hydrolyzable silyl ethers in the presence of B(C 6 F 5 ) 3 . We demonstrate that the process is general and can be used to convert model lignin compounds to both aryl silyl ethers and alkanes. The relative reactivity of functional groups on model lignin compounds was found to be phenol > primary alcohol > methoxybenzene > alkyl silyl ethers. The process thus leads to cleavage of β-O-4, α-O-4, and methoxybenzene groups with concomitant silylation of phenolic and secondary alcohol groups. At longer time points provided sufficient silane was present, the full reduction of primary and secondary alcohols to alkyl groups was observed. Softwood lignin itself could only be partially solubilized (∼30%) even using excess hydrosilane and high catalyst loadings; the products were not characterized in detail. The lack of further degradation was attributed to its highly branched network structure containing 5-5, β-5, 4-O-5, and other linkages derived from coniferyl alcohol monomers that are not susceptible to reductive silylation. By contrast, over 95% of hardwood lignin was efficiently reduced/degraded into organosoluble products by the monofunctional hydrosilane HMe 2 SiOSiMe 3 over a few hours at 50 °C. The molecular weight of the silylated products was consistent with oligomeric structures comprised of 3–8 linked aryl groups. This process holds promise to increase the accessibility to value-added products using lignin as a starting material.
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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 | 0.000 | 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 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".