Optimum phenolic monomer production by competing catalytic depolymerization and repolymerization of oak-extracted organosolv lignin
Bibliographic record
Abstract
Lignin, a byproduct of pulping and lignocellulosic biorefineries, holds promise as a feedstock for producing aromatic chemicals that can replace petroleum-derived counterparts. Reductive catalytic depolymerization of lignin has been proposed as a sustainable approach to generate phenolic monomers. However, achieving high yields of these monomers is challenging because of the complexity of the product mixture and process deactivation. Additionally, the interplay between lignin depolymerization and repolymerization remains poorly understood. In this study, organosolv lignin extracted from oak was depolymerized using a hydrogen-form zeolite β-supported ruthenium catalyst. By optimizing the catalyst-to-lignin ratio (0.25 w/w), a maximum phenolic monomer yield of 15.9 % (at 280 °C in 75 % (v/v) aqueous methanol) was achieved, independent of other reaction conditions. This finding highlights the catalyst-to-lignin ratio as a critical determinant of lignin conversion efficiency. Furthermore, the study emphasizes the need to optimize reaction conditions to mitigate repolymerization, which leads to the formation of non-degradable polymers and suppresses phenolic molecule production. • Reductive depolymerization of lignin was performed using Ru/Hβ catalyst. • Repolymerization along with depolymerization occurs on the catalyst. • Optimum amount of catalyst was determined for the optimum depolymerization. • Mechanism with competing repolymerization and depolymerization was suggested.
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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".