Sustainable Process for the Depolymerization/Oxidation of Softwood and Hardwood Kraft Lignins Using Hydrogen Peroxide under Ambient Conditions
Bibliographic record
Abstract
The present study demonstrated a sustainable and cost-effective approach to depolymerize/oxidize softwood (SW) and hardwood (HW) kraft lignins using concentrated hydrogen peroxide at temperatures ranging from 25 to 35 °C, in the absence of catalysts or organic solvents. The degree of lignin depolymerization could be simply controlled by reaction time, and no further separation process was needed at the completion of the treatment. The obtained depolymerized lignin products were comprehensively characterized by GPC–UV, FTIR, 31P-NMR, TGA, Py-GC/MS and elemental analysis. The weight-average molecular weights (Mw) of the depolymerized lignins obtained from SW or HW lignin at a lignin/H2O2 mass ratio of 1:1 after treatment for 120 h at room temperature (≈25 °C) were approximately 1420 Da. The contents of carboxylic acid groups in the obtained depolymerized lignins were found to significantly increase compared with those of the untreated raw lignins. Moreover, the depolymerized lignin products had lower thermal decomposition temperatures than those of the raw lignins, as expected, owing to the greatly reduced Mw. These findings represent a novel solution to lignin depolymerization for the production of chemicals that can be utilized as a bio-substitute for petroleum-based polyols in polyurethane production.
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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".