Efficient and rapid depolymerization of Kraft lignin to bio-oil using microwave heating with solvent sequential extraction
Bibliographic record
Abstract
Energy-intensive conditions in conventional methods for lignin depolymerization highlight the need for more sustainable approaches. Microwave irradiation, known for its faster heating, lower energy consumption, and reduced reaction times, makes it an ideal alternative. This study explored the feasibility of microwave-assisted lignin depolymerization in isopropanol and formic acid without a metal-based catalyst. The temperature and lignin dosage emerged as the most critical factors influencing yield and molecular weight. The highest bio-oil yield of 55 % was achieved by depolymerizing 0.5 g of lignin at 120 °C for 30 min. Due to the complex oligomer mixture in the bio-oil, a sequential solvent extraction method was used to obtain uniform fractions. The process involved sequential dissolution in ethyl acetate, dichloromethane/toluene, and hexane, with the insoluble residues collected at each process step. The extraction process favored a reduction in molecular weight, achieved a narrower polydispersity, and increased the concentration of phenolic units. These changes made the oligomeric fractions more suitable for various applications, enhancing their potential as replacements for petroleum-based compounds. This study also examined the biochar derived from the insoluble fraction of depolymerization, emphasizing its role in improving lignin valorization. Preliminary analysis indicated that 34 wt% of biochar was produced, with the majority of silica and inorganic compounds retained in this fraction. • Effective depolymerization of Kraft lignin using microwave heating was achieved. • The process yielded 55 % bio-oil and 4.2 mmol/g of total hydroxyl groups. • Solvent extraction method efficiently separated dimers and trimers from bio-oil. • Solvent extraction produced fractions enriched in non-condensed phenolic compounds.
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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) | 0.001 | 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".