Enzymatic Transesterification of Kraft Lignin with Long Acyl Chains in Ionic Liquids
Bibliographic record
Abstract
Valorization of lignin is essential for the economic viability of the biorefinery concept. For example, the enhancement of lignin hydrophobicity by chemical esterification is known to improve its miscibility in apolar polyolefin matrices, thereby helping the production of bio-based composites. To this end and due to its many reactive hydroxyl groups, lignin is a challenging macromolecular substrate for biocatalyzed esterification in non-conventional media. The present work describes for the first time the lipase-catalyzed transesterification of Kraft lignin in ionic liquids (ILs). Three lipases, three 1-butyl-3-methylimidazolium based ILs and ethyl oleate as long chain acyl donor were selected. Best results were obtained with a hydrophilic/hydrophobic binary IL system (1-butyl-3-methylimidazolium trifluoromethanesulfonate/1-butyl-3-methylimidazolium hexafluoro- phosphate, 1/1 v/v) and the immobilized lipase B from Candida antarctica (CALB) that afforded a promising transesterification yield (ca. 30%). Similar performances were achieved by using 1-butyl-3-methylimidazolium hexafluorophosphate as a coating agent for CALB rather than as a co-solvent in 1-butyl-3-methylimidazolium trifluoromethane-sulfonate thus limiting the use of hydrophobic IL. Structural characterization of lignin oleate was performed by spectroscopic studies (FTIR and ¹H-NMR). The synthesized lignin oleate exhibited interesting thermal and textural properties, different from those of the original Kraft lignin.
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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 | 0.001 |
| 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".