Cationic lignin as an efficient and sustainable homogenous catalyst for aqueous Knoevenagel condensation reactions
Bibliographic record
Abstract
Knoevenagel condensation is a chemical reaction between aldehydes and active methylene-containing compounds in the presence of heterogeneous, basic homogenous organic or inorganic catalysts and solvent or neat systems. Herein, we introduced a new strategy for this synthesis by using the aqueous solution of cationic kraft lignin (CKL) as a catalyst. The CKL was synthesized through the reaction of kraft lignin (KL) with glycidyltrimethylammonium chloride (GTMAC) in a basic medium. The optimal reaction conditions for the Knoevenagel reaction were 5% catalyst load (weight of catalyst to the weight of benzaldehyde), water as the solvent, and at room temperature, which generated the products with a yield of 97%, illustrating that the CKL was an effective homogenous and green catalyst. The results confirmed that the increase in CKL charge density improved the product yield. The water-insoluble products were easily separated by filtration, and the filtrate containing the catalysts was reused effectively for 5 cycles without a significant decrease in the production yield, which would confirm the advantages of this catalyst for this reaction system. The CKL catalyst exhibited biodegradability comparable to KL. This paper discusses a novel method for Knoevenagel condensation reactions for different aldehydes in a green system utilizing a sustainable, biodegradable catalyst at room temperature and in an aqueous system.
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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".