Enzymatic polymerization of sodium lignosulfonates: effect of catalysts, initial molecular weight, and mediators
Bibliographic record
Abstract
The aim of this study was to investigate the effect of different parameters on the enzymatic polymerization of sodium lignosulfonates (SLS) by laccase, compared with the chemical treatment by manganese III. Different initial molecular weights of SLS (commercial SLS (17 800 Da), F1 (4300 Da), F2 (2500 Da), and F3 (2300 Da)) were tested. Size exclusion chromatography (SEC-UV), Fourier transform infrared (FT-IR) and phenolic group determination showed that SLS molecular weight increases depending on the laccase origin, the enzyme, and the substrate concentrations and the initial molecular weight of the SLS fractions. The highest molecular weight (Mw) was obtained by fungal laccases, specifically when using laccase from Trametes versicolor, while no reactivity was observed by plant laccase (laccase from Rhus vernicifera). The largest increase of Mw (108 600 Da) is reached when using SLS (17 800 Da) at 50 g/L and 30 U/mL of laccase from Trametes versicolor. The laccase polymerization of SLS can be improved by the use of a mediator. In this study, 5 mediators were studied for F1 polymerization by laccase from Trametes versicolor: acetosyringone (ASG), violuric acid (VLA), 1-hydroxy-benzotriazole (HBT), acetovanillone (ACV) and 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS). Results of F1 polymerization with mediators showed that only ASG and VLA lead to a higher molecular weight (7500 Da) compared with reactions carried without a mediator (6600 Da).
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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".