Purification Processes for Generating Cationic Lignin-Acrylamide Polymers
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Purification is an essential step in many polymerization processes for fabricating highly pure polymers. This study considered various purification methods for purifying the product of lignin, acrylamide (AM), and diallyl dimethylammonium chloride (DADMAC) copolymerization reactions at a laboratory scale. The charge density, yield, molecular weight, and solubility analyses confirmed that ethanol extraction and membrane filtration were the most effective processes for producing lignin-p(AM)-p(DADMAC). The 1 H NMR analysis revealed that the membrane dialysis effectively removed unreacted AM and DADMAC monomers from the reaction medium. The produced samples of the ethanol-extraction and dialysis processes had higher solubility and yield compared to the product of the acidification process. Thermogravimetric studies confirmed that the ethanol-extracted and dialyzed samples had a degradation temperature (220 °C) higher than that of the acidified samples (160 °C). The rheological studies confirmed that the viscosities of the polymer solutions were influenced more by the solubility than by the molecular weight of the generated polymers within the molecular weight range examined in this study. The flocculation studies confirmed that the ethanol-extracted and dialyzed polymers were more effective flocculants than the acidified samples for the particles of a kaolinite suspension. Based on the above results, membrane filtration with a larger pore size could be an environmentally friendly method for effectively purifying lignin-p(AM)-p(DADMAC).
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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) | 0.001 | 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".