In-Situ Rheological Studies of Cationic Lignin Polymerization in an Acidic Aqueous System
Bibliographic record
Abstract
The chemistry of lignin polymerization was studied in the past. Insights into the rheological behavior of the lignin polymerization system would provide crucial information required for tailoring lignin polymers with desired properties. The in-situ rheological attributes of lignin polymerization with a cationic monomer, [2-(methacryloyloxy)ethyl] trimethylammonium chloride (METAC), were studied in detail in this work. The influences of process conditions, e.g., temperature, component concentrations, and shear rates, on the viscosity variations of the reaction systems during the polymerization were studied in detail. Temperature, METAC/lignin molar ratio, and shear rate increases led to the enhanced viscosity of the reaction medium and lignin polymer with a higher degree of polymerization. The extended reaction time enhanced the viscosity attributing to the larger molecular weight of the lignin polymer. Additionally, the size of particles in the reaction system dropped as reaction time was extended. The lignin polymer with a larger molecular weight and Rg behaved mainly as a viscose (tan δ > 1 or G″ > G′) material, while the lignin polymer generated with smaller molecular weight and shorter Rg demonstrated strong elastic characteristics with a tan (δ) lower than unity over the frequency range of 0.1−10 rad/s.
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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".