Polarity of Cationic Lignin Polymers: Physicochemical Behavior in Aqueous Solutions and Suspensions
Bibliographic record
Abstract
Abstract The structure of cationic monomers can significantly impact the polarity of lignin after polymerization. Cationic hydrolysis lignin (CHL) polymers were produced by polymerizing hydrolysis lignin (HL) with [3‐(methacryloylamino)propyl] trimethylammonium chloride (MAPTAC) or [2‐(methacryloyloxy)ethyl] trimethyl ammonium chloride (METAC). The METAC monomer has an oxygen atom, with larger electronegativity, in its molecular structure, whereas the MAPTAC monomer contains a nitrogen atom, as well as an extra nonpolar CH2 group, facilitating investigation into the effects of the polarity of CHLs on their physicochemical performance in an aqueous system. CHL polymers are analyzed and their interactions with clay particles are determined in colloidal systems. CHLs are designed to have similar charge densities (2.1–2.2 mmol g−1) and molecular weights (55000–60000 g mol−1 ). The hydrodynamic radius (Hy) and radius of gyration, (Rg) of HL‐METAC are larger than those of HL‐MAPTAC, implying a more 3‐dimensional structure of HL‐METAC in aqueous solution. The stability ratio of kaolin particles affirms the better performance of HL‐METAC in comparison to HL‐MAPTAC, which reflects the better flocculation efficiency of HL‐METAC. The results also reveal that salt and urea aqueous solutions affect the Hy, Rg, and configuration of CHL polymers, which alters the flocculation efficiency of HL‐METAC and HL‐MAPTAC polymers in kaolin suspensions.
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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 | 0.001 |
| 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".