Generation and Use of Lignin-<i>g</i>-AMPS in Extended DLVO Theory for Evaluating the Flocculation of Colloidal Particles
Bibliographic record
Abstract
Abstract In this work, Kraft lignin (KL) was polymerized with 2-acrylamido-2-methylpropane sulfonic acid (AMPS) to generate an anionic water-soluble KL-g-AMPS polymer. The effects of reaction conditions on the charge density of polymers were evaluated to induce lignin-based polymers with the highest anionic charge density. The optimal process conditions were 2.5 mol/mol AMPS/lignin, 0.6 g/g solid/water ratio, 2.0 initiator/lignin weight ratio, 80 °C, 120 min, and pH 1.5, which yielded KL-g-AMPS with the anionic charge density of 4.28 mequiv/g and the grafting ratio of 285%. The chemical structure and compositions of the polymers were confirmed by 1H NMR and elemental analysis. The flocculation performance of the polymer was evaluated in an aluminum oxide suspension, and its performance was compared with that of a homopolymer of AMPS produced under the same conditions. In addition, the extended Derjaguin–Landau–Verwey–Overbeek (XDLVO) theory was applied to study the flocculation mechanism of the polymers and alumina particles. The results revealed that electrostatic interaction was found to be the dominant force in this flocculation process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".