Water‐soluble acrylamide copolymers. X. Flocculation efficiencies of poly[acrylamide‐<i>co</i>‐<i>N,N</i>‐dimethylacrylamide], poly[acrylamide‐<i>co</i>‐methacrylamide], poly[acrylamide‐<i>co</i>‐<i>N</i>‐<i>t</i>‐butylacrylamide], and their cationic derivatives
Bibliographic record
Abstract
Abstract A hydrated, 1% by weight Na‐kaolinite suspension in deionized water was prepared, completely characterized, and reproducible measures of flocculation efficiency were validated. Flocculation tests of copolymers of acrylamide (AM) with dimethylacrylamide (DMA), methacrylamide (MeAM), or N‐t‐butylacrylamide (NTBAM) with 1% Na‐kaolinite suspensions gave average settling rate rates which decreased as the proportion of DMA, MeAM, or NTBAM in the copolymer increased. However, for a similar weight‐average molecular weight and slightly lower 〈rg〉, the copolymer from DMA‐co‐AM‐3 gave settling rates and supernatant turbidities comparable to similar types of commercial polymers. This new copolymer was also more resistant to changes in pH or the presence of an electrolyte than were the tested commercial polymers. Cationic derivatives of the new copolymers gave lower average settling rates and higher supernatant turbidities than those of Percol 721 (cationic PAM), probably because of their lower charge densities. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 84: 2090–2108, 2002; DOI 10.1002/app.10562
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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".