Flocculation with Poly(ethylene oxide)/Tyrosine-Rich Polypeptide Complexes
Bibliographic record
Abstract
New insights into the mechanism for the flocculation of aqueous colloids by the sequential addition of a water-borne phenolic polymer, called cofactor, followed by very high molecular poly(ethylene oxide) (PEO) are presented. It is proposed that PEO/cofactor complexes form in the aqueous phase and adsorb onto the surfaces of the target colloidal particles. Flocculation will occur if PEO/cofactor complex on one particle will bind to adsorbed complex on a second particle; i.e., if the complexes are sticky. The proposed mechanism was illustrated by flocculation experiments with precipitated calcium carbonate, very high molecular weight PEO, and a polypeptide cofactor called PEY1 which was a 1:1 random copolymer of l-glycine and l-tyrosine. Independent measurements of the PEO/PEY1 complex properties, in the absence of calcium carbonate, were used to support the mechanism. In order for PEO/PEY1 complexes to be sticky, they must simultaneously have unbound PEY1 and polymer segments. With time the complexes deactivate (i.e., lose their stickiness) by a reconfiguration process which results in elimination of either unbound PEY1 or PEO segments.
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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 | 0.000 |
| 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".