Bibliographic record
Abstract
The interactions between cellulose nanocrystals and six different polymers (three anionic, two non-ionic, and one cationic) were investigated using rheological measurements of aqueous solutions of nanocrystals and polymers. The experimental viscosity data could be described adequately by a power-law model. The variations in power-law parameters (consistency index and flow behavior index) with concentrations of nanocrystals and polymers were determined for different combinations of nanocrystals and polymers. The interactions between nanocrystals and the following polymers: anionic sodium carboxymethyl cellulose and non-ionic guar gum, were found to be strong in that the consistency index increased substantially with the addition of nanocrystals to polymer solutions. The interaction between nanocrystals and non-ionic polymer polyethylene oxide was moderate. Depending on the concentrations of nanocrystals and polymer, the consistency index both increased and decreased upon the addition of nanocrystals to polymer solution. The interactions between nanocrystals and the following polymers: anionic xanthan gum, anionic polyacrylamide, and cationic quaternary ammonium salt of hydroxyethyl cellulose, were found to be weak. The changes in rheological properties with nanocrystal addition to these polymer solutions were found to be small or negligible.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".