Tuning the Morphology of Cellulose Acetate Gels by Manipulating the Mechanism of Phase Separation
Bibliographic record
Abstract
The effect of dihydric alcohol (nonsolvent) addition on the rheological and microstructural behavior of cellulose acetate (CA) in a ternary CA, N,N-dimethylacetamide (DMA), nonsolvent system was investigated. Increasing the dihydric alcohol concentration led to enhanced steady shear viscosity and dynamic viscoelastic properties that were dependent on CA concentration. Changing the dihydric alcohol from 1,2-ethanediol to 1,4-butanediol and 1,6-hexanediol increased the moduli and decreased the concentration of nonsolvent at which the sol-gel transition occurred. At 10 wt % CA concentration the modulus and gel morphology of the 1,2-ethanediol and 1,4-butanediol systems were quite similar and distinctly different from that of 1,6-hexanediol. In the former, the gel morphologies were more heterogeneous, evident of more extensive coarsening, and likely obtained via nucleation and growth and spinoidal decomposition of off-critical mixtures. The latter exhibited more uniform dense network morphology, indicative of a spinoidal decomposition of near-critical mixtures. The gels were fractal in nature and exhibited different fractal dimensions in-line with the observed differences in microstructure; D ∼ 1.87 ± 0.02 (1,2-ethanediol and 1,4-butanediol) and D ∼ 1.97 ± 0.02 (1,6-hexanediol). However, at 15 wt % CA content, the gels exhibited more similar viscoelastic behavior and gel microstructures; D ∼ 1.97 ± 0.02 for all three dihydric alcohol systems.
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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) | 0.001 | 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".