Effect of Hydrophilic and Hydrophobic Interactions on the Rheological Behavior and Microstructure of a Ternary Cellulose Acetate System
Bibliographic record
Abstract
The effect of hydrophilic and hydrophobic interactions on the rheological and microstructural behavior of cellulose acetate (CA) in a ternary CA, N,N-dimethylacetamide (DMA), nonsolvent (alcohol) system was examined. Increasing nonsolvent concentration increased the viscosity and dynamic viscoelastic properties of the system. At a critical nonsolvent concentration, a sol-gel transition was observed, which was dependent on nonsolvent structure. Increasing the available hydrogen bonding groups within the nonsolvent led to higher modulus (stronger gels) and a sol-gel transition at lower nonsolvent concentration. Likewise, increasing the alkyl chain length (hydrophobicity) of the nonsolvent also enhanced the viscoelastic properties; however, hydrogen bonding, specifically the ability to hydrogen bond donate was critical for gel formation. For all gels studied, the elastic modulus shifts to higher values with increasing hydrophilicity and hydrophobicity of the nonsolvent and exhibits a power-law behavior with nonsolvent content. All of the gels exhibit similar fractal dimensions; however, confocal images of the different systems reveal distinct differences. Increasing the hydrophilicity of the nonsolvent led to a more uniform denser gel microstructure, whereas increasing the hydrophobicity resulted in a larger more heterogeneous network structure despite the increase in moduli.
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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 | 0.001 |
| 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".