Directed stratification in polymer–latex film blends via pH and drying control
Bibliographic record
Abstract
HYPOTHESIS: Stratification in polymer-colloid films is governed by interfacial interactions, component miscibility, and drying kinetics. We hypothesize that introducing a pH-responsive, acid-rich oligomer (ARO2) into latex films enables controlled vertical phase separation through electrostatic tuning and evaporation rate modulation. EXPERIMENTS: = 274 nm), varying pH (8.2-9.5), relative humidity (RH: 15-45 %), and ionic strength (0-50 mM NaCl). Förster Resonance Energy Transfer (FRET) was used to quantify ARO2-latex interdiffusion, while vertical distribution was characterized using confocal laser scanning microscopy (CLSM) and cross-sectional atomic force microscopy (AFM). FINDINGS: ≈ 0.06), creating conditions favorable for stratification during drying. At pH 9.5 and 15 % RH, electrostatic repulsion between ionized ARO2 and latex particles, combined with size ratio (α ≈ 18) and moderate Péclet numbers (Pe ≈ 2.1), drove diffusiophoretic transport of ARO2 toward the evaporating interface. CLSM and AFM revealed a distinct ARO2-rich surface layer (10-20 μm thick) under these conditions, while slower drying or increased salt concentration suppressed segregation. This controllable stratification mechanism enables design of structured polymer-latex coatings: pH > 9 + low RH promotes surface enrichment, while pH < 8 or high RH yields uniform distribution.
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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".