Morphological Control <i>via</i> Chemical and Shear Forces in Block Copolymer Self-Assembly in the Lab-on-Chip
Bibliographic record
Abstract
We investigate the effects of variation in chemical conditions (solvent composition, water content, polymer concentration, and added salt) on the morphologies formed by PS-b-PAA in DMF/dioxane/water mixtures in a two-phase gas-liquid segmented microfluidic reactor. The differences in morphologies between off-chip and on-chip self-assembly and on-chip morphological trends for different chemical conditions are explained by the interplay of top-down shear effects (coalescence and breakup) and bottom-up chemical forces. Using off-chip morphology results, we construct a water content-solvent composition phase diagram showing disordered, sphere, cylinder, and vesicle regions. On-chip morphologies are found to deviate from off-chip morphologies by three identified shear-induced paths: 1) sphere-to-cylinder, and 2) sphere-to-vesicle transitions, both via shear-induced coalescence when initial micelle sizes are small, and 3) cylinder-to-sphere transitions via shear-induced breakup when initial micelle sizes are large (high capillary number conditions). These pathways contribute to the generation of large extended bilayer aggregates uniquely on-chip, at either increased polymer or salt concentrations. Collectively these results demonstrate the broad utility of top-down directed molecular self-assembly in conjunction with chemical forces to control morphology and size of polymer colloids at the nanoscale.
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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".