Programmable droplets: Leveraging digitally-responsive flow fields to actively tune liquid morphologies
Bibliographic record
Abstract
Stimulus-responsive materials enable programmable and adaptive behaviors. Typical solid-phase systems can only achieve small deformations for applications where shape transformations are beneficial or required. Liquids, in contrast, can self-assemble and achieve very high strains in a multifluid environment. Here we report liquid droplet formation by tuning flow potential within a confined fluidic cell. We digitally inject small volumes of liquid-pigment into an otherwise-transparent liquid layer, generating macroscopic droplet assembly over large areas constrained between closely-spaced plates. Droplet morphology is actively controlled by modulating outlet conditions to tune flow fields. Pattern stability is maintained through control over injection rate, interfacial viscosity difference, and interfacial surface tension. We demonstrate time-dependent droplet formation and migration to achieve spatially-tunable optical properties. Applied as a multi-cell array, we imagine this liquid mechanism will enable scalable pattern dynamics for active shading and visual display technologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".