Liquid crystal–based open surface microfluidics manipulate liquid mobility and chemical composition on demand
Bibliographic record
Abstract
The ability to control both the mobility and chemical compositions of microliter-scale aqueous droplets is an essential prerequisite for next-generation open surface microfluidics. Independently manipulating the chemical compositions of aqueous droplets without altering their mobility, however, remains challenging. In this work, we address this challenge by designing a class of open surface microfluidic platforms based on thermotropic liquid crystals (LCs). We demonstrate, both experimentally and theoretically, that the unique positional and orientational order of LC molecules intrinsically decouple cargo release functionality from droplet mobility via selective phase transitions. Furthermore, we build sodium sulfide–loaded LC surfaces that can efficiently precipitate heavy metal ions in sliding water droplets to final concentration less than 1 part per million for more than 500 cycles without causing droplets to become pinned. Overall, our results reveal that LC surfaces offer unique possibilities for the design of novel open surface fluidic systems with orthogonal functionalities.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".