Residue‐Free Droplet Transport Against Gravity via Wettability‐Patterned Liquid‐Like Surfaces
Bibliographic record
Abstract
Abstract Lifting individual water droplets against gravity without external energy input is reported. Classical capillary rise requires the complete wetting of a transport pathway, resulting in water loss and poor collection efficiency. Boosting Laplace pressure propulsion while diminishing contact line pinning enables lossless water droplet ascension. Surfaces with properly designed physicochemical gradients can propel single water droplets as high as 65 mm against gravity, enabled by 1) surface wettability patterns, 2) liquid‐like polymer brushes, and 3) controlling droplet size within a shorebird beak‐inspired wedge geometry. Surface wettability patterns induce droplet elongation toward the gap‐to‐apex direction of the wedge geometry, which boosts Laplace pressure propulsion by increasing droplet length and diminishes contact line pinning by decreasing droplet width. Polymer brushes for wettability patterning exhibit minimal contact angle hysteresis, minimizing contact line pinning and enabling residue‐free droplet ascension. Controlling the droplet size prevents the dominance of either contact line pinning or gravity over Laplace pressure propulsion, enabling droplet transport against gravity. Utilizing these design rules, low surface tension n‐decane is also transported against gravity, and a particle‐laden solution is separated using sedimentation and against‐gravity transportation. Such surfaces may facilitate lossless, against‐gravity, and power‐free water pumps in numerous emerging sustainability and energy applications.
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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".