Superfast Liquid Transfer Strategy Through Sliding on a Liquid Membrane Inspired from Scorpion Setae
Bibliographic record
Abstract
Abstract Although diversified biological structures have evolved fog collection abilities, the typical speeds of the condensed water droplets on these surfaces are too slow to have practical utility. The main challenge focuses on the elimination of the interfacial hydrodynamic resistance without external energy support. Here, an unusual strategy for superfast self‐support transfer condensed droplets is supported by sliding on seta of desert scorpion. It can be rapidly wetted by the fog droplets owing to its conical shape with linear gradient channels. A loss of interfacial resistance by this hydrodynamically lubricating water membrane could significantly accelerate the movement of the droplets, thus making its velocity increasing by one order of magnitude, or even more. Inspired by this novel strategy, the novel bioinspired materials are fabricated with the similar gradient channel structures and droplet transportation mode, which can make the condensed droplets spontaneously slide on the low‐friction liquid membrane. The fundamental understanding of superfast fog capture and the sliding dynamics of condensed droplets in this system could inspire to develop novel materials or various systems to transfer liquid fast and efficiently without external energy support.
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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".