A Simple Method to Prepare a Bioinspired Fog Collection System Combined with Wettability Patterns and Slippery Liquid Infused Porous Surfaces
Bibliographic record
Abstract
Freshwater shortage is a growing problem, and inspired by the ultrafast directional water transport structure of the Sarracenia trichomes and the excellent lubrication effect of SLIPS, bionic hierarchical structured surfaces with wettability patterns were prepared based on laser processing combined with dip and oil-infused modification. The prepared surfaces were tested for sliding performance, water impact, corrosion resistance, and fog collection, and the relationships between the surface structure, wettability, sliding properties, and droplet directional condensation, coalescence, absorption, and directional water transport, as well as their influences on the fog collection performance, were investigated by analyzing the fog collection process. In addition, the optimization direction of surfaces with wettability patterns to alleviate water collection obstacles and improve fog collection efficiency is given. The method offers simplicity and high fog collection efficiency. This study provides a good reference for the development and preparation of fog collection surfaces.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".