Efficient Fog Harvesting Based on 1D Copper Wire Inspired by the Plant Pitaya
Bibliographic record
Abstract
The leaf of the plant pitaya shows excellent fog harvesting behavior through its 1D thorns with wire-like microstructures. The thorns of it cannot provide enough driving force for the droplet transportation by the special structure and chemistry gradient as the cactus thorns, but it showed efficient water supply which improved the fog harvesting greatly. The mechanism is studied based on 1D copper wire with similar 1D wire-like microstructure and wettability. This structure can significantly reduce the deviation of the fog-laden winds, and the surface intrinsic hydrophility makes water accumulate on it in the form of droplets, which endow it with an efficient water supply that is ∼100 times faster than that on a 2D-flat surface. In addition, it can also enhance the fog capture and water removal. The 3D fog collector composed of 1D microcopper wires has been fabricated which show a high fog harvesting efficiency of ∼13%. This work explains the role of 1D wire-like microstructure in efficient fog harvesting in a different view and provides new insight into the application of developing a more efficient fog collector.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".