Sprayable, Superhydrophobic, Electrically, and Thermally Conductive Coating
Bibliographic record
Abstract
Abstract A simple to make, multifunctional, heatable, and sprayable superhydrophobic and electrically conductive coating is developed by dispersing carbon nanofibers (CNFs) into a water repelling polymer matrix. The developed coating exhibits an average static and hysteresis contact angles of 160° and 5°, respectively. An electrical conductivity of 1100 S m−1 and a thermal conductivity of 0.001 W m−1 K−1 are obtained with a sample of dimensions: 3 cm × 1 cm × 20 µm. A 12 µm thick coating under an average electric current of 75 mA reaches to a surface temperature of more than 140 °C for a dry coating. The coating when in contact with ice or water (water at 25 °C for 300 h, and at 85 °C for less than an hour) does not show a deterioration of wetting performance. Furthermore, it is shown how this coating can be used to mitigate the ice formation on cold surfaces. The ability of application of the developed coating to various substrates is also shown.
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) | 0.001 | 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".