Spray-On Nanocomposite Coatings: Wettability and Conductivity
Bibliographic record
Abstract
Nanocomposite coatings, i.e., a combination of nanocompounds, and a polymer matrix together with suitable additives and solvents is a very versatile method for producing multifunctional coatings. Some of the most desired coating properties have a high repellency to liquids (e.g., superhydrophobic and/or superoleophobic) and electrical and thermal conductivities. From a practical perspective, coatings that can be sprayed are very suitable for large-scale production, conformity, and reduced time and cost. Carbon-based, metallic, and ceramic are the three groups of nanocompounds commonly used to formulate spray-on nanocomposite coatings. In this invited feature article, we discuss the applications, advantages, and challenges of using such nanocompounds to produce coatings with good water repellency or/and elevated electrical or/and thermal conductivities. We also discuss the role of additives and solvents briefly in relation to the properties of the coatings. Important spraying parameters, such as stand-off distance and its influence on the final coating properties, will also be examined. Our overall aim is to provide a guideline for the production of practical multifunctional nanocomposites utilizing carbon-based, metallic, or ceramic nanoparticles or nanofibers that covers both aspects of in-air wettability and conductivity under one umbrella.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".