Improving the Mechanical Durability of Superhydrophobic Coating by Deposition onto a Mesh Structure
Bibliographic record
Abstract
Superhydrophobic surfaces (SHSs) require a combination of a nano- or microscale rugosity and a low surface energy. However, SH is easily lost under relatively mild mechanical abrasion. Here, by introducing a mesh layer beneath the SH layer, we develop a method that significantly increases the mechanical durability of a SHS. Using the commercially available Ultra-ever Dry SH coating, we found that hardness, abrasion distance, flexibility and water-jet impact resistance all increase. These increases are attributed to the increased mechanical support offered by the presence of the mesh, which provides dynamic mechanical losses at the temperatures and equivalent frequencies of the applied stresses. The SH of the coating surface on both sliding abrasion and water jet impact, as determined by slide angle (SA), exhibits two steps; the first is associated with the wearing away of the surface nanoparticles, and the second, with the wear of the underlying microstructures. A comparison of the SAs, as a function of abrasion distance, demonstrates that the presence of the mesh can significantly protect the nanoparticles, improving and prolonging SH, thereby extending the number of applications of such coatings. The improved mechanical durability may be attributed to the mesh structure protecting the rugosity, and its ability to absorb the energy from both sliding abrasion and water-jet impact.
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".