Effect of Extreme Wettability on Platelet Adhesion on Metallic Implants: From Superhydrophilicity to Superhydrophobicity
Bibliographic record
Abstract
In order to design antithrombotic implants, the effect of extreme wettability (superhydrophilicity to superhydrophobicity) on the biocompatibility of the metallic substrates (stainless steel and titanium) was investigated. The wettability of the surface was altered by chemical treatments and laser ablation methods. The chemical treatments generated different functionality groups and chemical composition as evident from XPS analysis. The micro/nanopatterning by laser ablation resulted in three different pattern geometry and different surface roughness and consequently wettability. The patterned surface were further modified with chemical treatments to generate a wide range of surface wettability. The influence of chemical functional groups, pattern geometry, and surface wettability on protein adsorption and platelet adhesion was studied. On chemically treated flat surfaces, the type of hydrophilic treatment was shown to be a contributing factor that determines the platelet adhesion, since the hydrophilic oxidized substrates exhibit less platelet adhesion in comparison to the control untreated or acid treated surfaces. Also, the surface morphology, surface roughness, and superhydrophobic character of the surfaces are contributing factors to platelet adhesion on the surface. Our results show that superhydrophobic cauliflower-like patterns are highly resistant to platelet adhesion possibly due to the stability of Cassie-Baxter state for this pattern compared to others. Our results also show that simple surface treatments on metals offer a novel way to improve the hemocompatibility of metallic substrates.
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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 | 0.000 | 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".