Plasma-Based Amorphous Carbon Coatings on Polymeric Substrates for Biomedical Applications: A Critical Review Focused on Adhesion
Bibliographic record
Abstract
Material surfaces are of primary importance in biomaterial development, significantly influencing implant lifespan and clinical success. Consequently, coating technologies are frequently employed to modify surface properties and functionality. Plasma-based amorphous carbon coatings have been widely applied to all classes of substrates to improve their tribology, corrosion resistance, hardness, and even biological properties. Plasma technology is widely recognized to be effective, not only for the deposition of amorphous carbon coatings but also for substrate pre-treatment, in which it may play a key role in activating surfaces and enhancing interfacial adhesion. Amorphous carbon coatings can be classified into two major categories: diamond-like carbon (DLC) and polymer-like carbon (PLC), according to their mechanical properties. Regardless of their nature, the adhesion of both types of amorphous carbon coatings to the substrate has always represented a major challenge. Several strategies have been reported to enhance the adhesion of DLC coatings to silicon wafers, metals, and glass substrates. However, few studies report strategies aimed at controlling the adhesion of (both types of) amorphous carbon coatings to polymeric substrates, polymeric implants, and polymeric devices. Therefore, this work aims to provide a state-of-the-art review on the adhesion of amorphous carbon coatings to polymeric substrates for biomedical applications. Furthermore, this review presents the main techniques used to assess adhesion and the strategies available to improve adhesion between coatings and polymeric 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".