Plasma and Electrospray Deposition to Improve the Biocompatibility of Stents
Bibliographic record
Abstract
Metallic Intravascular stents are medical devices used to scaffold a biological lumen, mostly diseased arteries, after balloon angioplasty. They are commonly made of 316L stainless steel or Nitinol, two alloys containing Nickel, an element classified as potentially toxic and carcinogenic. Although they are largely implanted, the long-term safety of such metallic elements is still controversial, since the corrosion processes may lead to the release of several metallic ions. In order to avoid the metallic ion release in the body and to improve the biocompatibility of metallic stents with their biological environments, polymer coatings have been deposited by two different technologies, i.e. plasma surface modifications and Electrospraying. The role of the polymer coating is then to encapsulate the stainless steel device, and to favour the chemical grafting of Phosphorylcholine, a molecule known for its hemocompatible properties.1 In this talk, the state of the art on low pressure and atmospheric pressure plasmas for deposition of organic coatings will be given and we will present the advantages and drawbacks of each process. Then, we will present an original technology that combine a Dielectric Barrier Discharge and an electrospraying system to deposit well-defined Polyacrylic acid and Polyallylamine films. The advantage of such system is the possibility to limit the extent of the monomer fragmentation and to give rise to rapid deposition of a highly functionalised plasma polymer layer, and also the possibility to cover three dimensional objects, such as stents. Thus, the theory of EHDA technology will be explained: special attention has been paid to define the Electrospray parameters (Voltage, flow of precursor, nozzle-substrate distance…) which control the size distribution of the charged droplets and as a consequence, the structure of the film coating. The film coatings have been analysed with XPS and by ATR. Moreover, special attention will be paid on the stability of the coating which is related to both spraying conditions as well as to the preliminary plasma treatment. The potentiality and the features of the EHDA process will be then presented.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".