Bibliographic record
Abstract
Implant infections have become a major obstacle to implant failure. Although traditional antibiotic treatments have provided a solution to some extent, conventional antibiotic drug treatments fail to eradicate bacteria and even cause antibiotic resistance. Therefore, the development of biomedical implants employing antimicrobial coatings becomes the focus of more and more research. Nanomaterials, with their excellent biocompatibility and unique antimicrobial properties, have been widely used in biomedical devices. This paper focuses on the application of nanocoatings in biomedical implants and the introduction of potential surface functionalization materials for implants. This paper will analyze metals as well as their oxide nanoparticles and 2d-nanomaterial-based Nanocoatings with antimicrobial properties. It is also essential to highlight how these nanoparticles can deal with biofilm infections and achieve antimicrobial properties. In addition, the article describes different nanoparticle coating strategies that provide a variety of options for the design of antimicrobial coatings for implants. In conclusion, nanotechnology provides clues to solve the problem of biomedical implant infections, reduces the risk of infection, and generates more reliable and effective treatments for patients.
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".