PEEK surface modification methods and effect of the laser method on surface properties
Bibliographic record
Abstract
Polyether ether ketone (PEEK) is one the most interesting polymeric materials used in the industry today, such as aerospace, nuclear reactors, polymer electrolyte membranes and especially in biomedical applications like bone implants. PEEK’s desirable properties like mechanical strength, biocompatibility, chemical resistance, radiation resistance and high thermal stability in the body make this suitable polymer choice for a bone implant. Besides these useful properties, PEEK is bio-inert in the biological environment, which is a big problem in implant application. Fortunately, there are several methods to improve the surface bioactivity of such materials. Here surface modification methods of the PEEK, including laser and their effect on the surface bioactivity were studied. Laser techniques are one of the exciting methods for PEEK surface modification because of being a secure processing method, time-consuming, easy to control the laser parameter, which leads to the control of surface properties. Several kinds of laser with different settings is used for the enhancement of the surface of PEEK, were described here. Here different surface modification techniques to enhance the adhesion and wettability of the PEEK surface studied. Along with varying categories of laser were introduced and different laser methods, which used for PEEK surface treatment is collected, that is the exciting point of this review paper.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".