Surface Treatments of PEEK for Osseointegration to Bone
Bibliographic record
Abstract
Polymers, in general, and Poly (Ether-Ether-Ketone) (PEEK) have emerged as potential alternatives to conventional osseous implant biomaterials. Due to its distinct advantages over metallic implants, PEEK has been gaining increasing attention as a prime candidate for orthopaedic and dental implants. However, PEEK has a highly hydrophobic and bioinert surface that attenuates the differentiation and proliferation of osteoblasts and leads to implant failure. Several improvements have been made to the osseointegration potential of PEEK, which can be classified into three main categories: (1) surface functionalization with bioactive agents by physical or chemical means; (2) incorporation of bioactive materials either as surface coatings or as composites; and (3) construction of three-dimensionally porous structures on its surfaces. The physical treatments, such as plasma treatments of various elements, accelerated neutron beams, or conventional techniques like sandblasting and laser or ultraviolet radiation, change the micro-geometry of the implant surface. The chemical treatments change the surface composition of PEEK and should be titrated at the time of exposure. The implant surface can be incorporated with a bioactive material that should be selected following the desired use, loading condition, and antimicrobial load around the implant. For optimal results, a combination of the methods above is utilized to compensate for the limitations of individual methods. This review summarizes these methods and their combinations for optimizing the surface of PEEK for utilization as an implanted biomaterial.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".