Topography‐Supported Nanoarchitectonics of Hybrid Scaffold for Systematically Modulated Bone Regeneration and Remodeling
Bibliographic record
Abstract
Abstract Orthopedic implants should have sufficient strength and promote bone tissue regeneration. However, most conventional implants are optimized for use either under high mechanical load or for active osseointegration. To achieve the dual target of mechanical durability and biocompatibility, polyether ether ketone (PEEK) filaments reinforced with internal titanium dioxide (TiO2) nanoparticles via dopamine‐induced polymerization are additively manufactured into an orthopedic implant through material extrusion (ME). The exterior of the PEEK/TiO2 composite is coated with hydroxyapatite (HA) using radiofrequency (RF) magnetron sputtering to increase both the strength and biocompatibility provided by homogeneous ceramic–ceramic interactions and the protuberant nanoscale topography between the internal TiO2 nanoparticle reinforcement and external HA coating. The hardness, tensile, and compression, and scratch test results demonstrate a considerable enhancement in the mechanical strength of the hierarchical PEEK/TiO2/HA hybrid composite structure compared to that of the conventional 3D‐printed PEEK. Furthermore, PEEK with internal TiO2 reinforcement improves the proliferation and differentiation of bone cells in vitro, whereas the external HA coating leads to a more prevalent osteoblast absorption. Micro‐computed tomography and histological analyses confirm new bone formation and a high bone‐to‐implant contact ratio on the HA‐coated PEEK structure reinforced with TiO2 nanoparticles.
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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 | 0.000 |
| 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".