Laser Direct Write Passive Sensors for Smart Orthopedic Implants Base on Poly-Ether-Ether-Ketone (PEEK) and Carbon Fiber Reinforced (CFR)-PEEK
Bibliographic record
Abstract
To monitor bone healing wirelessly on Polyether-ether-ketone (PEEK) and Carbon fiber reinforced (CFR)-PEEK orthopedic implants, a laser-direct writing technique was utilized to fabricate a sensor on it. The split ring resonators (SRRs) pattern was written on the orthopedic implant as the strain sensor, read wirelessly by another antenna. The conductivity of the laser-written layer was up to 30,000 S/m. The sensitivity of the SRR sensor on the CFR-PEEK orthopedic plate was ~500 MHz/ε. In vivo, the biocompatibility and mechanical support of the PEEK plate with the sensor were proved. A strain sensor written by laser on PEEK and CFR-PEEK was feasible for orthopedic implants.
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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 | 0.000 |
| 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".