Biocompatible Energy Harvester for Smart Knee Implants
Bibliographic record
Abstract
Abstract Although total knee replacements (TKR) are generally considered highly successful, patient satisfaction may not always be sustained over time. Monitoring the performance of the replaced knee can lead to improved surgical outcomes. Triboelectric Nano Generator (TENG) can generate electrical energy to power a load sensor, enabling continuous monitoring of the total knee replacement and more specifically monitoring imbalance over time. The objective of this study is to create a TENG capable of handling body forces and generating enough electrical energy to power the sensors. This is achieved through the energy harvester, which generates electric power by utilizing the contact separation resulting from cyclic compressive loads. To enhance the electrical properties of the energy harvester, a keystroke shape was used as a TENG structure. Moreover, the favorable characteristics of biocompatibility of the materials used in this study carry significant importance, particularly in applications where compatibility with biological systems is essential. This makes them particularly well-suited for integration into total knee implants. The experiment was conducted in this study and showed that the keystroke TENG structure could generate around 20 V peak voltage 2 kN axial force was applied to the TENG at 1 Hz. Expanding this to an array design enables measuring pressure distribution across TKR.
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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".