Effect of Dielectric Material and Package Stiffness on the Power Generation in a Packaged Triboelectric Energy Harvesting System for Total Knee Replacement
Bibliographic record
Abstract
The objectives of this study are to experimentally investigate the effects of the dielectric material and the package stiffness on the durability and the efficiency of a previously developed triboelectric-based instrumented knee implant prototype. The proposed smart knee implant may provide useful information about prosthesis health and its functionality after a total knee replacement (TKR) by routine monitoring of tibiofemoral load transfer without the need for any external power source. The triboelectric powered load sensing by the proposed TKR system needs to be functional throughout the entire life of a knee replacement. The power output of the triboelectric system depends on the surface charge generations and accumulations on its dielectric material, and the force that transmits through its housing into the tribo-materials. The properties of the dielectric material and the package stiffness can significantly influence the reliability of the proposed device. For such a TKR system, a compliant mechanism with the ideal material selection can improve its state of the art. We investigated the performance of three vertical contact mode triboelectric generators made with three different dielectric materials: polydimethylsiloxane (PDMS), fluorinated ethylene propylene (FEP), and polytetrafluoroethylene (PTFE). To investigate the effect of package stiffness, we tested two Ti-PDMS-Ti harvesters inside a polyethylene and a Ti6Al4V package. At 1500 N of sinusoidal loads, the harvesters could generate 67.73 μW and 19.81 μW of mean apparent power in parallel and single connections in the polyethylene package, which was 32 and 17 times greater than the power recorded in the Ti assembly, respectively.
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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 | 0.001 |
| 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".