MEMS-Based Power Generation Techniques for Implantable Biosensing Applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Implantable biosensing is attractive for both medical monitoring and diagnostic applications. It is possible to monitor phenomena such as physical loads on joints or implants, vital signs, or osseointegration in vivo and in real time. Microelectromechanical (MEMS)-based generation techniques can allow for the autonomous operation of implantable biosensors by generating electrical power to replace or supplement existing battery-based power systems. By supplementing existing battery-based power systems for implantable biosensors, the operational lifetime of the sensor is increased. In addition, the potential for a greater amount of available power allows additional components to be added to the biosensing module, such as computational and wireless and components, improving functionality and performance of the biosensor. Photovoltaic, thermovoltaic, micro fuel cell, electrostatic, electromagnetic, and piezoelectric based generation schemes are evaluated in this paper for applicability for implantable biosensing. MEMS-based generation techniques that harvest ambient energy, such as vibration, are much better suited for implantable biosensing applications than fuel-based approaches, producing up to milliwatts of electrical power. High power density MEMS-based approaches, such as piezoelectric and electromagnetic schemes, allow for supplemental and replacement power schemes for biosensing applications to improve device capabilities and performance. In addition, this may allow for the biosensor to be further miniaturized, reducing the need for relatively large batteries with respect to device size. This would cause the implanted biosensor to be less invasive, increasing the quality of care received by the patient.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it