Miniature Implantable Pressure Sensors for Medical Applications
Bibliographic record
Abstract
Pressure sensors are requisite for many medical implantable devices to monitor physiological pressures or fluid pressure and flow from a subsystem. Size, power consumption, accuracy, sensitivity, stability, and biocompatibility are all key considerations in the design and fabrication of such sensors. Conventional designs, based on piezoresistive technologies, are power consuming with significant drift and temperature error, whereas capacitive solutions are often cumbersome when packaged for biocompatibility. Tronics Microsystems has developed absolute pressure sensors, which achieve the benefits of both technologies. Miniaturization is achieved using a MEMS sensing element and a multifunction ASIC with small form factors. Low power consumption, low drift, high resolution, and waveform capture capability are obtained by using a capacitive MEMS coupled with a sigma-delta, direct capacitance to digital converter. Biocompatibility is achieved with grade II titanium packaging in two form factors (“tubular” or “pancake”) for incorporation into various applications. These sensors have been fabricated, calibrated, and tested extensively over physiologic temperature ranges. The design has achieved power consumption lower than 500 ÂμW at 100 Hz and a drift lower than 0.5% full scale per year. An accuracy of +/−1% full scale, over the temperature range is obtained by on-ASIC nonlinearity and temperature compensation. The two packaging configurations allow analysis of the trade-offs on the temperature range, sensitivity, volume, sterilization, etc. Different feed-through materials permit optimization of the form factors for the tube and the flat sensor and wired or wireless communication.
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.
How this classification was reachedexpand
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) | 0.000 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".