Design, Simulation, Fabrication, and Characterization of Low-Frequency CMARs Relying on Flexible and Bio-Compatible Electronics for Cis
Bibliographic record
Abstract
The requirements for cochlear implants (CIs) vary over time and go beyond present device performance. In this work, capacitive micromachined acoustic receivers (CMARs) are adopted to promote low-frequency acoustic transducer development. Nowadays, half-implantable CIs are based on piezoelectric materials and face problems of large volume and low biocompatibility. CMARs can take advantage of electronic integration with bio-compatible materials to overcome obstructions, address issues of the self-heating effects, and fill narrow bandwidths from piezoelectric materials, which gives CMARs the potential to fulfill full-implantable CIs. CMARs are the improved product based on capacitive micromachined ultrasonic transducers (CMUTs). The commonplace micromachined materials for CMUTs limit the receivers' eigen frequencies of the first bulking mode shape in megahertz (MHz). Herein, flexible electronics with low Young's moduli, particularly, are regarded as film candidates for CMARs. Finite element modeling (FEM) is applied to demonstrate that CMARs with the polydimethylsiloxane (PDMS) film can achieve the human hearing range 12.44 kHz, which has been verified by characterization of fabricated CMARs, 13.11 kHz. These matched and profound results indicate that low-frequency CMARs may lead to the development of full-implantable CIs in medical and scientific research fields.
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 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".