Advanced modeling of AlN-based micromachined energy harvesters driven by β-emitting radioisotopes
Bibliographic record
Abstract
This work presents mathematical modelling of unimorph and bimorph AlN piezoelectric micromachined harvesters utilizing an energetic electron source - amenable to powering miniaturized devices such as MEMS(micro electro mechanical system) sensors. Tritiated silicon, as the energetic electron source, is appropriately aligned under a cantilever structure such that the emitted electrons are trapped by the collecting surface of the cantilever, thereby rendering it negatively charged while the electron emitting surface becomes positively charged. As a result, the attractive electric force causes the cantilever to bend towards the electron emitting surface until it makes contact and is discharged, and thus the cantilever snaps back. The resulting energy from the piezoelectric capacitor is rectified to provide electrical power to MEMS devices. Detailed electromechanical analysis and modelling of unimorph and series and parallel bimorph architectures are presented. Very good agreement between the results of the analytical model and the available experimental findings is demonstrated, thus providing assurance for the optimization study of tritiated silicon radioisotope excited piezoelectric energy harvesters.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".