Integration of a torsion-based shear-mode energy harvester and energy management electronics for a sensor module
Bibliographic record
Abstract
This work demonstrates the ability of a torsion-based shear-mode energy harvester to power a sensor module by integrating a temperature sensor circuit with a purpose developed piezoelectric energy harvester. A 10-cm 3 energy harvester was developed for this application and was found to produce over 200 µW of maximum power through an optimal load resistance under 0.25 g pk acceleration excitation at its resonant frequency of 237 Hz. This harvester was then tested with two interface circuits: a standard interface diode bridge rectifier and a nonlinear synchronous electrical charge extraction circuit that were compared for their suitability in powering the sensor module. Through this, the synchronous electrical charge extraction nonlinear conditioning circuit was found to have superior performance when charging a capacitor and with DC loads at low voltages and was capable of providing a maximum power output of 37 µW under 0.25 g pk acceleration at 237 Hz. This output power was then used to successfully power a temperature sensor module consisting of a temperature sensor, a microcontroller, and a radio-frequency identification memory chip at a sensing frequency of 0.5 Hz.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".