Self-powered monolithic accelerometer using a photonic gate
Bibliographic record
Abstract
Harvesting sustainable energy resources from surrounding environments to power small electronic devices and systems has attracted massive research attention. Herein, we develop a novel technology to harvest light energy to self-power and simultaneously sense mechanical acceleration in a monolithic structure. When the photonic gate is illuminated the operation mode of the device changes from conventional mode to light harvesting and self-powered operation. The light illumination provides a gradient of majority carrier concentration on the top semiconductor layer, generating a lateral photovoltage, which is the output voltage of the sensor. Under acceleration, the mechanical inertial force induces stress in the sensor material leading to the change of mobility of the charge carriers, which shifts their diffusion rate, and hence changes the gradient of the majority carriers and the lateral photovoltage. The sensitivity at 480 lx light illumination was measured to be 107 μV/g, while it was approximately 30 μV/g under the ambient light illumination without any electrical power source. In addition, the acceleration sensitivity is tunable by controlling parameters of the photonic gate such as light power, light spot position and light wavelength. The integration of sensing and powering functions into a monolithic platform proposed in this work eliminates the requirement of external power sources and offers potential solutions for wireless, independent, remote, and battery-free sensing devices and systems.
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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 | 0.000 |
| 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 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".