Bibliographic record
Abstract
With the effects of climate change being felt more and more each day, any form of alternative energy production, no matter how small, must be investigated. Each new transduction mechanism discovered opens up new possibilities of harnessing energy that was previously untapped or underutilized, possibly shifting some of the burden on carbon-based power plants to non-emitting sources. Even if the mechanism is inefficient or unsuitable for energy conversion, the technology may still be useful as a sensor. This thesis examines a recently discovered mechanical-to-electrical transduction mechanism that can be as simple as a water droplet sandwiched between vibrating electrodes. The mechanism in question is analogous to electrostatic transduction where motion is converted to electricity by pulling apart the two plates of a charged capacitor. Conventional technology, however, has plateaued as their performance is limited by the breakdown potential of air. One drawback of using this transduction mechanism is the necessity of having a biasing source, a requirement not shared by electromagnetic and piezoelectric transducers. By utilizing an electrical double layer capacitor’s (EDLC) high capacitance per area and inherent self-biasing, performance can be improved and one disadvantage can be avoided. In this work, we demonstrate such a device using a droplet of water between two Indium Tin Oxide (ITO) electrodes with one electrode being coated with poly[4,5-difluoro-2,2-bis(trifluoromethyl)-1,3-dioxole-co-tetrafluoroethylene] (PTFE-AF). We investigate its frequency response, an important parameter for sensing and generation applications, and show an improved frequency response of up to 100 Hz, surpassing literature’s best, with a maximum peak-to-peak voltage of 892 mV. We present a linear approximation model that can be used for further optimization of such a system and correctly predicts the point of maximum power transfer. We also investigate how and why the technology self-biases, proposing an alternative theory to those posed in literature. We finally evaluate the system as both a sensor and generator in its current state and ideas that could make this technology competitive.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".