Modeling and electromechanical performance analysis of polyvinylidene difluoride/textile‐system for energy harvesting from the human body toward a novel class of self‐powered sensors
Bibliographic record
Abstract
Abstract Because batteries' ability to power portable and wireless devices is limited, a great deal of research has been conducted on energy harvesting technology as a self‐power source for portable devices. Harvesting mechanical energy from human motion is an attractive approach to obtaining green and renewable energy by converting wasted ambient energy to electrical power. Hence, integrating an energy harvester into shoes is among the most interesting possibilities for harvesting energy from the body. In this paper, we explore the effect of flexible substrate mechanical parameters on the energy harvesting capability of a flexible piezoelectric film. Analytical modeling and simulations of a smart structure composed by polyvinylidene fluoride film stuck on three different textiles substrates have been done to evaluate the ability of this approach to harvest energy and its application within shoes to transform mechanical energy generated by the foot while walking into electrical energy. The maximum harvested power of the polyvinylidene difluoride film under 200 N compressive force was found to be 5.37 μW on a Cotton textile, which is a potentially useful amount of energy.
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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 | 0.001 | 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".