A constriction resistance model of conjugated polymer based piezoresistive sensors for electronic skin applications
Bibliographic record
Abstract
Human intervention can be replaced through the development of tools resulting from utilization of sensing devices possessing a wide range of applications including humanoid robots or remote and minimally invasive surgeries. Similar to the five human senses, sensors interface with their surroundings to stimulate a suitable response or action. The sense of touch which arises in human skin is among the most challenging senses to emulate due to its ultra high sensitivity. This has brought forth novel challenging issues to consider in the field of biomimetic robotics. In this work, using a multiphase reaction, a polypyrrole (PPy) based hydrogel is developed as a resistive type pressure sensor with an intrinsically elastic microstructure stemming from three dimensional hollow spheres. It is shown that the electrical conductivity of the fabricated PPy based piezoresistive sensors is enhanced as a result of adding conductive fillers and therefore, endowing the sensors with a higher sensitivity. A semi-analytical constriction resistance based model accounting for the real contact area between the PPy hydrogel sensors and the electrode along with the dependency of the contact resistance change on the applied load is developed. The model is then solved using a Monte Carlo technique and its corresponding sensitivity is obtained. Comparing the results with their experimental counterparts, the proposed modeling methodology offers a good tracking ability.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".