Transmission line circuit model of a PPy based trilayer mechanical sensor
Bibliographic record
Abstract
ABSTRACT Many efforts have been devoted to modeling the diffusive impedance of conjugated polymer (CP) based actuators using their equivalent electrical circuits. Empl oying the same methodology, CP based mechanical sensors can also be treated by an equivalent transmission line circuit and their overall impedance can be modeled, correspondingly. Due to the large number of resources to study the electrical circuits, this technique is a practical tool. Therefore, in this study, an equivalent RC-circuit model including electrochemical parameters is determined to obtain a better perception of the sensing mechanism of these mechanical sensors. Conjugated polymers are capable of generating an output current or voltage upon an induced mechanical deformation or force. This observed behavior in polymer based mechanical sensors is considered as the reverse actuation process. Many outstanding properties of the conjugated polymer actuators including their light weight and biocompatibility are still retain ed by these sensors. Sensors with a trilayer configuration are capable of operating in air in response to a mechanically induced bending deformation. However, due to their nonlinear behavior and multivariable characteristics, it is required to propose a systematic approach in order to optimize their performance and gain the optimal values of their constituent decision variable. Therefore, the proposed mathematical model is used to define the output voltage of the PPy based mechanical sensor along with the sensitivity of the model to the applied frequency of the induced deformation. Applying a multiobjective optimization algorithm, the optimization problem was solved and the tracking ability of the proposed model was then verified. Keywords: Polypyrrole based mechanical sensors, Multiobjective optimization, Electroactive polymers, and Transmission line circuit model
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".