Stress‐Sensor Device Based on Flexoelectric Liquid Crystalline Membranes
Bibliographic record
Abstract
Membrane flexoelectricity is an electromechanical coupling process that describes membrane bending and membrane electrical polarization caused by bending under electric fields. In this paper we propose, formulate, and characterize a stress-sensor device for mechanically loaded solids, consisting of a soft flexoelectric thin membrane attached to the loaded deformed solid. Because the curvature of the deformed solid is transferred to the attached flexoelectric membrane, the electromechanical transduction of the latter produces a charge that is proportional to the stress of the solid. The model of the stress-sensor device is based on the integration of the thermodynamics of polarizable membranes with isotropic solid elasticity, leading to a transfer function that identifies the elastic, electromechanical, and geometrical parameters involved in electrical-signal generation. The model is applied to representative normal bending and then to more complex off-axis bending of elastic bars. In all cases, a common transfer function shows the generic material and its geometric contributions. The sensor sensitivity increases linearly with flexoelectricity and the membrane-solid interface, and the sensitivity decreases with increasing membrane thickness and Young's modulus of the solid. The theoretical results contribute to ongoing experimental efforts towards the development of anisotropic soft-matter-based stress-sensor devices through solid-membrane interactions and electromechanical transduction.
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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 | 0.001 |
| Open science | 0.001 | 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".