Bibliographic record
Abstract
Synthetic and biological fl exoelectric membranes are actuators that bend under the action of external electric fi elds, a phenomenon of interest to the development of emerging adaptive materials as well as biological mechano-transduction.This paper presents an actuator model of fl exoelectric membranes based on a Helmholtz free energy that incorporates tension, bending, and torsion as well as polarization and dielectric energies.The electro-elastic components of the membrane tension, moment tensor and tensor are derived and used to construct an actuator model that includes dissipation due to viscous fl uids in contact with the membrane.The actuator model is expressed by a balance between the externally imposed electric forces, the viscous dissipation of the contacting fl uid phases, and the elastic storage of the membrane.The nonlinearity is shown to originate in the viscous dissipation.The model is analyzed for externally imposed oscillating electric fi elds.The Deborah number De given by the ratio of driving frequency and the resonant frequency is shown to control the viscoelastic response.The key fi ndings are: (i) for De 1 the response is purely elastic and the electric energy is stored in the elastic deformations of the membrane; (ii) at larger De, the response is anharmonic and viscoelastic; (iii) due to the nature of the viscous nonlinearity only even harmonics are generated in the response; and (iv) secondary resonant frequencies appear at lower driving frequencies.These fi nding contribute towards the emerging understanding of fl exoeletricity in biological membranes, pioneered by Petrov and co-workers (Petrov, A.G., The Lyotropic State of Matter, Gordon and Breach Science Publishers: Amsterdam, 1999).
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".