Bibliographic record
Abstract
Impedance spectroscopy is widely adopted for probing the charge and charge mobility of soft ion-conducting media, such as synthetic membranes and biological tissue. The spectra exhibit a variety of distinctive signatures, but the physical basis of these is not well understood, e.g. models have not previously accounted for viscoelasticity, hydrodynamics or microstructural heterogeneity. This study explores a physically grounded continuum model that captures how these factors shape conductivity spectra. Nonlinear thermodynamics and linearised dynamics of a viscous electrolyte and compressible, elastic polymer network are coupled under the forcing of an oscillatory electric field. The model is solved in a one-dimensional spatially periodic unit cell, reporting conductivity and dielectric permittivity spectra, including Nyquist representations. Whereas rigid microstructures exhibit ion-diffusion-controlled relaxation, which manifests as a low-frequency dielectric ‘constant’, hydrodynamic and elastic forces contribute to a strongly diverging dielectric permittivity at low frequencies for heterogeneous anionic microstructures. The model also captures distinctive characteristics of experimentally reported impedance spectra for films bearing alternating layers of cationic and anionic charge, again highlighting the role of coupled hydrodynamic, elastic and electrical forces. Sufficiently thin and highly charged bilayers exhibit a notably low high-frequency conductivity. This is explained by strong low-frequency electrostatic polarisation and counter-ion release. The one-dimensional solutions computed herein provide a foundation for much more challenging computations in two and three dimensions.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".