Dynamic electric-field-induced response of charged spherical colloids in uncharged hydrogels
Bibliographic record
Abstract
Embedding colloidal particles in polymeric hydrogels often endows the polymer skeleton with appealing characteristics for microfluidics and biosensing applications. This theoretical study provides a rigorous foundation for interpreting active electrical microrheology and electroacoustic experiments on such materials. In addition to viscoelastic properties of the composites, these techniques sense physicochemical characteristics of the particle–polymer interface. Wang & Hill ( Soft Matter , vol. 4, 2008, p. 1048) studied the steady response of a rigid, impenetrable sphere in a compressible hydrogel skeleton. Here, we extend their analysis to arbitrary frequencies, showing, in general, how the frequency response depends on the particle size and charge, ionic strength of the electrolyte and elastic and hydrodynamic characteristics of the polymer skeleton. Our calculations capture the transition from quasi-steady compressible to quasi-steady incompressible dynamics as the frequency passes through the reciprocal draining time of the gel. Above the reciprocal draining time, the skeleton and fluid move in unison, so the dynamics are incompressible and, thus, given to an excellent approximation by the well-known dynamic electrophoretic mobility but with the Newtonian shear viscosity replaced by a complex, frequency-dependent value.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".