Electric-field-induced displacement of charged spherical colloids in compressible hydrogels
Bibliographic record
Abstract
This paper concerns the electric-field-induced displacement of a charged spherical colloid embedded in an uncharged compressible hydrogel. Previous theoretical calculations for incompressible polymer skeletons predict sub-nanometre particle displacements within the experimentally accessible parameter space (e.g., particle surface charge density, polymer shear modulus, and electric field strength). Accordingly, the prevailing expectation is that an experimental test of the theory would be extraordinarily difficult. In this work, however, we solved the electrokinetic model for compressible polymer skeletons with arbitrary Poisson's ratio. The most striking result, obtained from numerically exact solutions of the full model and an analytical boundary-layer approximation, is that polymer compressibility admits particle displacements that increase linearly with particle size when the radius is greater than the Debye length. This scaling is qualitatively different than previously obtained for incompressible skeletons, where the ratio of the particle displacement to the electric field approaches a particle-size-independent constant. The displacement is also much more sensitive to the hydrodynamic permeability of the polymer skeleton. Therefore, when compressible hydrogels are deformed at frequencies below their reciprocal draining time, our theory identifies the parameter space where displacements could be registered using optical microscopy. In turn, this will help to establish a quantitative connection between the electric-field-induced particle displacement and physicochemical characteristics of the particle-polymer interface.
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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 | 0.001 |
| 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".