Steady electrodiffusion in hydrogel-colloid composites: macroscale properties from microscale electrokinetics
Bibliographic record
Abstract
A rigorous microscale electrokinetic model for hydrogel-colloid composites is adopted to compute macroscale profiles of electrolyte concentration, electrostatic potential, and hydrostatic pressure across membranes that separate electrolytes with different concentrations. The membranes are uncharged polymeric hydrogels in which charged spherical colloidal particles are immobilized and randomly dispersed with a low solid volume fraction. Bulk membrane characteristics and performance are calculated from a continuum microscale electrokinetic model (Hill 2006b, c). The computations undertaken in this paper quantify the streaming and membrane potentials. For the membrane potential, increasing the volume fraction of negatively charged inclusions decreases the differential electrostatic potential across the membrane under conditions where there is zero convective flow and zero electrical current. With low electrolyte concentration and highly charged nanoparticles, the membrane potential is very sensitive to the particle volume fraction. Accordingly, the membrane potential--and changes brought about by the inclusion size, charge and concentration--could be a useful experimental diagnostic to complement more recent applications of the microscale electrokinetic model for electrical microrheology and electroacoustics (Hill and Ostoja-Starzewski 2008, Wang and Hill 2008).
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".