Hydrogel Mesh Size and Its Impact on Predictions of Mathematical Models of the Solute Diffusion Coefficient
Bibliographic record
Abstract
A mathematical model that can provide good predictions of the solute diffusion coefficient in hydrogels would be highly beneficial in designing hydrogels for biomedical and industrial applications, and a number of such models have been derived. Mesh size plays a prominent role in determining the solute diffusion coefficient within a hydrogel. However, in assessing the predictive ability of models derived for this purpose, we have employed various values of the mesh size, i.e., the correlation length or the mesh radius. Herein, a systematic examination of the use of the correlation length or the mesh radius as the mesh size was performed in assessing the predictive quality of four recent models: a semiempirical Cukier hydrodynamic model, an obstruction model, an obstruction-exclusion model, and a combined free volume/obstruction model. The use of the correlation length as the mesh size along with the obstruction model yielded the most consistent agreement between experimental data and model predictions. In contrast, use of the mesh radius did not yield good agreement with the experimental data when used with any of the models.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".