Proton friction and diffusion coefficients in hydrated polymer electrolyte membranes: Computations with a non-equilibrium statistical mechanical model
Bibliographic record
Abstract
A recently derived mathematical model to compute the effective friction and diffusion coefficients of hydronium ions in hydrated polymer electrolyte membranes is described and tested for dependence on membrane-specific parameters. Contributions to the friction coefficient due to water–polymer, water–hydronium, and hydronium–polymer interactions are determined through computation of force–force correlation functions. The conventional Stokes law friction coefficient of the hydronium ion in bulk water is then “corrected” with these statistically derived contributions and the corresponding diffusion coefficient calculated. For a Nafion® membrane pore with an hydration level of six water molecules per sulfonic acid functional, the model was used to compute friction coefficients for various distributions of the fixed sites, and for different side chain lengths. The model showed substantial sensitivity to these parameters and predicted that for pores of fixed volume and a constant total number of sulfonate groups, the friction on the hydrated proton is the greatest for distributions with high local anionic charge density. In a second series of computations where the radius and length of the pore were varied, the model demonstrated that the proton diffusion increases with increasing channel diameter. These calculations, therefore, demonstrate the important predictive capability of this molecular-based, nonequilibrium statistical mechanical model.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".