Coarse-Grained Molecular Dynamics Study of Ionomer Aggregation and Network Formation in Dilute Solution
Bibliographic record
Abstract
The presented work comprises a computational study of self-assembly in dilute solution of Nafion-type perfluorosulfonic acid (PFSA) ionomers. We employ coarse-grained molecular dynamics (CGMD) to investigate the formation of ionomer bundles [1] and study the assembly of ionomer bundles into a bundle network upon increasing ionomer concentration. Formation of this supramolecular network is experimentally indicated by a markedly increased viscosity [2]. Evolution of a gel-like structure in the aqueous solutions also studied experimentally by Cirkel et al. [3] and Rubatat et al. [4], revealing more details about transport properties, stability, and water sorption properties of the network structure of interconnected ionomer bundles in Nafion membranes. Simulation of the interconnected, gel-like ionomer state provides opportunities to scrutinize different proposed structural models of ionomer membranes and assess changes in bundle size and network connectivity as functions of backbone hydrophobicity, side chain density, counterion valence, and strength of electrostatic interaction between anionic head groups at ionomer sidechains. References [1] M. Ghelichi, K. Malek and M. Eikerling, Ionomer Self-Assembly in Dilute Solution Studied by Coarse-Grained Molecular Dynamics, Macromolecules 49, 1479-1489 (2016). [2] Private communication. [3] Peter A. Cirkel, Tatsuhiro Okada, A Comparison of Mechanical and Electrical Percolation during the Gelling of Nafion Solutions, Macromolecules 33, 4921-4925 (2000). [4] L. Rubatat, G. Gebel, O. Diat, Fibrillar Structure of Nafion: Matching Fourier and Real Space Studies of Corresponding Films and Solutions, Macromolecules 37, 77727783 (2004).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".