A magnetic resonance and electrochemical study of the role of polymer mobility in supporting hydrogen transport in perfluorosulfonic acid membranes
Bibliographic record
Abstract
Perfluorosulfonic acid (PFSA) materials have been used in polymer electrolyte membrane fuel cells (PEMFCs) as electrolyte materials due to their mechanical durability and high proton conductivity. To understand the fundamental chemistry at a molecular level in material performance properties, we have developed and validated method for evaluating local dynamics using 19F double-quantum solid-state nuclear magnetic resonance (ssNMR) spectroscopy. The local dynamics information can be separated and analyzed in terms of fluorine interactions with respect to the different temperatures and hydration levels. The polymer side chain is proven to be more locally mobile which is reflected by the lower apparent dipolar coupling constant (Dapp) compared to the backbone. This observation agrees with the micro-phase separation morphology evolution. In the current study, different types of PFSA materials were explored and compared. The dynamics investigation of the PFSA materials has been conducted at various conditions. In operando membrane performance analyses were performed in parallel at Ballard Power Systems. PFSA membranes were prepared into membrane electrode assemblies (MEAs), with catalyst layers and gas diffusion layers. From the cyclic voltammetry measurements, the H2 crossover values were extracted. These data reveal a strong correlation between the proton conductivity and the site-specific PFSA side chain local dynamics. Moreover, a correlation was drawn between increasing side chain mobility (lower Dapp), and increased H2 permeability. The link between the fundamental dynamics study and this key PFSA performance analysis provides insight into proton transport mechanisms.
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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 | 0.000 |
| 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".