Proton Dynamics of Nafion and Nafion/SiO<sub>2</sub> Composites by Solid State NMR and Pulse Field Gradient NMR
Bibliographic record
Abstract
Proton mobilities in Nafion and Nafion/SiO 2 composites have been studied using high-resolution solid-state MAS NMR. High-resolution solid-state 1 H NMR show that low concentrations of TEOS or short permeation times are necessary to allow complete hydrolysis of TEOS in Nafion. Incomplete hydrolysis of TEOS leaves residual ethyl groups on the surface of silica, which not only reduces the amount of water adsorbed by silica but also blocks the pathway of proton transport in the Nafion/SiO 2 composites. The diffusion coefficients established using PFG NMR show that the best Nafion/SiO 2 composite can be obtained from synthesis with a low concentration of TEOS in a methanol solution. This composite gives a higher diffusion coefficient than pure Nafion under dry conditions, although no differentiation in performance is observed when the membranes are hydrated. 29 Si NMR shows that this composite has a high ratio of Q 3 /Q 4 sites, consistent with a small particle size and many surface hydroxyl groups. Together, these data demonstrate the role of high-surface-area SiO 2 particles in trapping water and building a pathway for structural (Grotthuss mechanism) proton diffusion. Good proton transport under low relative humidity is the holy grail of the PEM-FC community, and this molecular level study shows how conditions can be iteratively optimized to target desirable structure−property relationships.
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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) | 0.001 | 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".