<sup>1</sup>H Solid-State NMR Study of Nanothin Nafion Films
Bibliographic record
Abstract
The unique behaviors of Nafion nanothin films with thicknesses of 10 nm (ultrathin) and 160 nm (thin) were evaluated using variable-temperature and variable-humidity solid-state 1 H NMR spectroscopy. These unprecedented measurements of nanothin films stacked within an NMR rotor represent a remarkable experimental achievement and demonstrate that 1 H NMR spectroscopy of such minute amounts of ionomer might be possible within active catalyst layers in polymer electrolyte fuel-cell electrodes. This study was motivated by the observation, in a separate work, of thickness-dependent and highly suppressed conductivity in nanothin films of Nafion (4–300 nm) compared to counterpart free-standing Nafion membranes. Trends in the line width and, more precisely, the T 2 relaxation, as probed using a Hahn echo, showed that the local mobility within the hydrogen-bonded domain is equivalent for 10 and 160 nm films and is governed by the fast exchange limit in terms of NMR time scales. Subtle differences in the chemical shift trends provide insight into the domain structures, where the 10 nm films show no changes whereas the thicker 160 nm films exhibit chemical shift trends that indicate a rearranging hydrogen-bonded network. Thus, it is inferred that domain structure formation is influenced by film thickness and that the interaction with the substrate becomes limiting as the film becomes thinner.
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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".