<sup>19</sup>F Double Quantum NMR Spectroscopy: A Tool for Probing Dynamics in Proton-Conducting Fluorinated Polymer Materials
Bibliographic record
Abstract
Solid-state NMR spectroscopy is an important technique for probing the structure and local dynamics of materials at the molecular level. For example, 1 H double quantum (DQ) NMR is a well-established probe of local dynamics. Here, this concept has been extended to characterize fluorinated ionomer materials for the first time. 19 F DQ recoupling NMR experiments are applied to investigate the site-specific local dynamics of the polymer electrolyte material, Nafion 117, under various conditions with respect to temperature and hydration level. The initial rise of the normalized double quantum (nDQ) build-up curves generated from NMR dipolar recoupling experiments is compared as a measure of the motionally averaged 19 F– 19 F dipolar couplings for spectroscopically resolved domains of the polymers. Since the side-chain and backbone fluorines can be distinguished by their chemical shifts, it was possible to demonstrate a difference between the side-chain and backbone local dynamics profiles. The side chain is shown to be more sensitive toward the temperature and relative humidity (%RH) changes, and generally the side chain exhibits greater local dynamics as compared to the hydrophobic backbone, which is consistent with subsegmental motion known as β-relaxation. Elevated temperature and increased relative humidity give rise to increased local dynamics, which is reflected by the slower initial increase of the nDQ build-up curves. This NMR technique has been validated as a comparative analysis tool, suitable for a range of perfluorinated ionomers.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".