Selective NMR Pulse Sequences for the Study of Solid Hydrogen‐Containing Fluoropolymers
Bibliographic record
Abstract
Abstract Summary: Fluorine‐19 NMR spectra of solids have some special features, which are discussed in this article. In particular, they generally contain two abundant spin baths (protons and fluorine nuclei). This situation throws up some special operational requirements, as does the study of heterogeneous samples. The relaxation characteristics of heterogeneous systems, which are briefly described herein, frequently permit the use of specific pulse sequences to obtain subspectra for individual components. Various possible selective sequences for use in fluorinated heterogeneous organic solids are listed and their actions rationalized on the basis of molecular mobility. Semicrystalline hydrogen‐containing fluoropolymers form especially suitable systems for such operations, and in order to understand their domain structures it is essential to obtain subspectra of the amorphous and crystalline domains. Examples are given of the use of selective pulse sequences for studying fluoropolymers, especially for poly(vinylidene fluoride) (PVDF) and the copolymer P(VDF75/TrFE25) (TrFE = trifluoroethylene). DIVAM/CP spectra of the vinylidene fluoride/trifluorethylene copolymer as a function of the minipulse angle used. Top: Unfiltered spectrum. Middle: the amorphous domains. Bottom: the crystalline domains. magnified image DIVAM/CP spectra of the vinylidene fluoride/trifluorethylene copolymer as a function of the minipulse angle used. Top: Unfiltered spectrum. Middle: the amorphous domains. Bottom: the crystalline domains.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".