The refocused discrimination induced by variable amplitude minipulses (DIVAM) experiment — Improved domain selection in semicrystalline fluoropolymers by <sup>19</sup>F solid state nuclear magnetic resonance spectroscopy
Bibliographic record
Abstract
The discrimination induced by variable amplitude minipulses (DIVAM) filter can be tuned to select for signals from a particular domain, therefore it is possible to obtain signals specific to different domains using only one experiment. An early description of the DIVAM sequence, where the filter terminates with cross-polarization, explains this tune ability using a simple one-spin-relaxation model, thereby limiting the selection mechanism to incoherent processes. Recently, a more complete description of the selection behaviour was offered for the DIVAM filter, when it was directly applied to the observed nucleus (direct DIVAM), taking into account both the incoherent and coherent terms. Direct DIVAM experiments on poly(vinylidenefluoride) (PVDF) show significant phase distortions when large excitation angles were used. The signal from the amorphous domain is seen to nutate in a normal fashion with respect to the excitation angle, while those from the crystalline and defect units did not. The refocused DIVAM sequence is introduced to restore normal nutation for all signals. The selection behaviour is investigated using SIMPSON (simulation program for solid-state nuclear magnetic resonance (NMR) spectroscopy) simulations. These illustrate that the isotropic shift terms have been effectively removed and the dipolar term attenuated, such that the chemical shift anisotropy (CSA) leads to domain selection; however, in a different manner than seen in direct DIVAM. Therefore, this sequence provides a method to select on the basis of the CSA term in the presence of both strong dipolar couplings and a large range of isotropic shifts.
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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".