The Direct DIVAM Experiment: A Spin Dynamics Analysis
Bibliographic record
Abstract
Domain selection in polymer NMR is limited to experiments specifically suited to each structural domain owing to its particular spin dynamics and relaxation properties. The DIVAM experiment can be tuned to select for signal from the domain of interest, making it possible to obtain signals specific to different domains using only one experiment. An early description of this sequence explains this tunability using a simple one-spin-relaxation model, thereby limiting the selection mechanism to incoherent processes and thus ignoring the coherent terms such as chemical shift anisotropy (CSA), dipolar coupling and offset terms. Experiments have shown that when the DIVAM sequence is applied directly to the nucleus of interest, referred to as direct DIVAM (DD), transient behavior is observed in the signal intensity on the sample spinning time scale. This indicates that the coherent terms are involved in the selection process; the exact role of these terms is explored in this work. SIMPSON simulations illustrate that the CSA and offset terms can play a dominant role in domain selection; however, the dipole term was relatively ineffective and required large values before substantial selection was predicted. Using a one-spin-relaxation model, which now includes a chemical shift evolution term, an analytical expression for the signal intensity was provided as a function of interpulse delay (tau), excitation angle (theta), relaxation time (T2), and offset frequency (Deltanu). These indicate that the selection behavior varies substantially with differing time scales and excitation angles. For small angles and long delay times DD behaves primarily as a relaxation filter, whereas for larger angles and short delay times the coherent terms take over dominated by the CSA interactions. The DD sequence can therefore be set to select on the basis of the transverse relaxation rate or the strength of the CSA interaction, depending on the excitation angle used.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".