Verdazyl-Based Radicals for High-Field Dynamic Nuclear Polarization NMR
Bibliographic record
Abstract
High-field dynamic nuclear polarization nuclear magnetic resonance (DNP NMR) spectroscopy transfers polarization from unpaired electrons in polarizing agents to nuclei of interest to boost NMR sensitivity. Verdazyl biradicals are a promising choice as polarizing agents because they have been found to generate narrower electron paramagnetic resonance (EPR) signals compared to nitroxide biradicals; an advantageous characteristic for high-field DNP when operating above 400 MHz/263 GHz. The use of verdazyl radicals as DNP polarizing agents has been very limited to date, yet, recent numerical simulations have predicted that verdazyl-nitroxide hybrid biradicals could be more effective polarizing agents than nitroxide-nitroxide biradicals. Herein, the syntheses of a series of verdazyl mono- and biradicals, as well as verdazyl-nitroxide biradicals are described. These radicals were examined in high-field DNP NMR experiments (600 MHz/395 GHz), by measuring 1 H signal enhancements directly and through 13 C{ 1 H} cross-polarization experiments. X-band EPR, 1 H DNP field profiles, and experiments to determine the nuclear build-up times were performed for verdazyl-nitroxide biradicals VerTEMPol and VerTEKol . These hybrid biradicals provide enhancements of up to 100-fold increased signal intensities (i.e., representing >10 4 -fold time savings), approximately four times higher than that of the nitroxide biradical TEKPol, a commonly used polarizing agent in the field.
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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) | 0.001 | 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".