Pulse sequence and sample formulation optimization for dipolar order mediated <sup>1</sup>H→<sup>13</sup>C cross-polarization
Bibliographic record
Abstract
We have recently demonstrated the use of contactless radiofrequency pulse sequences under dissolution-dynamic nuclear polarization conditions as an attractive way of transferring polarization from sensitive 1H spins to insensitive 13C spins with low peak radiofrequency pulse powers and energies via a reservoir of dipolar order. However, many factors remain to be investigated and optimized to enable the full potential of this polarization transfer process. We demonstrate herein the optimization of several key factors by: (i) implementing more efficient shaped radiofrequency pulses; (ii) adapting 13C spin labelling; and (iii) avoiding methyl group relaxation sinks. Experimental demonstrations are presented for the case of [1-13C]sodium acetate and other relevant molecular candidates. By employing the range of approaches set out above, polarization transfer using the dipolar order mediated cross-polarization radiofrequency pulse sequence is improved by factors approaching ∼1.65 compared with previous results. Dipolar order mediated 1H→13C polarization transfer efficiencies reaching ∼76% were achieved using significantly reduced peak radiofrequency pulse powers relative to the performance of highly sophisticated state-of-the-art cross-polarization methods, indicating 13C nuclear spin polarization levels on the order of ∼32.1% after 10 minutes of 1H DNP. The approach does not require extensive pulse sequence optimization procedures and can easily accommodate high concentrations of 13C-labelled molecules.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".