A multisample 7 T dynamic nuclear polarization polarizer for preclinical hyperpolarized MR
Bibliographic record
Abstract
Dynamic nuclear polarization (DNP) provides the opportunity to boost liquid state magnetic resonance (MR) signals from selected nuclear spins by several orders of magnitude. A cryostat running at a temperature of ~ 1 K and a superconducting magnet set to between 3 and 10 T are required to efficiently hyperpolarize nuclear spins. Several DNP polarizers have been implemented for the purpose of hyperpolarized MR and recent systems have been designed to avoid the need for user input of liquid cryogens. We herein present a zero boil‐off DNP polarizer that operates at 1.35 ± 0.01 K and 7 T, and which can polarize two samples in parallel. The samples are cooled by a static helium bath thermally connected to a 1 K closed‐cycle 4He refrigerator. Using a modified version of the commercial fluid path developed for the SPINlab polarizer, we demonstrate that, within a 12‐minute interval, the system can produce two separate hyperpolarized 13C solutions. The 13C liquid‐state polarization of [1‐13C]pyruvate measured 26 seconds after dissolution was 36%, which can be extrapolated to a 55% solid state polarization. The system is well adapted for in vitro and in vivo preclinical hyperpolarized MR experiments and it can be modified to polarize up to four samples in parallel.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".