Electron-to-nuclear spectral mapping via dynamic nuclear polarization
Bibliographic record
Abstract
We report on a strategy to indirectly read out the spectrum of an electronic spin via polarization transfer to nuclear spins in its local environment. The nuclear spins are far more abundant and have longer lifetimes, allowing for repeated polarization accumulation in them. Subsequent nuclear interrogation can reveal information about the electronic spectral density of states. We experimentally demonstrate the method by reading out the ESR spectrum of nitrogen vacancy center electrons in diamond via readout of lattice 13C nuclei. Spin-lock control on the 13C nuclei yields a significantly enhanced signal-to-noise ratio for the nuclear readout. Spectrally mapped readout presents operational advantages in being background-free and immune to crystal orientation and optical scattering. We harness these advantages to demonstrate applications in underwater magnetometry. The physical basis for the "one-to-many" spectral map is itself intriguing. To uncover its origin, we develop a theoretical model that maps the system dynamics, involving traversal of a cascaded structure of Landau-Zener anti-crossings, to the operation of a tilted "Galton board." This work points to new opportunities for "ESR-via-NMR" in dilute electronic systems and in hybrid electron-nuclear quantum memories and sensors.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".