Ultrafast Inside‐Out NMR Assessment of Rechargeable Cells
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Rechargeable battery cells are notoriously difficult to analyze. Conductive casings and the close spacing between electrode layers prevent the penetration of radiofrequency into the active compartment, and thus preclude direct nuclear magnetic resonance studies of cells unless they are specifically designed for such studies. Recently, an inside‐out magnetic resonance imaging (MRI) method was developed that allowed measuring the magnetic field distributions in the volume surrounding the cells, and inferring internal parameters, such as the state of charge and current distributions. While the imaging approach provides a potentially very detailed picture of internal mechanisms, it can often be sensitive to background gradients and can be slow. In this work, an alternative approach is presented, which is based on the acquisition of free induction decays in the sample volume surrounding the cells. The signals encode intrinsic battery properties via the induced magnetic fields from the battery materials. A large range of cells were studied with different cathode materials, electrolyte amounts and cycle numbers (age). The spectroscopic signatures from these studies are shown to provide strong classification power for cathode materials. In addition, the derived principal components follow distinct pathways as a function of state of charge. The method is simple and fast (completes in less than a second), and requires only minimal hardware.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 it