Ultrafast Inside‐Out NMR Assessment of Rechargeable Cells
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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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 | 0.000 |
| 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".