Nuclear magnetic resonance spectroscopy of rechargeable pouch cell batteries: beating the skin depth by excitation and detection via the casing
Bibliographic record
Abstract
Abstract Rechargeable batteries are notoriously difficult to examine nondestructively, and the obscurity of many failure modes provides a strong motivation for developing efficient and detailed diagnostic techniques that can provide information during realistic operating conditions. In-situ NMR spectroscopy has become a powerful technique for the study of electrochemical processes, but has mostly been limited to laboratory cells. One significant challenge to applying this method to commercial cells has been that the radiofrequency, required for NMR excitation and detection, cannot easily penetrate the battery casing due to the skin depth. This complication has limited such studies to special research cell designs or to ‘inside-out’ measurement approaches. This article demonstrates that it is possible to use the battery cell as a resonator in a tuned circuit, thereby allowing signals to be excited inside the cell, and for them to subsequently be detected via the resonant circuit. Employing this approach, 7Li NMR signals from the electrolyte, as well as from intercalated and plated metallic lithium in a multilayer (rolled) commercial pouch cell battery were obtained. Therefore, it is anticipated that critical nondestructive device characterization can be performed with this technique in realistic and even commercial cell designs.
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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".