Application of Magnetic Resonance Techniques to the In Situ Characterization of Li-Ion Batteries: A Review
Bibliographic record
Abstract
In situ magnetic resonance (MR) techniques, such as nuclear MR and MR imaging, have recently gained significant attention in the battery community because of their ability to provide real-time quantitative information regarding material chemistry, ion distribution, mass transport, and microstructure formation inside an operating electrochemical cell. MR techniques are non-invasive and non-destructive, and they can be applied to both liquid and solid (crystalline, disordered, or amorphous) samples. Additionally, MR equipment is available at most universities and research and development centers, making MR techniques easily accessible for scientists worldwide. In this review, we will discuss recent research results in the field of in situ MR for the characterization of Li-ion batteries with a particular focus on experimental setups, such as pulse sequence programming and cell design, for overcoming the complications associated with the heterogeneous nature of energy storage devices. A comprehensive approach combining proper hardware and software will allow researchers to collect reliable high-quality data meeting industrial standards.
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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 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".