Application of Magnetic Resonance in Lithium- and Sodium-ion Batteries
Bibliographic record
Abstract
Rechargeable battery systems, including Li-ion and Na-ion batteries, form the basis of modern portable technologies and are key to the electrification of 21st century energy utilization. Over the past ∼30 years, magnetic resonance methods have provided critical insight into electrode material structure, electronic structure, and electrolyte dynamics. This chapter aims to cover this research effort by highlighting key findings by research groups around the world, including structure and dynamics studies. Structural analysis by NMR crystallography and density functional theory calculations can correlate electronic structure with NMR properties. We begin with a general introduction on rechargeable batteries, particularly focusing on Li-ion batteries. This is followed by a review of NMR studies of cathode material structure and dynamics, including a discussion of relevant NMR methods for probing ion dynamics. A variety of cathode chemistries as well as both liquid-state and solid-state electrolytes are considered. Finally, the emergence of real-time, operando methods to probe electrochemical lithium insertion and plating mechanisms at the anode is shown to be a powerful contribution of the field of magnetic resonance (both NMR and MRI) to the advancements of rechargeable battery technology.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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".