Slice-Selective NMR Diffusion Measurements: A Robust and Reliable Tool for In Situ Characterization of Ion-Transport Properties in Lithium-Ion Battery Electrolytes
Bibliographic record
Abstract
The main impediments to the widespread acceptance of electric drive vehicles are the cost, energy-storage capacity, and durability of portable electrical energy sources and, in particular, batteries. In situ experimental techniques that can accurately detect and monitor performance degradation mechanisms on the nanoscale, including the identities of short-lived chemical species and changes in materials properties as a function of cycling rate, temperature, or time, are not widely used. Herein we demonstrate the combination of in situ 1D imaging and slice-selective NMR diffusion measurements as a tool for the spatially and temporally resolved determination of lithium diffusivities in a conventional liquid electrolyte (1.0 M lithium bis(trifluoromethanesulfonyl)imide solution in propylene carbonate) under application of a constant electrical current. All experiments were carried out using standard NMR equipment, so the proposed technique can be easily implemented in any modern R&D facility.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".