Slice-Selective NMR Diffusion Measurements - a Tool for the Characterization of Ion Transport Properties in Lithium Ion Battery Electrolytes
Bibliographic record
Abstract
The main impediments to the wide-spread acceptance of electric drive vehicles are the cost, energy storage capacity, and durability of Li-ion batteries. The first issue is best addressed within the context of materials and batteries manufacturing, the second by research on the synthesis and characterization of new materials. Successfully tacking of the third issue requires an improved understanding of battery degradation mechanisms and the development of suitable mitigation measures. In situ experimental techniques which can accurately detect and monitor performance degradation mechanisms at the nanoscale, including the identities of short-lived chemical species, or changes in materials properties as functions of temperature, position or time are emerging only gradually, due to their associated difficulties and complex challenges. We show that the combination of in situ one-dimensional imaging and slice-selective NMR diffusion measurements (Fig. 1) is a tool for spatially and temporally resolving the lithium self-diffusion coefficient in a liquid electrolyte during the application of a dc current (Fig. 2). A solution of 1M LiTFSI in propylene carbonate was used for our method development work.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".