How to Measure Solid State Lithium-ion Diffusion using the Atlung Method for Intercalant Diffusion
Bibliographic record
Abstract
A systematic investigation of the factors that affect lithium diffusion coefficient measurements in the Atlung Method for Intercalant Diffusion (AMID) is carried out. Single crystal LiNi0.6Mn0.2Co0.2O2 is used for method development. The factors include electrode mass loading, separator thickness, electrolyte solvent choice and salt molarity, voltage interval sizes, open circuit relaxation time, C-rate choice, and charge vs discharge direction. In an ideal diffusivity measurement method, none of these factors listed should impact the true material diffusivity since it should be a purely material property. However, as we demonstrate here, all these experimental parameters should be selected carefully to minimize cell and electrode resistance contributions so that one can measure true lithium diffusivity in a material that is independent of current direction. We propose a low electrode loading cell design that is applicable for all diffusion measurement methods consisting of an ultrathin electrode coating (0.2 mAh cm−2), thin separator and 1.5 M LiPF6 ethylene carbonate: dimethyl carbonate 1:1 electrolyte. Additionally, we show diffusivity is not dependent on charge or discharge direction in NMC622, and we compare AMID results to Galvanostatic Intermittent Titration. Specific to AMID 15 min OCV time with 0.1 V intervals are suitable for this measurement.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".