Study of the Reactions between Ni-Rich Positive Electrode Materials and Aqueous Solutions and their Relation to the Failure of Li-Ion Cells
Bibliographic record
Abstract
The handling of positive electrode active materials must be done carefully due to their propensity to degrade when exposed to ambient atmosphere. The growth of impurities on Ni-rich layered lithium transition metal oxides (LTMOs) is particularly concerning as these materials readily react with H 2 O and CO 2 in atmosphere. The resulting surface impurity species have detrimental effects on the performance of the Li-ion cell and are commonly removed by washing the positive electrode active materials. However, little is understood about the reaction between these materials and aqueous solutions. In this study, LTMOs samples were exposed to acidic and neutral aqueous solutions for various periods of time. The resulting material samples were analysed by X-ray powder diffraction (XRD), thermogravimetric analysis coupled with mass spectrometry (TGA-MS), and by scanning electron microscopy (SEM). The solutions collected after washing were analysed by pH titration and inductively coupled plasma optical emission spectrometry (ICP-OES). From this, we propose two pH-dependent regimes that define the reaction between the positive electrode material and the aqueous solution used for washing. Possible consequences of these reactions on cell performance and lifetime are discussed.
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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) | 0.000 | 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 | 0.000 |
| 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".