Correlating the Effects of Processing Conditions to Cation Mixing and Performance of an NMC 111 Cathode Material for Lithium Ion Batteries
Bibliographic record
Abstract
Lithium ion batteries have been utilized in a wide range of applications. On-going research and development efforts are expanding the application of these materials for use in electric vehicles and household energy storage solutions. Much of these efforts have focused on improving the performance of lithium ion battery materials through achieving alterations to the composition of the cathode material, which has been seen as the limiting factor in terms of capacity and overall battery lifetime. For example, LiNi1/3Mn1/3Co1/3O2 (often referred to as NMC 111) is becoming a material of focus for commercial-scale production. A significant challenge in synthesizing this material is the propensity for Ni/Li based cation mixing in the octahedral sites of the product. While several studies have correlated the synthetic and processing methods with observations for cation mixing and electrochemical performance of the final material, these studies have not performed an in-depth analysis of the cation mixing phenomenon in situ on a mechanistic level. A detailed investigation is presented on the correlations between the processing conditions (e.g., thermal and compositional) for NMC 111 using a pre-lithiated precursor material. The relationship between cation mixing, sintering temperature and sintering time, as well as potential methods for the reversal of cation mixing were investigated through the use of in situ, variable temperature XRD methods. The importance of this knowledge and determining the ideal processing conditions for NMC 111 (and by extension, cathode materials in general) is further demonstrated through a final analysis of the quality of Li+ distribution in these materials as assessed by microscopy techniques and electrochemical coin cell tests.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".