Studying The Impact of Electrolyte, Li Excess, NMC Blending, and Cycling Conditions on The Lifetime and Degradation of LMO/AG Cells Using UHPC Cycling, XRF, and Isothermal Microcalorimetry
Bibliographic record
Abstract
The impact of electrolyte, Li excess, NMC blending, and cycling conditions on the performance of Li 1+x Mn 2-x O 4 (LMO)/Artificial Graphite (AG) cells was studied using ultra-high precision coulometry (UHPC), X-ray fluorescence (XRF), and isothermal microcalorimetry (IMC). Decreasing the Li excess resulted in severe capacity fade which was greatly improved by blending LMO with NMC622. The known synergy between NMC and LMO is electrolyte-dependant and was more significant at elevated temperatures. We showed with XRF that Mn deposition on the negative electrode occurs primarily during the early cycles and is reduced by increasing the Li excess in LMO or by blending with NMC622. IMC experiments demonstrates a correlation between parasitic heat flow and Mn loading on the negative electrode and gas generation. Finally, LiFSI co-salts were examined to suppress Al corrosion while retaining the beneficial role of LiFSI in improving cell performance.
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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 | 0.000 | 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".