Studies of the SEI layers in Li(Ni<sub>0.5</sub>Mn<sub>0.3</sub>Co<sub>0.2</sub>)O<sub>2</sub>/Artificial Graphite Cells after Formation and after Cycling
Bibliographic record
Abstract
Li(Ni0.5Mn0.3Co0.2)O2/artificial graphite cells containing different electrolyte additives were studied using electrochemical impedance spectroscopy (EIS) and X-ray photoelectron spectroscopy (XPS) after formation and after long-term charge-discharge cycling. Positive and negative electrodes were examined separately in symmetric cells to study the solid electrolyte interphase (SEI) at each electrode. EIS measurements were taken vs temperature, and activation energies (Ea) related to Li+ transport through the SEI were calculated. After cycling, Ea differed depending on electrolyte additive, electrode type, and cycling voltage limits. Charge transfer resistance was also compared after formation and cycling and did not always correlate with Ea trends, suggesting that multiple factors influence SEI properties. XPS was used to study the chemical composition and thickness of the SEI. Electrolyte additives affected the quantity of inorganic materials in the SEI, and more inorganic material appeared to correlate with lower Ea values. Cells containing lithium difluorophosphate electrolyte additive had the best lifetime of the cells studied in this work. These cells also showed the lowest SEI activation energy values, lowest charge transfer resistance, and most inorganic SEI composition after cycling.
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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".