High Precision Coulometry Studies of Single-Phase Layered Compositions in the Li-Mn-Ni-O System
Bibliographic record
Abstract
Positive electrode materials which do not react with electrolyte at high potentials (≥ 4.6 V vs. Li/Li + ) are essential for developing Li-ion batteries with high energy densities and long cycle lives. Reactions with electrolyte can be detected using precise measurements of coulombic efficiency (CE) and charge end point capacity slippage. Three single-phase layered compositions in the Li-Mn-Ni-O system, Li[Li 0.16 Ni 0.12 Mn 0.65 □ 0.07 ]O 2 , Li[Li 0.12 Ni 0.32 Mn 0.56 ]O 2 , and Li[Li 0.09 Ni 0.46 Mn 0.45 ]O 2 were studied by high precision coulometry at upper potential limits of 4.6 V and 4.8 V. When cycled to 4.6 V, Li[Li 0.16 Ni 0.12 Mn 0.65 □ 0.07 ]O 2 had a reversible capacity of 225 mAh g −1 after 50 cycles, and maintained a substantially higher CE and a lower charge end point capacity slippage per cycle than Li[Li 0.12 Ni 0.32 Mn 0.56 ]O 2 , Li[Li 0.09 Ni 0.46 Mn 0.45 ]O 2 , and industry standard Li[Ni 1/3 Mn 1/3 Co 1/3 ]O 2 (cycled to only 4.2 V). Overall, these results highlight the inherent "inertness" of Li[Li 0.16 Ni 0.12 Mn 0.65 □ 0.07 ]O 2 and its suitability as a thin protective shell in a core-shell particle configuration.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".