The Effect of Lithium Content and Core to Shell Ratio on Structure and Electrochemical Performance of Core-Shell Li<sub>(1+x)</sub>[Ni<sub>0.6</sub>Mn<sub>0.4</sub>]<sub>(1−x)</sub>O<sub>2</sub>Li<sub>(1+y)</sub>[Ni<sub>0.2</sub>Mn<sub>0.8</sub>]<sub>(1−y)</sub>O<sub>2</sub>Positive Electrode Materials
Bibliographic record
Abstract
Core-shell positive electrode materials with a core:shell mass ratio of 2:1 and 4:1 were synthesized in a two-step reaction. Powder X-ray diffraction, SEM and spatial EDS measurements were used to characterize the core and shell phases in the precursors and lithiated products. It was determined using EDS that the precursor and lithiated products are both core-shell and the two phases can be easily resolved with laboratory grade XRD equipment. Two phase Rietveld refinement was completed on the core-shell lithiated products. The results of these refinements in conjunction with contour plots of the lattice parameters within the Li-Ni-Mn oxide layered single phase region were used to position the core and shell of each sample on the Li-Ni-Mn-O phase diagram as a function of the amount of Li 2 CO 3 used in synthesis. The shell phase retained an approximately fixed amount of Li while the Li content of the core phase increased as the overall Li content of the core-shell sample increased. Both the core and shell were electrochemically active. A specific capacity of 220 mAh/g was achieved in a core shell material between 2.5-4.6 V vs. Li/Li + .
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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".