Effect of Structural and Electrochemical Properties of Yttrium-doped LiNi0.90Co0.05Al0.05O2 Electrode by Co-precipitation for Lithium Ion-batteries
Bibliographic record
Abstract
In this study, the LiNi0.90−xCo0.05Al0.05YxO2 (x = 0, 0.025, 0.075) have been synthesized by a co-precipitation and solid-state reaction method. The effect of the Y3+-doping on the structural and electrochemical properties were investigated by X-ray diffraction (XRD), scanning electron microscopy (SEM), and by electrochemical and impedance spectroscopy (EIS). From the results of the XRD pattern changes between before and after the doping, less cation mixing and more ordered hexagonal structure were observed for the LiNi0.875Co0.05Al0.05Y0.025O2 cathode and the cell delivered an initial discharge capacity of 195.8 mAhg-1 and was 10.2 mAhg-1 higher than the pristine cell by yttrium doping effect. High rate capability studies were also performed and showed the capacity retention of 95, 81.7 and 63.8 % at 0.2, 1.0 and 5.0 C-rate, respectively during the cycling. The impedance spectra showed that the charge transfer resistance for the pristine cathode grew significantly, while that for the Y3+-doped cathode decreased during cycling. It was concluded that the capacity fading for LiNi0.90Co0.05Al0.05O2 mainly due to the cation mixing, partially contributed by the impedance growth and by doping the pristine material with Y3+, cation mixing can be efficiently suppressed, which results in the improved rate capability.
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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 | 0.000 |
| 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".