Synthesis and Characterization of Zn doped Li(Li0.21Mn0.54Ni0.125Co0.125)O2 as the Layer Materials For Battery Applications
Bibliographic record
Abstract
In this paper, pure and impure nanopowders of Li(Li0.021Mn0.54Ni0.125Co0.125)O2 were prepared with different percentages (x=0.02%, 0.05%, 0.075%, 0.10%) of Zn impurity by sol-gel method, and the effect of different percentages were investigated on the structural, physical and chemical properties of the samples. These properties of samples characterized by X-ray diffraction (XRD), field-scattering microscopy (FESEM), X-ray energy spectroscopy (EDS), transmission electron microscopy (TEM), thermogravimetric analysis (TGA), differential thermal analysis (DTA), infrared spectroscopy (FTIR), and the results of characterization were investigation. All the reflection peaks indicate that the samples have standard α-NaFeO2 layered structure with the space group R3m, except for the super lattice ordering between 22°-25°.The FESEM images have shown that these nanoparticles have Hexagonal structures for doped and undoped nanopowders. The particle size of nanopowders in the range of 30-80 nm the chemical analysis of EDS has proven the presence of Zn in the samples. TG /DTA measurements showed weight loss in pure and impure of nanopowders. In infrared spectroscopy (FTIR), the connection bonds and chemical elements used in these nanopowders have been investigated.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".