Effect of Metal Composition on the Electrochemical Properties of Lithium-Rich Positive Electrode Materials
Bibliographic record
Abstract
Introduction Lithium-rich mixed transition metal (TM) oxide positive electrode materials such as Li[Li0.2Ni0.2Mn0.6]O2 and Li[Li0.2Mn0.54Ni0.13Co0.13]O2 are attractive due to their high reversible capacities. However, they suffer from some problems such as high irreversible capacity loss1, poor rate capability2 and voltage fade3. There is no clear understanding in the literature how the overall metal composition of the Li-rich material affects their electrochemical properties including the above mentioned issues. Hence we embark, in this study, on studying the effect of metal composition on the electrochemical properties of the Li-rich positive electrode materials. Experiment A series of Ni(II)aMn(II)bCo(II)cCO3 precursors where a + b + c = 1 were made with co-precipitation synthesis using a continuously-stirring tank reactor (CSTR). Ni(II)aMn(II)bCo(II)cCO3 precursors were mixed with required amounts of Li2CO3and made into Li-rich positive electrode materials of the desired composition using solid-state synthesis at 900ᵒC in air. All the prepared materials were characterized by X-ray diffraction and their true densities were measured using a helium pycnometer. Coin-type cells were made from the synthesized positive electrode materials and Li metal anodes, which were then electrochemically tested under galvanostatic conditions at constant temperature, and their electrochemical properties were compared. Results and Discussion A Li-rich positive electrode material comprised of Li, Ni2+, Mn4+ and Co3+ can be made from a Ni(II)aMn(II)bCo(II)cCO3 precursor. By knowing the exact composition of Ni(II)aMn(II)bCo(II)cCO3 precursor, the theoretical formula of a Li-rich material can be calculated. For example, the Ni0.25Mn0.75 composition can be used to make Li1.2Ni0.2Mn0.6O2. Thus, the Ni-Mn-Co compositions that can be used to make all the possible Li-rich positive electrode materials were determined. Details results showing how the electrochemical behavior varies with overall metal composition will be presented. References 1. J. H. Kim, C. W. Park, and Y. K. Sun, Solid State Ionics, 164, 43 (2003). 2. B. Xu , C. R. Fell , M. Chi , Y. S. Meng, Energy Environ. Sci.2011, 4 , 2223 3. Debasish Mohanty, Athena S. Sefat, Jianlin Li, Roberta A. Meisner, Adam J. Rondinone, E. Andrew Payzant, Daniel P. Abraham, David L. Wood, Claus Daniel, Phys.Chem.Chem.Phys., 2013, 15, 19496--19509
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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 | 0.001 | 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".