Stabilizing LiNi<sub>0.8</sub>Co<sub>0.15</sub>Mn<sub>0.05</sub>O<sub>2</sub> Cathode by Doping Sulfate for Lithium‐Ion Batteries
Bibliographic record
Abstract
Abstract Residual sulfate (SO42−) in precursor Ni0.8Co0.15Mn0.05(OH)2 (pre‐NCM) is commonly regarded as being harmful to Li[Ni0.8Co0.15Mn0.05]O2 (NCM) performance, leading to significant performance losses and also hampering the electrode fabrication. Therefore, manufacturers try their best to lower sulfate contents in pre‐NCM. However, how the sulfate affects the cathode materials is not systematically studied. To address these issues, NCM was synthesized with different amounts of intentionally added sulfate (NH4)2SO4 in pre‐NCM. It was demonstrated that anionic SO42− doped in NCM could influence the grain size in sintering process and stabilize the layer structure during the charge–discharge process at a certain doping amount. The first‐principles calculations suggested that the SO42− doped in the transition metal layer could effectively facilitate Li+ diffusion in the lattice. SO42− doping could reduce the energy barrier for Li+ migration and then suppress drastic contraction of the c axis during cycling. The phase transition of H2 to H3 caused by c axis contraction was suppressed and the cycling performance was enhanced. The capacity retention could reach 80.9 (0.2 C) and 80.4 % (1 C) after 380 and 240 cycles in coin cells, respectively. These findings illustrate that a certain amount of sulfate could be beneficial to NCM cathodes.
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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) | 0.001 | 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".