Insights into Fast-Charge-Induced Cracking and Bulk Structural Deterioration of Ni-Rich Layered Cathodes for Lithium-Ion Batteries
Bibliographic record
Abstract
Ni-rich NMCs (lithium nickel manganese cobalt oxides) have been extensively utilized as a type of cathode material for Li-ion batteries due to their high energy density and cost efficiency. Meeting the increasing consumer demand for fast charging has become an urgent priority within the industry. Studies on structural degradation caused by fast charging, however, remain limited, especially concerning the understanding of the inter-related failure phenomena and mechanisms. In this study, our results reveal that prolonged fast-charge cycling of an NMC811 cathode leads to significant crack growth at both nano- and microscales. The rapid propagation of cracks from the particle interior to the surface significantly accelerates electrolyte infiltration, leading to the formation of the cathode electrolyte interphase inside NMC811 particles. A fatigue-cracking mechanism based on Li concentration gradients is proposed to be the root cause of crack formation. Additionally, electron energy loss spectroscopy and X-ray diffraction analysis provide direct evidence of irreversible Li loss and layered structure distortion in the bulk material of fast-charged NMC811, which contributes to the significant capacity loss of the NMC811 cathode after fast-charge cycling. The intragranular transition metal/Li cation mixing is also observed within the particle interior of fast-charged NMC811, which further deteriorates the material. This study offers valuable insights into the structural challenges encountered by Ni-rich NMC cathodes under fast-charge conditions, providing a foundational framework for designing strategies to enhance their structural integrity and electrochemical performance in demanding applications.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".