Modulating Grain Boundary Networks to Achieve Superior Chemomechanical Coupling Properties in Nickel-Rich Cathode Materials
Bibliographic record
Abstract
To forge ahead with the next generation of power batteries boasting superior energy density, nickel-rich layered oxides are regarded as some of the most promising cathode materials. However, challenges such as microcracks, which are attributed to the elevated nickel content of the materials, have posed impediments to their further development and application. Consequently, this article focuses on the understanding of the materials in the deep delithiation state, dissecting their degradation mechanisms through a dual lens of electrochemical and mechanical properties. The comprehensive analysis reveals that microcracks within the particles exhibit a degree of reversibility. However, with repeated Li + de-/intercalation, these microcracks progressively propagate and permeate the entire particle, ultimately leading to particle fragmentation. Therefore, this study employs Dy 2 O 3 as an inducer to facilitate the growth of primary crystal grains, reducing the internal porosity of the particles. This effectively enhances the conductivity and lithium-ion diffusion kinetics in deep lithium-ion deintercalation states of nickel-rich cathode materials. The modified material exhibits significant suppression of microcrack formation and growth during cycling, leading to notable improvements in its chemical-mechanical properties. These degradation mechanisms and modification strategies of Ni-rich cathodes offer valuable insights into the development of Ni-rich cathode materials tailored for electric vehicles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".