Measurements of Interdiffusion Coefficients of Transition Metals in Layered Li–Ni–Mn–Co Oxide Core–Shell Materials during Sintering
Bibliographic record
Abstract
Core–shell structure positive electrode materials, based on layered Li–Ni–Mn–Co oxides, could be the next generation of positive electrode materials for high energy density lithium-ion batteries. Diffusion of the transition metal cations between the core and shell phases occurs during sintering, which can significantly affect the final core–shell (CS) properties. However, the interdiffusion constants have never been measured. Laminar pellets of the pure core phase and pure shell phase were pressed in contact and then heated to measure the interdiffusivity of the transition metals at various temperatures. The diffusion couples Ni 3+ /Co 3+, Co 3+ /Mn 4+, and Ni 3+ /Mn 4+ were measured by analyzing composition versus position, with respect to the initial interface between the core and shell phase pellets. The transition metal composition profiles were measured with energy dispersive spectroscopy (EDS) line scans. This is the first time interdiffusion constants have been reported in the layered cathode materials to our knowledge. At 900 °C, Ni 3+ /Co 3+ has the highest interdiffusivity, D, of ∼4.7 × 10 –12 cm 2 /s, while Ni 3+ /Mn 4+ has the lowest of ∼0.1 × 10 –12 cm 2 /s. The activation energy barriers for Ni 3+ /Co 3+, Co 3+ /Mn 4+, and Ni 3+ /Mn 4+ interdiffusion were determined from Arrhenius plots of D vs 1/ T . Simulations of diffusion in spherical core–shell materials were performed to show how knowledge of the interdiffusion constants can guide rational design of practical core–shell materials.
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".