Factors that Affect Capacity in the Low Voltage Kinetic Hindrance Region of Ni-Rich Positive Electrode Materials and Diffusion Measurements from a Reinvented Approach
Bibliographic record
Abstract
With research continuing to push for higher Ni content in positive electrode materials, issues such as the 1st cycle irreversible capacity and kinetic hindrances related to Li diffusion become more significant. This work highlights the impact of various material parameters on electrochemical performances, specifically the kinetic hindrances to Li diffusion in the low voltage region. Increasing the amount of substituents, increasing the secondary particle size and increasing the primary particle size were all variables found to decrease capacity in the ∼3.4–3.6 V region at modest discharge rates and increase the 1st cycle IRC. The capacity in the ∼3.4–3.6 V region can be recovered when cycling at a higher temperature at similar discharge rates or when cycling to a low cut-off voltage of 2 V. Since these processes are related to the diffusion of Li in the positive electrode, analysis of the Li chemical diffusion coefficient, D c , is presented using a reinvented approach we call the “Atlung Method for Intercalant Diffusion.” The measured D c for the single crystalline LiNi0.975Mg0.025O2 materials were found to be about 2 orders of magnitude smaller compared to the polycrystalline materials if the secondary particle size was used in the calculation of D c for the polycrystalline samples. If the primary particle size of the polycrystalline materials was used, then D c was similar to the single crystal materials. These results demonstrate that lattice diffusion is much slower compared to grain boundary diffusion offering insight for optimizing material morphology for better rate performance.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".