Quantitative Identification of Dopant Occupation in Li‐Rich Cathodes
Bibliographic record
Abstract
Abstract Elemental doping is widely used to improve the performance of cathode materials in lithium‐ion batteries. However, macroscopic/statistical investigation on how doping sites are distributed in the material lattice, despite being a key prerequisite for understanding and manipulating the doping effect, has not been effectively established. Herein, to solve this predicament, a universal strategy is proposed to quantitatively identify the locations of Al and Mg dopants in lithium‐rich layered oxides (LLOs). Solid evidence confirms that Al prefers to occupy the transition metal (TM) layer, while Mg evenly occupies both TM and Li layers. As a result, Mg significantly reduces the thickness of LiO 2 slabs at room temperature, which will increase the energy barrier of oxygen activation and enhance the structure stability of LLOs. The suppressed oxygen activity in Mg‐doped LLO can be kinetically unlocked at 55 °C. The different characteristics of Al and Mg enlighten an Al/Mg co‐doping strategy to optimize LLOs, which significantly improves the cycle performance while lifting the capacity. These insights from the quantitative identification of doping sites shed light on the manipulation of doping effects toward better cathodes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".