The Role of Na Doping in Li-Rich Li–Mn–Ni–O Layered Oxides
Bibliographic record
Abstract
Lithium- and manganese-rich layered oxides, especially cobalt-free compositions, have gained considerable attention as sustainable and cost-effective cathode materials for lithium-ion batteries. This attention is largely due to the relative abundance and low cost of Mn compared to Ni and Co. However, these materials face critical challenges, including voltage and capacity fade, structural instability, and transition metal (TM) dissolution. Sodium doping has been proposed as a promising strategy to address these issues by expanding the lithium interlayer spacing, reducing cation mixing, and introducing stabilizing defects. Herein, we present the impact of sodium substitution across a broad compositional range (0–20%) in Li 1+ x Na y (Ni 0.35 Mn 0.65 ) 1– x – y O 2 by synthesizing and characterizing 128 compositions within the Li–Na–Ni + Mn pseudoternary system. Using high-throughput X-ray diffraction, elemental analysis, and electrochemical testing of all samples, we accurately mapped the layered solid-solution region, despite substantial Li and Na loss during synthesis. The solid-solution region extends to high sodium content (13% of all cations) and spans both stoichiometric Li contents and Li-rich compositions. We uncover strong relationships between composition, structural stability, and electrochemical performance. Our results show that moderate sodium doping (∼2–8%) stabilizes the layered phase at low Li content and enhances electrochemical performance at high Li content. Additionally, several lithium-rich multiphase compositions display extreme capacity growth (e.g., 250% over 7 cycles), which yields the highest discharge capacity after 10 cycles of approximately 225 mA h g –1 . Furthermore, we systematically quantified TM dissolution under aggressive cycling and found that high Li content is needed to suppress TM dissolution and that this is resilient to the addition of sodium. Thus, we determine the optimal sodium content that improves battery performance while also showing good stability against TM dissolution due to the high Li content. These results highlight the importance of systematically exploring complex composition spaces, as small compositional changes can dramatically impact performance.
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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 | 0.000 |
| 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".