Tuning cation ordering and Mn3+ content in non-stoichiometric LiNi0.5-xMn1.5+xO4-y (LNMO) for enhanced cathode stability in lithium-ion batteries
Bibliographic record
Abstract
Rapidly increasing demand for high-energy, long-cycle-life lithium-ion batteries (LIBs), particularly in electric vehicles and grid-scale applications, has highlighted the need for advanced cathode materials. High-voltage LiNi 0.5 Mn 1.5 O 4 (LNMO) has attracted considerable attention owing to its elevated working potential, reduced reliance on nickel, and cobalt-free composition. In this work, a scalable co-precipitation method is employed to synthesize non-stoichiometry LNMO cathodes with varying particle size, enabling precise control over particle morphology, cation ordering and Mn 3+ content. Comprehensive structural and electrochemical evaluations reveal that reducing the Ni content in LNMO elevates the Mn 3+ concentration and promotes the degree if cation disorder, which facilitates a single-phase, solid-solution reaction mechanism during lithiation and de-lithiation. In such a mechanism, Li + are inserted and extracted uniformly throughout the material without the formation of distinct phase boundaries, thereby significantly reducing kinetic barriers and polarization. Furthermore, although Mn 3+ typically induces local Jahn-Teller distortions, in a highly disordered lattice these distortions are more uniformly distributed, which minimizes local stress accumulation and enhances structural stability during cycling. This uniform distribution not only supports rapid Li + diffusion through continuous and well-connected pathways but also improves electronic conductivity by optimizing the local electronic structure. Consequently, LNMO with the highest cation disorder and Mn 3+ content, exhibits superior electrochemical performance, delivering 119.6 mAh·g −1 at 2C, retaining 70.3 % of its capacity after 1000 cycles and demonstrating the best kinetics among the samples. • Non-Stoichiometric LNMO was synthesized through a scalable co-precipitation method. • The cation ordering and Mn 3+ content LNMO were precisely tuned by control of Ni/Mn ratio. • Optimized LNMO achieves reduced polarization and accelerated Li + kinetics. • LNMO-9 delivers 119.6 mAh·g −1 at 2C and 70.3 % retention after 1000 cycles at 1C.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".