Crystallographic Habit Tuning of Li<sub>2</sub>MnSiO<sub>4</sub> Nanoplates for High-Capacity Lithium Battery Cathodes
Bibliographic record
Abstract
Li 2 MnSiO 4 has attracted significant attention as a cathode material for lithium ion batteries because of its high theoretical capacity (330 mA h g –1 with two Li + ions per formula unit), low cost, and environmentally friendly nature. However, its intrinsically poor Li diffusion, low electronic conductivity, and structural instability preclude its use in practical applications. Herein, elongated hexagonal prism-shaped Li 2 MnSiO 4 nanoplates with preferentially exposed {001} and {210} facets have been successfully synthesized via a solvothermal method. Density functional theory calculations and experimental characterization reveal that the formation mechanism involves the decomposition of solid precursors to nanosheets, self-assembly into nanoplates, and Ostwald ripening. Hydroxyl-containing solvents such as ethylene glycol and diethylene glycol play a crucial role as capping agents in tuning the preferential growth. Li 2 MnSiO 4 @C nanoplates demonstrate a near theoretical discharge capacity of 326.7 mA h g –1 at 0.05 C (1 C = 160 mA h g –1 ), superior rate capability, and good cycling stability. The enhanced electrochemical performance is ascribed to the electrochemically active {001} and {210} exposed facets, which provide short and fast Li + diffusion pathways along the [001] and [100] axes, a conformal carbon nanocoating, and a nanoscaled platelike structure, which offers a large electrode/electrolyte contact interface for Li + extraction/insertion processes.
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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".