Gradient Surface Modification of Li-Excess Layered Oxide Cathodes Using Polyanions
Bibliographic record
Abstract
Li-excess layered oxides (LLOs) of the general formula xLi2MnO3·(1-x)LiTMO2 (TM = Mn, Ni, Co, etc.) exhibit reversible capacities of up to 250 mAh g-1 and are more cost-effective. The Li2MnO3 component plays crucial roles in the electrochemistry of the LLOs, which not only improves the structural stability of LiTMO2 at high potentials, but also serves as an electrochemically active phase for Li extraction when charged above 4.5 V vs. Li/Li+. However, LLO cathode materials still suffer from limited cycle life and sluggish kinetics (e.g., poor rate capability), which origins from complex transition metal arrangements associated with the activation of Li2MnO3 (release of Li2O) upon charging at high potential and the inevitable layered-to-spinel structural transformation during electrochemical cycling. Herein, a gradient polyanion-doping strategy, on the basis of borate, phosphate and silicate polyanions, is developed to integrate the advantages of both bulk doping and surface modification as the oxygen close-packed structure of LLOs is stabilized by polyanion doping, and the LLO cathodes are protected from steady corrosion induced by electrolytes. We comprehensively investigate the electrochemical performance of polyanion-doped LLOs with various local structures, which were prepared by manipulating doping content and annealing temperature. With the assistance of aberration-corrected scanning transmission electron microscopy (STEM), we present direct evident that the incorporation of polyanions induces a complex crystallization process of layered structure, and propose some new insights into the electrochemical improvement resulted from polyanion doping.
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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) | 0.000 | 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 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".