Efficient Method of Designing Stable Layered Cathode Material for Sodium Ion Batteries Using Aluminum Doping
Bibliographic record
Abstract
Despite their high specific capacity, sodium layered oxides suffer from severe capacity fading when cycled at higher voltages. This key issue must be addressed in order to develop high-performance cathodes for sodium ion batteries (SIBs). Herein, we present a comprehensive study on the influence of Al doping of Mn sites on the structural and electrochemical properties of a P2–Na 0.5 Mn 0.5– x Al x Co 0.5 O 2 ( x = 0, 0.02, or 0.05) cathode for SIBs. Detailed structural, morphological, and electrochemical investigations were carried out using X-ray diffraction, cyclic voltammetry, and galvanostatic charge–discharge measurements, and some new insights are proposed. Rietveld refinement confirmed that Al doping caused TMO 6 octahedra (TM = transition metal) shrinkage, resulting in wider interlayer spacing. After optimizing the aluminum concentration, the cathode exhibited remarkable electrochemical performance, with better stability and improved rate performance. Electrochemical impedance spectroscopy (EIS) measurements were performed at various states of charge to probe the surface and bulk effects of Al doping. The material presented here exhibits exceptional stability over 100 cycles within a 1.5–4.3 V window and outperforms several other Mn–Co-based cathodes for SIBs. This study presents a facile method for designing structurally stable cathodes for SIBs.
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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 | 0.000 | 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".