Enhanced electrochemical performance of Spray–Dried Na2Fe0.5Mn0.5PO4F/CNT cathode for Sodium–Ion batteries
Bibliographic record
Abstract
Na 2 Fe 0.5 Mn 0.5 PO 4 F and Na 2 Fe 0.5 Mn 0.5 PO 4 F/CNT composite were prepared through a one–step spray–drying process and evaluated as cathode active materials for Na–ion batteries (NIBs). The prepared materials crystallize in the monoclinic P2 1 /c space group as confirmed by XRD analysis. Benefiting from the spray drying protocol, distorted particle shape with large surface indentations were obtained with an average size of ∼9 μm (d 50 ) measured by laser granulometry. The distribution of the CNTs in the composite was demonstrated by TEM analysis. The materials were also characterized by 57 Fe Mössbauer spectroscopy and magnetic hysteresis measurements. The spray–dried Na 2 Fe 0.5 Mn 0.5 PO 4 F/CNT composite demonstrates enhanced electrochemical performance by delivering a capacity of 118 mAh/g (95 % of the C th = 124.5 mAh/g) at C/20 versus 60 mAh/g for Na 2 Fe 0.5 Mn 0.5 PO 4 F with a good cycling stability after 100 cycles. In-situ potentiostatic electrochemical impedance spectroscopy (PEIS) and galvanostatic intermittent titration technique (GITT) were considered to study the charge transfer kinetic in the electrodes. Overall, in this work, we propose a scalable synthesis method and strategy for the development of Na 2 Fe 0.5 Mn 0.5 PO 4 F/CNT as high performance cathode material that could compete with existing cathode materials for NIBs.
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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".