Probing the active sites of 2D nanosheets with Fe-N-C carbon shell encapsulated FexC/Fe species for boosting sodium-ion storage performances
Bibliographic record
Abstract
Developing stable but high active metal-nitrogen-carbon (M-N-C)-based hard carbon anode is a promising way to be the alternatives to graphene and blank hard carbon for sodium-ion batteries (SIBs), requiring the precise tailoring of the electronic structure for optimizing the Na+ intercalation behavior, yet is greatly challenging. Herein, Fe-N-C graphitic layer-encapsulating Fe3C species within hard carbon nanosheets (Fe-N-C/Fe3C@HCNs) are rationally engineered by pyrolysis of self-assembled polymer. Impressively, the Fe-N-C/Fe3C@HCNs exhibit outstanding rate capacity (242 mAh·g−1 at 2,000 mA·g−1), which is 2.1 and 4.2 times higher than that of Fe-N-C and N-doped carbon (N-C), respectively, and prolonged cycling stability (176 mAh·g−1 at 2,000 mA·g−1 after 2,000 cycles). Theoretical calculations unveil that the Fe3C species enhance the electronic transfer from Na to Fe-N-C, resulting in the charge redistribution between the interfaces of Fe3C and Fe-N-C. Thus, the optimized adsorption behavior towards Na+ reduces the thermodynamic energy barriers. The synergistic effect of Fe3C and Fe-N-C species maintains the structural integrity of electrode materials during the sodiation/desodiation process. The in-depth insight into the advanced Na+ storage mechanisms of Fe3C@Fe-N-C offers precise guidance for the rational establishment of confinement heterostructures in 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".