A MoSe<sub>2</sub>/N-doped hollow carbon sphere host for rechargeable Na–Se batteries
Bibliographic record
Abstract
Sodium-selenium (Na-Se) batteries are promising alternatives to lithium-ion batteries for energy storage systems owing to their high energy density and natural abundance of Na resources. However, their drawbacks of low Se loading, dissolution of intermediate sodium polyselenides in the electrolyte and volumetric expansion of Se impede their real applications. To address these issues, herein, we report a multifunctional Se host with MoSe2 nanosheets coupled with nitrogen-doped porous carbon hollow spheres for the first time. The N-doped hollow carbon sphere structure could provide a large space for Se loading (Se content up to 72 wt%) and accommodate the volume expansion of Se species upon cycling. MoSe2 was chosen as a polar coupling component for the carbon matrix, owing to its low conversion reaction voltage. Based on density functional theory (DFT) calculations, the MoSe2 nanosheets coupled with hollow spheres could enhance the adsorption energy of the host to polyselenides chemically, which benefits the immobilization of polyselenides. Therefore, as a cathode for Na-Se batteries, the as-prepared composite exhibits excellent energy storage performance with long cycling life and superior rate performance. Our study of introducing transition metal selenides into Na-Se batteries may stimulate the designing of diverse Se-based cathodes.
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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".