High-Performance Li–Se Batteries Enabled by Selenium Storage in Bottom-Up Synthesized Nitrogen-Doped Carbon Scaffolds
Bibliographic record
Abstract
Selenium (Se) has great promise to serve as cathode material for rechargeable batteries because of its good conductivity and high theoretical volumetric energy density comparable to sulfur. Herein, we report the preparation of mesoporous nitrogen-doped carbon scaffolds (NCSs) to restrain selenium for advanced lithium-selenium (Li-Se) batteries. The NCSs synthesized by a bottom-up solution-phase method have graphene-like laminar structure and well-distributed mesopores. The unique architecture of NCSs can severe as conductive framework for encapsulating selenium and polyselenides, and provide sufficient pathways to facilitate ion transport. Furthermore, the laminar and porous NCSs can effectively buffer the volume variation during charge/discharge processes. The integrated composite of Se-NCSs has a high Se content and can ensure the complete electrochemical reactions of Se and Li species. When used for Li-Se batteries, the cathodes based on Se-NCSs exhibit high capacity, remarkable cyclability, and excellent rate performance.
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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".