Synthesis of MnO–Sn cubes embedding in nitrogen-doped carbon nanofibers with high lithium-ion storage performance
Bibliographic record
Abstract
Abstract In this paper, a carbon nanofiber (CNF) hybrid nanomaterial composed of MnO–Sn cubes embedding in nitrogen-doped CNF (MnO–Sn@CNF) is synthesized through electrospinning and post-thermal reduction processes. It exhibits good electrochemical lithium-ion storage performance as the anode, such as high reversible capacity, outstanding cycle performance (754 mAh g −1 at 1 A g −1 after 1000 cycles), and good rate capability (447 mAh g −1 at 5 A g −1 ). The excellent electrochemical properties are derived from a unique nanostructure design. MnO–Sn@CNF has a three-dimensional conductive network with a stable core–shell structure, which improves the electrical conductivity and mechanical stability of the materials. In addition, the mesopores on the surface of carbon fibers can shorten the diffusion distance of lithium ions and promote the combination of active sites of the material with lithium ions. The internal MnO and Sn form a heterostructure, which enhances the stability of the physical structure of the electrode material. This material design method provides a reference strategy for the development of high-performance lithium-ion batteries anode.
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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) | 0.000 | 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 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".