Synergistic Engineering of Architecture and Composition in Bimetallic Selenide@Carbon Hybrid Nanotubes for Enhanced Lithium‐ and Sodium‐Ion Batteries
Bibliographic record
Abstract
Abstract Developing sustainable and affordable anode materials that are capable of delivering high performance in both lithium‐ion batteries (LIBs) and sodium‐ion batteries (SIBs) remains a significant challenge. Bimetallic selenide@carbon hybrids are considered as one of the most promising anode materials in LIBs and SIBs due to their high electronic conductivity, high specific capacity, and fast reaction kinetics. Herein, a series of bimetallic selenide@carbon hybrid nanotubes are successfully prepared as anodes of LIBs or SIBs based on the dual regulation of component and micro‐nanostructure. The selenization strategy plays a key important role in determining the composition, microstructure, and electrochemical energy storage properties of anode materials. As a consequence, the ZnSe/CoSe2@NPC NTs(I)‐600 exhibit a reversible capacity of 1328.3 mAh g−1 at 0.1 A g−1 and superior rate capability (269.1 mAh g−1 at 10 A g−1) towards Li+ storage. Meanwhile, ZnSe/CoSe2@NPC NTs(II)‐700 achieve 354.1 mAh g−1 at 0.1 A g−1 and ultralong cycling stability (97.6% of capacity retention after 40 000 cycles at 10 A g−1) used as anode materials in SIBs. This study provides a feasible strategy to fabricate selenide‐based composites as anode materials for high‐performance LIBs and SIBs via architecture engineering and composition tailoring.
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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".