Synergistic Regulation of Polyselenide Dissolution and Na‐Ion Diffusion of Se‐Vacancy‐Rich Bismuth Selenide toward Ultrafast and Durable Sodium‐Ion Batteries
Bibliographic record
Abstract
Abstract Metal selenides (MSes) have great potential as candidate anode materials in high‐specific‐energy sodium‐ion batteries (SIBs) but are plagued by rapid capacity degradation and slow kinetics. Here, it is reveal that the Bi 2 Se 3 anode discharge process involves multiple‐types of sodium polyselenides (Na‐pSe x ) which suffer from terrible dissolution and shuttling properties. Based on these observations, a nanoflower‐like composite of dual carbon‐confined Bi 2 Se 3− x crystallites is designed via facile defect chemistry. The robust dual N‐doped carbon layer suppresses the precipitation and aggregation of Bi 2 Se 3 , significantly alleviating the dissolution and shuttle effect of Na‐pSe x . Theoretical calculations indicate that the pyridine/pyrrole nitrogen sites exhibit strong van der Waals resistance and chemisorption properties against Na 2 Se 4 and Na 2 Se 2 . Furthermore, the abundant Se vacancies improve the inherent conductivity of Bi 2 Se 3 , reduce the diffusion barrier of Na + , and accelerate the reaction kinetics. Consequently, the resulting Bi 2 Se 3− x @DNC electrode exhibits extraordinary durability (over 2000 cycles at 10.0 A g −1 ) and high‐rate capability (354.4 mAh g −1 at 75.0 A g −1 ), propelling the battery performance to new heights. Encouragingly, the assembled hybrid capacitor displays competitive rate performance and an ultra‐long lifespan exceeding 40 000 cycles, making the Bi 2 Se 3− x @DNC electrode a promising candidate for 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".