Understanding the Mechanism of Enhanced Cycling Stability in Sn–Sb Composite Na-Ion Battery Anodes: Operando Alloying and Diffusion Barriers
Bibliographic record
Abstract
Sn–Sb composites are of great interest for high capacity sodium-ion batteries due to their high stability, but because multiple phases and alloys are formed during cycling, the roles of each are challenging to deduce. In this work, two approaches were taken to investigate the importance of β-SnSb formation on the cycling stability of Sn-rich Sn–Sb composite sodium-ion battery (SIB) anodes. First, to tease out the role of each component, thin layers of amorphous silicon, with thicknesses ranging from 0.5 to 10 nm, were incorporated between Sn and Sb layers, of equal thicknesses. Silicon has low solubility in both tin and antimony, and thus acts as a barrier layer that can interfere with the formation of Sn–Sb alloys. The equivalent composition of this sandwich structure was Sn 53 Sb 47 . Upon electrochemical cycling, a clear correlation between capacity retention and Si thickness was observed, and it was found that a 1 nm thick Si layer was sufficient to inhibit the formation of the β-SnSb intermetallic, resulting in loss of the capacity of the tin layer after a few tens of cycles. The second approach involved capping a Sn film with increasingly thicker Sb layers. Thicker antimony layers were found to have a large positive influence on cycling stability with a marked drop-off in the capacity retention when there is not enough Sb to fully convert the bilayer into β-SnSb. These results point to the necessity of the Sn and Sb being in intimate contact prior to cycling for the β-SnSb phase to form in operando, which is necessary for the excellent capacity retention of the Sn–Sb system.
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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 | 0.001 |
| 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".