Anodes for Sodium Ion Batteries Based on Tin–Germanium–Antimony Alloys
Bibliographic record
Abstract
Here we provide the first report on several compositions of ternary Sn-Ge-Sb thin film alloys for application as sodium ion battery (aka NIB, NaB or SIB) anodes, employing Sn50Ge50, Sb50Ge50, and pure Sn, Ge, Sb as baselines. Sn33Ge33Sb33, Sn50Ge25Sb25, Sn60Ge20Sb20, and Sn50Ge50 all demonstrate promising electrochemical behavior, with Sn50Ge25Sb25 being the best overall. This alloy has an initial reversible specific capacity of 833 mAhg(-1) (at 85 mAg(-1)) and 662 mAhg(-1) after 50 charge-discharge cycles. Sn50Ge25Sb25 also shows excellent rate capability, displaying a stable capacity of 381 mAhg(-1) at a current density of 8500 mAg(-1) (∼10C). A survey of published literature indicates that 833 mAhg(-1) is among the highest reversible capacities reported for a Sn-based NIB anode, while 381 mAhg(-1) represents the optimum fast charge value. HRTEM shows that Sn50Ge25Sb25 is a composite of 10-15 nm Sn and Sn-alloyed Ge nanocrystallites that are densely dispersed within an amorphous matrix. Comparing the microstructures of alloys where the capacity significantly exceeds the rule of mixtures prediction to those where it does not leads us to hypothesize that this new phenomenon originates from the Ge(Sn) that is able to sodiate beyond the 1:1 Na:Ge ratio reported for the pure element. Combined TOF-SIMS, EELS TEM, and FIB analysis demonstrates substantial Na segregation within the film near the current collector interface that is present as early as the second discharge, followed by cycling-induced delamination from the current collector.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".