Stabilizing Tin Anodes in Sodium-Ion Batteries by Alloying with Silicon
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Bibliographic record
Abstract
Group IV of the periodic table is a promising column with respect to high capacity anode materials for sodium-ion batteries (SIBs). Unlike carbon which relies on interlayer defects, pores, and intercalation to store sodium, its heavier cousins—silicon, germanium, and tin—form binary alloys with sodium. Alloying does lead to the formation of high-capacity compounds, but they are, however, susceptible to large volumetric changes upon expansion that results in pulverization of the electrodes and poor cycling stability. Silicon and tin are particularly intriguing due to their high theoretical reversible capacities of 954 mAh/g (NaSi) and 847 mAh/g (Na15Sn4), respectively, but suffer from poor practical capacity and very short lifetimes, respectively. To buffer the detrimental effects of volume expansion and contraction, nanoscale multilayer anodes comprising silicon and tin films were prepared and compared with uniform films composed of atomically mixed silicon and tin as well as elemental silicon and tin films. The results reveal that the high capacity fade for elemental Sn is associated with detrimental anodic (desodiation) reactions at a high cutoff voltage with a threshold defined as ∼0.8 VNa. Binary mixtures of Si and Sn were tested in a number of different architectures, including multilayer films and cosputtered films with varying volume ratios of both elements. All mixed films showed improved capacity retention compared to the performance of anodes comprising only elemental Sn. A multilayer structure composed of 3 nm thick silicon and tin layers showed the highest Coulombic efficiency and retained 97% of its initial capacity after 100 cycles, which is vastly improved compared to 7% retention observed for the elemental Sn film. The alloying element, Si, plays two roles: it stabilizes grain growth/pulverization and also alters the surface chemistry of the anodes, thus affecting the formation of solid electrolyte interphase.
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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 | 0.000 |
| 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 it