Stabilizing Tin Anodes in Sodium-Ion Batteries by Alloying with Silicon
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 that 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. In order 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 co-sputtered 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 role of the Si interlayers appears to be one of acting as a buffer during cycling to help preserve Sn particles within the thin Sn interlayers. 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 (SEI).
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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) | 0.000 | 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 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".