Investigating Sodium Storage Mechanisms in Tin Anodes: A Combined Pair Distribution Function Analysis, Density Functional Theory, and Solid-State NMR Approach
Bibliographic record
Abstract
Abstract The alloying mechanism of high-capacity tin anodes for sodium-ion batteries is investigated using a combined theoretical and experimental approach. Ab initio random structure searching (AIRSS) and high-throughput screening using a species-swap method provide insights into a range of possible sodium–tin structures. These structures are linked to experiments using both average and local structure probes in the form of operando pair distribution function analysis, X-ray diffraction, and 23Na solid-state nuclear magnetic resonance (ssNMR), along with ex situ119Sn ssNMR. Through this approach, we propose structures for the previously unidentified crystalline and amorphous intermediates. The first electrochemical process of sodium insertion into tin results in the conversion of crystalline tin into a layered structure consisting of mixed Na/Sn occupancy sites intercalated between planar hexagonal layers of Sn atoms (approximate stoichiometry NaSn3). Following this, NaSn2, which is predicted to be thermodynamically stable by AIRSS, forms; this contains hexagonal layers closely related to NaSn3, but has no tin atoms between the layers. NaSn2 is broken down into an amorphous phase of approximate composition Na1.2Sn. Reverse Monte Carlo refinements of an ab initio molecular dynamics model of this phase show that the predominant tin connectivity is chains. Further reaction with sodium results in the formation of structures containing Sn–Sn dumbbells, which interconvert through a solid-solution mechanism. These structures are based upon Na5–xSn2, with increasing occupancy of one of its sodium sites commensurate with the amount of sodium added. ssNMR results indicate that the final product, Na15Sn4, can store additional sodium atoms as an off-stoichiometry compound (Na15+xSn4) in a manner similar to Li15Si4.
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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 | 0.001 | 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".