In Situ, Atomic‐Resolution Observation of Lithiation and Sodiation of WS<sub>2</sub> Nanoflakes: Implications for Lithium‐Ion and Sodium‐Ion Batteries
Bibliographic record
Abstract
Abstract WS2 nanoflakes have great potential as electrode materials of lithium‐ion batteries (LIBs) and sodium‐ion batteries (SIBs) because of their unique 2D structure, which facilitates the reversible intercalation and extraction of alkali metal ions. However, a fundamental understanding of the electrochemical lithiation/sodiation dynamics of WS2 nanoflakes especially at the nanoscale level, remains elusive. Here, by combining battery electrochemical measurements, density functional theory calculations, and in situ transmission electron microscopy, the electrochemical‐reaction kinetics and mechanism for both lithiation and sodiation of WS2 nanoflakes are investigated at the atomic scale. It is found that compared to LIBs, SIBs exhibit a higher reversible sodium (Na) storage capacity and superior cyclability. For sodiation, the volume change due to ion intercalation is smaller than that in lithiation. Also, sodiated WS2 maintains its layered structure after the intercalation process, and the reduced metal nanoparticles after conversion in sodiation are well‐dispersed and aligned forming a pattern similar to the layered structure. Overall, this work shows a direct interconnection between the reaction dynamics of lithiated/sodiated WS2 nanoflakes and their electrochemical performance, which sheds light on the rational optimization and development of advanced WS2‐based electrodes.
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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) | 0.001 | 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".