Unraveling chemical origins of dendrite formation in zinc-ion batteries via in situ/operando X-ray spectroscopy and imaging
Why this work is in the frame
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Bibliographic record
Abstract
To prevent zinc (Zn) dendrite formation and improve electrochemical stability, it is essential to understand Zn dendrite growth, particularly in terms of morphology and relation with the solid electrolyte interface (SEI) film. In this study, we employ in-situ scanning transmission X-ray microscopy (STXM) and spectro-ptychography to monitor the morphology evolution of Zn dendrites and to identify their chemical composition and distribution on the Zn surface during the stripping/plating progress. Our findings reveal that in 50 mM ZnSO4, the initiation of moss/whisker dendrites is chemically controlled, while their continued growth over extended cycles is kinetically governed. The presence of a dense and stable SEI film is critical for inhibiting the formation and growth of Zn dendrites. By adding 50 mM lithium chloride (LiCl) as an electrolyte additive, we successfully construct a dense and stable SEI film composed of Li2S2O7 and Li2CO3, which significantly improves cycling performance. Moreover, the symmetric cell achieves a prolonged cycle life of up to 3900 h with the incorporation of 5% 12-crown-4 additives. This work offers a strategy for in-situ observation and analysis of Zn dendrite formation mechanisms and provides an effective approach for designing high-performance Zn-ion batteries. Understanding the evolution of Zn dendrites during cycling is crucial for development of Zn batteries. Here, authors employ in situ scanning transmission X-ray microscopy to identify the chemical origins of Zn dendrite formation and propose an electrolyte additive strategy to prolong cycle life.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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