A Facile Chemical Reduction Approach of Li–Sn Modified Li Anode for Dendrite Suppression
Bibliographic record
Abstract
Lithium dendrites are among the most significant threats associated with the practical application of lithium metal anode in lithium batteries. Lithium dendrites are caused by the slow Li‐ion diffusivity in the bulk lithium, which results in a non‐uniform electric field‐cum‐uneven Li plating/stripping at the electrode/electrolyte interface over prolonged cycling. Herein, a facile chemical reduction method is utilized to construct a Li‐ion diffusive Li–Sn protective layer on the electrolyte‐exposed surface of lithium metal to overcome the aforementioned challenge. A systematic study on the SnCl4 precursor concentration variation demonstrated that 25 mM SnCl4 concentration is the most effective and displays a cumulative areal capacity beyond 700 mAh cm−2 at 1 mA cm−2 for 1 h. Moreover, it exhibits superior cumulative capacities than bare Li metal at higher current densities of 2 and 3 mA cm−2. In situ optical microscopy reveals more uniform lithium deposition on the Li–Sn‐modified electrode, while mossy and dendritic lithium growth is observed on the bare lithium electrode. Full cells fabricated with Li–Sn modified anode and NMC532 cathode exhibited 83% capacity retention after 150 cycles, outperforming bare Li‐containing cells, which shows a catastrophic decay post 100 cycles, illustrating the propensity for safer Li metal batteries with Li–Sn modified anode.
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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 | 0.001 |
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".