Fundamentals, preparation, and mechanism understanding of Li/Na/Mg-Sn alloy anodes for liquid and solid-state lithium batteries and beyond
Bibliographic record
Abstract
Lithium metal is one of the most promising anodes to develop high energy density and safe energy storage devices due to its highest theoretical capacity (3860 mAh·g −1 ) and lowest electrochemical potential, demonstrating great potential to fulfill unprecedented demand from electronic gadgets, electric vehicles, and grid storage. Despite these good merits, lithium metal suffers from low Coulombic efficiency and dendritic growth, leading to internal short-circuiting of the cell and raising safety concerns about employing lithium metal as an anode. Recently, lithium-tin (Li-Sn) alloys, among other lithium alloys, have emerged as a potential alternative to lithium metal to efficiently suppress the lithium dendrite formation and reduce interfacial resistance for safer and longer-lasting lithium batteries. Accordingly, this work first reviews the fundamentals of Li-Sn alloys, and critically analyzes the failure mechanisms of pristine Li-metal anode and how Li-Sn alloys could overcome those challenges. The subsequent section examines various strategies to synthesize Li-Sn bulk and protection film alloys, followed by an evaluation of symmetric cell performance. Furthermore, the comparative electrochemical performance of full cells against different cathodes and solid electrolytes provides an overview of the present research. Subsequently, advanced characterization techniques were discussed to visualize lithium dendrites directly and quantify the mechanical performance of Li-Sn alloys. Last but not the least, the state-of-the-art progress of applying M-Sn (M = Na and Mg) beyond lithium batteries was summarized. In closing, this work identifies the critical challenges and provides future perspectives on Li-Sn alloy for lithium batteries and beyond.
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How this classification was reachedexpand
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 | 0.001 | 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 teacher head, 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".