Li-Sn Alloyed Layer Containing Artificial SEI on Li-Metal Anode for Superior Lithium Metal Batteries: Development and Understanding of Anode/Electrolyte Interfacial Phenomena
Bibliographic record
Abstract
Modern civilization and cutting-edge technologies and tools are highly dependent on energy storage devices and demand efficient and long-lasting high-energy-density storage devices. Lithium metal batteries have surpassed their competitors concerning energy density due to their inherent high specific capacity (3860 mAh/g) and the lowest electrochemical potential (-3.04 V vs. standard hydrogen electrode), proving to be one of the most favorable energy storage devices for smart and mobile gadgets, electric automobiles, grid storage, etc. However, safety-threatening and performance-decay challenges associated with lithium metal anodes, such as lithium dendrite formation, uncontrolled parasitic reactions between Li-metal and electrolyte, low coulombic efficiency, dead Li-formation, etc., restrict its employment in practical batteries. The root cause of the dendrite formation is poor Li-ion conductivity of bulk lithium metal, which results in an uneven electric field distribution, leading to non-uniform Li deposition/stripping upon repeated charge/discharge. Herein, a facile chemical reduction process is being opted to fabricate a highly Li-ion diffusive Li-Sn-based artificial SEI layer on the lithium metal surface to prevail over the above-mentioned issues. Therefore, upon careful concentration investigation of reactants, 25 mM precursor concentration (SnCl4 in EC: DMC in 1:1 volume ration) demonstrated the best electrochemical performance, revealing a cumulative capacity of over 700 mAh/cm2 at a current density of 1 mA/cm2 for 1 h in a symmetric cell. Physical characterizations (XRD and SEM-EDS) showed a thin layer containing Li-Sn alloys along with LiCl formed at the lithium metal surface. Possessing high Li-ion diffusivity, Li-Sn alloyed hybrid anode illustrated a gradual decrease in the charge transfer resistance on increasing the concentration, thus, lower overpotential in symmetric cells, in contrast to pristine Li-metal. However, an increase in the electrical resistance has been observed on increasing the precursor concentration due to the presence of the insulating LiCl phase in the protective layer. Moreover, in-situ optical microscopy demonstrated more uniform and on-surface Li-deposition for the hybrid electrode, whereas mossy and dendritic growth on bare Li-metal. Full cells prepared with Li-Sn alloyed anode against NMC532 cathode illustrated stable cycling up to 150 cycles; contrastingly, the cell with pristine Li-metal showed a drastic capacity fade just after 100 cycles, demonstrating the inclination towards safer lithium metal batteries.
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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) | 0.000 | 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 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".