Effects of Lithium Metal Storage Environment on Its Reactivity toward Polyethylene Oxide-Based Blend Electrolytes
Bibliographic record
Abstract
Lithium metal has generated significant interest as an anode material because of its high theoretical capacity. However, issues such as dendrite growth and lithium loss during cycling make this material incompatible with liquid electrolytes. Solid polymer electrolytes (SPE) have been proposed as replacements as they are non-flammable, resist dendrite growth, have decent ionic conductivity, and have low resistance with lithium metal. Passivation layers, which form on the lithium metal surface and are hence intrinsic to its chemical composition, are often overlooked. Residual quantities of atmospheric gases are present in lithium metal storage environments, making surface modification and its subsequent impact on anode reactivity inevitable. Moreover, the impact of this phenomenon in a realistic lithium metal anode (LMA) environment with SPE has not yet been extensively investigated. In this study, the impact of gas exposure on an LMA was investigated by exposing freshly cut lithium rods to O 2, CO 2, and N 2 . Passivation layers were characterized via X-ray photoelectron spectroscopy. The effect of passivation layer formation on LMA reactivity toward SPE was measured by exposing passivated samples to common SPE materials. The resultant interface was characterized using Raman spectroscopy. SPE-passivation layer reactivity was correlated to ageing by electrochemical impedance spectroscopy and kinetic charge transfer via galvanostatic linear polarization at the LMA–SPE interface in symmetric Li─SPE─Li stacks. This study revealed that the chemical composition of the passivation layer affects LMA reactivity toward SPE and electrochemical performance. A thorough characterization of the lithium metal passivation layer is essential to understanding the fundamental factors affecting solid-state lithium metal battery performance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".