Engineered interfaces between perovskite La2/3xLi3xTiO3 electrolyte and Li metal for solid-state batteries
Bibliographic record
Abstract
Perovskite La 2/3x Li 3x TiO 3 (LLTO) materials are promising solid-state electrolytes for lithium metal batteries (LMBs) due to their intrinsic fire-resistance, high bulk ionic conductivity, and wide electrochemical window. However, their commercialization is hampered by high interfacial resistance, dendrite formation, and instability against Li metal. To address these challenges, we first prepared highly dense LLTO pellets with enhanced microstructure and high bulk ionic conductivity of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mrow><mml:mn>2.1</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mn>4</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math> S cm −1 at room temperature. Then, the LLTO pellets were coated with three polymer-based interfacial layers, including pure (polyethylene oxide) (PEO), dry polymer electrolyte of PEO-LITFSI (lithium bis (trifluoromethanesulfonyl) imide) (PL), and gel PEO-LiTFSI-SN (succinonitrile) (PLS). It is found that each layer has impacted the interface differently; the soft PLS gel layer significantly reduced the total resistance of LLTO to a low value of 84.88 Ω cm −2 . Interestingly, PLS layer has shown excellent ionic conductivity but performs inferior in symmetric Li cells. On the other hand, the PL layer significantly reduces lithium nucleation overpotential and shows a stable voltage profile after 20 cycles without any sign of Li dendrite formation. This work demonstrates that LLTO electrolytes with denser microstructure could reduce the interfacial resistance and when combined with polymeric interfaces show improved chemical stability against Li metal.
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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) | 0.000 | 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".