Ultrafast One‐Step Synthesis of Garnet‐Type Solid Electrolytes With Modified Surface and Microstructure for Solid‐State Lithium‐Metal Batteries
Bibliographic record
Abstract
Abstract Garnet‐type Li6.5La3Zr1.5Ta0.5O12 (LLZTO) solid electrolytes provide the necessary electrochemical stability and ionic conductivity for solid‐state lithium‐metal batteries (SSLMBs). However, their wider application is hindered by their high interfacial resistance with electrodes and a lengthy synthesis process. This study presents the synthesis of densified LLZTO electrolytes using unconventional Li2O and Li2ZrO3 precursors through an ultrafast (≈60 s) Joule heat‐assisted synthesis approach in a single‐step process. The lower sintering temperature of Li2ZrO3 compared to traditional ZrO2 precursor yields LLZTO with larger grains, resulting in enhanced Li+ conductivity (7.0 × 10−4 S cm−1 at 25 °C), reduced electronic conductivity (1.7 × 10−10 S cm−1), and higher density (94.2%). Applying a 52–80 nm Sn:SnF2 coating on the LLZTO surface using a melt‐quenching approach produces a uniform interlayer that chemically converts to Li‐Sn alloy and LiF upon contact with lithium, resulting in a near‐zero interfacial resistance and a critical current density of 4.2 mA cm−2 at 25 °C. The SSLMBs, incorporating Sn:SnF2‐coated LLZTO electrolyte with NMC811 cathode, demonstrate remarkable initial capacity (181.1 mAh g−1) and cycle performance (88.63% capacity retention at 3000th cycle). The results indicate that this approach has the potential to advance the commercial fabrication technology for high‐performance solid electrolytes for SSLMBs.
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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".