Rational Design of Hierarchical “Ceramic‐in‐Polymer” and “Polymer‐in‐Ceramic” Electrolytes for Dendrite‐Free Solid‐State Batteries
Bibliographic record
Abstract
Abstract Solid polymer electrolytes as one of the promising solid‐state electrolytes have received extensive attention due to their excellent flexibility. However, the issues of lithium (Li) dendrite growth still hinder their practical applications in solid‐state batteries (SSBs). Herein, composite electrolytes from “ceramic‐in‐polymer” (CIP) to “polymer‐in‐ceramic” (PIC) with different sizes of garnet particles are investigated for their effectiveness in dendrite suppression. While the CIP electrolyte with 20 vol% 200 nm Li 6.4 La 3 Zr 1.4 Ta 0.6 O 12 (LLZTO) particles (CIP‐200 nm) exhibits the highest ionic conductivity of 1.6 × 10 −4 S cm −1 at 30 °C and excellent flexibility, the PIC electrolyte with 80 vol% 5 µm LLZTO (PIC‐5 µm) shows the highest tensile strength of 12.7 MPa. A sandwich‐type composite electrolyte (SCE) with hierarchical garnet particles (a PIC‐5 µm interlayer sandwiched between two CIP‐200 nm thin layers) is constructed to simultaneously achieve dendrite suppression and excellent interfacial contact with Li metal. The SCE enables highly stable Li plating/stripping cycling for over 400 h at 0.2 mA cm −2 at 30 °C. The LiFePO 4 /SCE/Li cells also demonstrate excellent cycle performance at room temperature. Fabricating sandwich‐type composite electrolytes with hierarchical filler designs can be an effective strategy to achieve dendrite‐free SSBs with high performance and high safety at room temperature.
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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) | 0.001 | 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".