Polymer dispersed ionic liquid electrolytes with high ionic conductivity for ultrastable solid‐state lithium batteries
Bibliographic record
Abstract
Abstract Solid polymer electrolytes (SPEs) have emerged as one of the most promising candidates for building solid‐state lithium batteries due to their excellent flexibility, scalability, and interfacial compatibility with electrodes. However, the low ionic conductivity and poor cyclic stability of SPEs do not meet the requirements for practical applications of lithium batteries. Here, a novel polymer dispersed ionic liquid‐based solid polymer electrolyte (PDIL‐SPE) is fabricated using the in situ polymerization‐induced phase separation (PIPS) method. The as‐prepared PDIL‐SPE possesses both outstanding ionic conductivity (0.74 mS cm −1 at 25°C) and a wide electrochemical window (up to 4.86 V), and the formed unique three‐dimensional (3D) co‐continuous structure of polymer matrix and ionic liquid in PDIL‐SPE can promote the transport of lithium ions. Also, the 3D co‐continuous structure of PDIL‐SPE effectively accommodates the severe volume expansion for prolonged lithium plating and stripping processes over 1000 h at 0.5 mA cm −2 under 25°C. Moreover, the LiFePO 4 //Li coin cell can work stably over 150 cycles at a 1 C rate under room temperature with a capacity retention of 90.6% from 111.1 to 100.7 mAh g −1 . The PDIL‐SPE composite is a promising material system for enabling the ultrastable operation of solid‐state 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".