Polyacrylonitrile Porous Membrane-Based Gel Polymer Electrolyte by In Situ Free-Radical Polymerization for Stable Li Metal Batteries
Bibliographic record
Abstract
Because of their high ionic conductivity, utilizing gel polymer electrolytes (GPEs) is thought to be an effective way to accomplish high-energy-density batteries. Nevertheless, most GPEs have poor adaptability to Ni-rich cathodes to alleviate the problem of inevitable rapid capacity decay during cycling. Therefore, to match LiNi 0.8 Co 0.1 Mn 0.1 O 2 (NCM811), we applied pentaerythritol tetraacrylate (PETEA) monomers to polymerize in situ in a polyacrylonitrile (PAN) membrane to obtain GPEs (PETEA-TCGG-PAN). The impedance variations and key groups during the in situ polymerization of PETEA-TCGG-PAN are investigated in detail. PETEA-TCGG-PAN with a high lithium-ion transference number (0.77) exhibits an electrochemical decomposition voltage of 5.15 V. Noticeably, the NCM811|PETEA-TCGG-PAN|Li battery can cycle at 2 C for 120 cycles with a capacity retention rate of 89%. Even at 6 C, the discharge specific capacity is able to reach 101.47 mAh g –1 . The combination of LiF and Li 2 CO 3 at the CEI interface is the reason for the improved rate performance. Moreover, when commercialized LFP is used as the cathode, the battery can also cycle stably for 150 cycles at 0.5 C . PETEA and PAN can together foster the transportation of Li + with the construction of a fast ion transport channel, making a contribution to stable charge–discharge of the above batteries. This study provides an innovative design philosophy for designing in situ GPEs in high-energy-density 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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".