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 LiNi0.8Co0.1Mn0.1O2 (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 2C for 120 cycles with a capacity retention rate of 89%. Even at 6C, the discharge specific capacity is able to reach 101.47 mAh g–1. The combination of LiF and Li2CO3 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 0.5C. 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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".