Polyacrylonitrile‐Reinforced Composite Gel Polymer Electrolytes for Stable Potassium Metal Anodes
Bibliographic record
Abstract
Abstract The potassium‐ion battery (PIB) is an emerging energy storage technology due to its potential low cost and reasonably high energy density. However, PIB suffers from severe potassium (K) dendrites growth and even short circuiting caused by the high reactivity of K metal. To address these challenges, this work develops a robust composite gel polymer electrolyte (CGPE), named poly(vinylidene fluoride‐hexafluoropropylene) potassium bis(fluorosulfonyl)imide polyacrylonitrile (PVDF‐HFP‐KFSI@PAN). It is found that the introduction of PAN nanofibers not only improves the mechanical properties but also widens the electrochemical stability window of the CGPE. As a result, the CGPE is much more effective in regulating K stripping/plating and enabling stable K metal anode. K metal symmetrical cells with CGPE exhibit a lifetime of over 1200 h at the current density of 0.5 mA cm−2, in contrast to 22 h for PVDF‐HFP‐KFSI and 185 h for a conventional glass fiber separator. The main reasons for the excellent performance of K metal cells with CGPE are attributed to the suppressed K dendrite growth, good structural integrity electrochemical stability of the CGPE, and stable KF‐rich solid electrolyte interphase layer at the electrode‐CGPE interface. It is expected that this facile strategy to stabilize K metal will pave the way for safer and more durable K 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 | 0.000 |
| 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".