Boron Nitride Nanosheet-Based Gel Polymer Electrolytes for Stable Lithium Metal Batteries
Bibliographic record
Abstract
In order to address the safety concerns of conventional carbonate liquid electrolytes in lithium (Li) batteries, porous gel polymer electrolytes (GPEs) can encapsulate the solution while providing good electrolyte–electrode contact. In this work, a GPE is designed and fabricated with multifunctional exfoliated two-dimensional (2D) hexagonal boron nitride nanosheets (BNs), leading to improved thermal stability, ionic conductivity, Li + transference number, mechanical strength, and dendrite-suppressing properties for Li metal batteries. Through phase inversion, a high porosity and electrolyte uptake are achieved while maintaining a stable film structure. Utilizing a binary polymer mixture of polyvinylidene fluoride (PVDF) and poly(ethylene oxide) (PEO) doped with exfoliated BN flakes, the final BN-GPE at 3.6 wt % BN can effectively suppress dendrite growth through multiple charge/discharge cycles with a high ionic conductivity of 3.03 × 10 –3 S/cm at ambient conditions while showing a high Li transference number of 0.60. The Li metal battery cell performance with this GPE demonstrates a strong initial capacity at 82.9 mAh/g at 0.5C and improves capacity over the undoped GPE by 22% after 45 cycles.
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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".