In Situ Synthesis of Phosphate-Based CelloMOF as a Promising Separator for Li–Ion Batteries
Bibliographic record
Abstract
Nowadays, battery separators play a critical role in determining the sustainability, electrochemical efficiency, and safety of lithium-ion batteries (LIBs). In this contribution, we developed fire-resistant composite membranes called CelloMOF by in situ grafting of metal-organic framework, ZIF-67, onto phosphorylated cellulose nanofibers (P-CNFs) followed by a vacuum filtration process akin to papermaking. The hybrid ZIF-67@P-CNF membrane exhibits superior properties than a polyolefin-based commercial separator (CS) in terms of enhanced thermal and dimensional stability, flame-retardant properties, better surface wettability, and improved electrolyte uptake. Thermal dimensional stability tests revealed that the ZIF-67@P-CNF separator maintained its structure even at 200 °C, whereas CS suffered severe shrinkage, potentially leading to internal short circuits. Combustion tests showed a peak heat release rate (PHRR) of 34.5 W/g and a total heat release (THR) of 1.61 kJ/g for ZIF-67@P-CNF, significantly lower than the PHRR (1111.82 W/g) and THR (40.89 kJ/g) of CS. The composite separator also demonstrated significantly improved wettability, with a contact angle of 32 ± 1.04°, compared to 92 ± 1.07° for CS, highlighting its hydrophilic nature. Electrochemical evaluations in LiFePO 4 /Li half-cells indicated a higher discharge capacity of 149 mA h g –1 at 0.2 C and superior capacity retention of 86% after 50 cycles, outperforming CS (145 mA h g –1 and 84%, respectively). These results underscore the potential of the ZIF-67@P-CNF membrane to advance safe, high-performance LIBs by addressing critical challenges in thermal stability, flame retardancy, and electrolyte compatibility.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".