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 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 | 0.000 |
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 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".