Fabrication of metal-organic framework-based nanofibrous separator via one-pot electrospinning strategy
Bibliographic record
Abstract
Metal-organic framework (MOF)/polymer composites have attracted extensive attention in the recent years. However, it still remains challenging to efficiently and effectively fabricate these composite materials. In this study, we propose a facile one-pot electrospinning strategy for preparation of HKUST-1/polyacrylonitrile (PAN) nanofibrous membranes from a homogeneous stock solution containing HKUST-1 precursors and PAN. MOF crystallization and polymer solidification occur simultaneously during the electrospinning process, thus avoiding the issues of aggregation and troublesome multistep fabrication of the conventional approach. The obtained HKUST-1/PAN electrospun membranes show uniform MOF distribution throughout the nanofibers and yield good mechanical properties. The membranes are used as separators in Li-metal full batteries under harsh testing conditions, using an ultrathin Li-metal anode, a high mass loading cathode, and the HKUST-1/PAN nanofibrous separator. The results demonstrate significantly improved cycling performance (capacity retention of 83.1% after 200 cycles) under a low negative to positive capacity ratio (N/P ratio of 1.86). The improvement can be attributed to an enhanced wettability of the separator towards electrolyte stemmed from the nanofibrous structure, and a uniform lithium ion flux stabilized by the open metal sites of uniformly distributed HKUST-1 particles in the membrane during cycling.
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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".