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Cited by 1,731

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Metal-organic framework nanosheets as building blocks for molecular sieving membranes

Peng, Yuan, Li, Yanshuo, Ban, Yujie, Jin, Hua, Jiao, Wenmei, Liu, Xinlei, Yang, Weishen·2014·Science

Abstract

Layered metal-organic frameworks would be a diverse source of crystalline sheets with nanometer thickness for molecular sieving if they could be exfoliated, but there is a challenge in retaining the morphological and structural integrity. We report the preparation of 1-nanometer-thick sheets with large lateral area and high crystallinity from layered MOFs. They are used as building blocks for ultrathin molecular sieve membranes, which achieve hydrogen gas (H 2 ) permeance of up to several thousand gas permeation units (GPUs) with H 2 /CO 2 selectivity greater than 200. We found an unusual proportional relationship between H 2 permeance and H 2 selectivity for the membranes, and achieved a simultaneous increase in both permeance and selectivity by suppressing lamellar stacking of the nanosheets.

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Crossref · 10.1126/science.1254227

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Metadata

Authors
Peng, Y., Li, Y., Ban, Y., Jin, H., Jiao, W., Liu, X., & Yang, W.
Venue
Science
Published
2014-12-12
Volume
346
Issue
6215
Pages
1356-1359
ISSN
  • 0036-8075 (ISSN-L)
  • 1095-9203
OpenAlex
W2040057498
Cited by
1,731
References
1
FWCI
109.47
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Metal-organic framework nanosheets as building blocks for molecular sieving membranes. https://app.cochat.ai/papers/10.1126/science.1254227
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Peng, Y., Li, Y., Ban, Y., Jin, H., Jiao, W., Liu, X., & Yang, W. (2014). Metal-organic framework nanosheets as building blocks for molecular sieving membranes. Science. https://doi.org/10.1126/science.1254227

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.