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Cited by 328

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

Rapid preparation of giant unilamellar vesicles.

Open access
Moscho, A, Orwar, O, Chiu, D T, Modi, B P, Zare, R N·1996·Proceedings of the National Academy of Sciences

Abstract

We report here a rapid evaporation method that produces in high yield giant unilamellar vesicles up to 50 microns in diameter. The vesicles are obtained after only 2 min and can be prepared from different phospholipids, including L-alpha-phosphatidylcholine (lecithin), dipalmitoleoyl L-alpha-phosphatidylcholine, and beta-arachidonoyl gamma-palmitoyl L-alpha-phosphatidylcholine. Vesicles can be produced in distilled water and in Hepes, phosphate, and borate buffers in the pH range of 7.0 to 11.5 with ionic strengths up to 50 mM. The short preparation time allows encapsulation of labile molecular targets or enzymes with high catalytic activities. Cell-sized proteoliposomes have been prepared in which gamma-glutamyltransferase (EC 2.3.2.2) was functionally incorporated into the membrane wall.

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Crossref · 10.1073/pnas.93.21.11443

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Metadata

Authors
Moscho, A., Orwar, O., Chiu, D. T., Modi, B. P., & Zare, R. N.
Venue
Proceedings of the National Academy of Sciences
Published
1996-10-15
Volume
93
Issue
21
Pages
11443-11447
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2085371119
Cited by
328
References
1
FWCI
1.11
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Rapid preparation of giant unilamellar vesicles. https://app.cochat.ai/papers/10.1073/pnas.93.21.11443
The paper
Moscho, A., Orwar, O., Chiu, D. T., Modi, B. P., & Zare, R. N. (1996). Rapid preparation of giant unilamellar vesicles. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.93.21.11443

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.