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Cited by 293

The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.

Molecular sieving using nanofilters: Past, present and future

Open access
Han, Jongyoon, Fu, Jianping, Schoch, Reto B.·2008·Lab Chip

Abstract

Filtration of molecules by nanometer-sized structures is ubiquitous in our everyday life, but our understanding of such molecular filtration processes is far less than desired. Until recently, one of the main reasons was the lack of experimental methods that can help provide detailed, microscopic pictures of molecule-nanostructure interactions. Several innovations in experimental methods, such as nuclear track-etched membranes developed in the 70s, and more recent development of nanofluidic molecular filters, played pivotal roles in advancing our understanding. With the ability to make truly molecular-scale filters and pores with well-defined sizes, shapes, and surface properties, now we are well positioned to engineer better functionality in molecular sieving, separation and other membrane applications. Reviewing past theoretical developments (often scattered across different fields) and connecting them to the most recent advances in the field would be essential to get a full, unified view on this important engineering question.

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Crossref · 10.1039/b714128a

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Metadata

Authors
Han, J., Fu, J., & Schoch, R. B.
Venue
Lab Chip
Published
2008
Volume
8
Issue
1
Pages
23-33
ISSN
  • 1473-0189 (ISSN-L)
  • 1473-0197
OpenAlex
W2168031077
Cited by
293
References
47
FWCI
14.17
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Molecular sieving using nanofilters: Past, present and future. https://app.cochat.ai/papers/10.1039/b714128a
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Han, J., Fu, J., & Schoch, R. B. (2008). Molecular sieving using nanofilters: Past, present and future. Lab Chip. https://doi.org/10.1039/b714128a

Data sources

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.