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Cited by 440

The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.

Oxygen plasma treatment for reducing hydrophobicity of a sealed polydimethylsiloxane microchannel

Open access
Tan, Say Hwa, Nguyen, Nam-Trung, Chua, Yong Chin, Kang, Tae Goo·2010·Biomicrofluidics

Abstract

Rapid prototyping of polydimethylsiloxane (PDMS) is often used to build microfluidic devices. However, the inherent hydrophobic nature of the material limits the use of PDMS in many applications. While different methods have been developed to transform the hydrophobic PDMS surface to a hydrophilic surface, the actual implementation proved to be time consuming due to differences in equipment and the need for characterization. This paper reports a simple and easy protocol combining a second extended oxygen plasma treatments and proper storage to produce usable hydrophilic PDMS devices. The results show that at a plasma power of 70 W, an extended treatment of over 5 min would allow the PDMS surface to remain hydrophilic for more than 6 h. Storing the treated PDMS devices in de-ionized water would allow them to maintain their hydrophilicity for weeks. Atomic force microscopy analysis shows that a longer oxygen plasma time produces a smoother surface.

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Crossref · 10.1063/1.3466882

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Metadata

Authors
Tan, S. H., Nguyen, N.-T., Chua, Y. C., & Kang, T. G.
Venue
Biomicrofluidics
Published
2010-09-01
Volume
4
Issue
3
Pages
32204-32204
ISSN
1932-1058 (ISSN-L)
OpenAlex
W1965500282
Cited by
440
References
19
FWCI
4.73
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Oxygen plasma treatment for reducing hydrophobicity of a sealed polydimethylsiloxane microchannel. https://app.cochat.ai/papers/10.1063/1.3466882
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Tan, S. H., Nguyen, N.-T., Chua, Y. C., & Kang, T. G. (2010). Oxygen plasma treatment for reducing hydrophobicity of a sealed polydimethylsiloxane microchannel. Biomicrofluidics. https://doi.org/10.1063/1.3466882

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