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Cited by 298

The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.

Discrete elements for 3D microfluidics

Open access
Bhargava, Krisna C., Thompson, Bryant, Malmstadt, Noah·2014·Proceedings of the National Academy of Sciences

Abstract

Significance: Microfluidic systems promise to improve the analysis and synthesis of materials, biological or otherwise, by lowering the required volume of fluid samples, offering a tightly controlled fluid-handling environment, and simultaneously integrating various chemical processes. To build these systems, designers depend on microfabrication techniques that restrict them to arranging their designs in two dimensions and completely fabricating their design in a single step. This study introduces modular, reconfigurable components containing fluidic and sensor elements adaptable to many different microfluidic circuits. These elements can be assembled to allow for 3D routing of channels. This assembly approach allows for the application of network analysis techniques like those used in classical electronic circuit design, facilitating the straightforward design of predictable flow systems.

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Registry

Crossref · 10.1073/pnas.1414764111

Cites

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Metadata

Authors
Bhargava, K. C., Thompson, B., & Malmstadt, N.
Venue
Proceedings of the National Academy of Sciences
Published
2014-09-22
Volume
111
Issue
42
Pages
15013-15018
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W4246354122
Cited by
298
References
16
FWCI
18.37
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Discrete elements for 3D microfluidics. https://app.cochat.ai/papers/10.1073/pnas.1414764111
The paper
Bhargava, K. C., Thompson, B., & Malmstadt, N. (2014). Discrete elements for 3D microfluidics. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1414764111

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.