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Updated: Jul 3, 2026

Microfluidic Chips Controlled with Elastomeric Microvalve Arrays

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Published on: October 1, 2007

Microfluidic networks using isotachophoresis.

Alexandre S Avaro^1,2, Shahab Mirjalili^1,3, Andrew D Griffiths²

¹Department of Mechanical Engineering, Stanford University, Stanford, CA 94305.

Proceedings of the National Academy of Sciences of the United States of America

|November 4, 2025

Summary

This summary is machine-generated.

New microfluidic networks use isotachophoresis (ITP) for automated sample handling without moving parts. This electrokinetic process enables complex fluidic control for applications like parallel biochemical reactions.

Keywords:

electrophoresis integrated devices isotachophoresis microfluidics networks

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Area of Science:

Microfluidics
Analytical Chemistry
Biotechnology

Background:

Microfluidic technologies enable advanced chemical and biological analysis.
Complex fluidic architectures are crucial for enhanced functionality and parallelization.
Existing systems often require moving parts for intricate fluid control.

Purpose of the Study:

To introduce a novel class of microfluidic networks based on isotachophoresis (ITP).
To develop a theoretical framework for describing dynamic ITP networks.
To demonstrate the capabilities of ITP networks for automated sample manipulation and analysis.

Main Methods:

Developed a theoretical framework coupling 1D ITP description with 2D transient graphs.
Created numerical simulations of branched ITP circuits.
Experimentally built and controlled ITP networks to study sample dynamics.

Main Results:

Demonstrated ITP networks can automatically split and merge sample zones.
Validated a model that accurately captures experimentally observed sample dynamics.
Showcased an ITP network controlling parallel CRISPR-Cas enzymatic reactions.

Conclusions:

ITP-based microfluidic networks offer a powerful, reconfigurable platform for automated analysis.
These systems provide complex sample manipulation with minimal external control.
The described framework supports diverse, highly complex microfluidic topologies for lab-on-a-chip integration.