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Updated: Aug 31, 2025

Computer Numerical Control Micromilling of a Microfluidic Acrylic Device with a Staggered Restriction for Magnetic Nanoparticle-Based Immunoassays

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An Aqueous Analog MAC Machine.

Woo-Bin Jung¹, Han Sae Jung¹, Jun Wang¹

¹John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA.

Advanced Materials (Deerfield Beach, Fla.)

|August 23, 2022

Summary

This summary is machine-generated.

Researchers developed a 16x16 array of ionic transistors in aqueous solution. This bioinspired platform performs analog multiply-accumulate operations, paving the way for low-power artificial neural networks.

Keywords:

analog computing artificial neural networks bioinspired engineering electrochemical transistors electrochemistry ionic transistors neuromorphic engineering

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Area of Science:

Electrochemistry
Bioinspired computing
Materials science

Background:

Current research focuses on individual ionic devices, not complex circuits.
Aqueous ionic systems offer potential for bioinspired information processing.
Developing integrated ionic circuits is crucial for advanced applications.

Purpose of the Study:

To develop and demonstrate a large-scale ionic transistor array.
To achieve analog multiply-accumulate (MAC) operations using ionic transistors.
To advance the field of aqueous ionics for computing.

Main Methods:

Fabrication of a 16x16 array of ionic transistors with concentric ring and central disk electrodes.
Utilizing electrochemical principles in an aqueous quinone solution for transistor operation.
Modulating disk electrode current via electrochemical gating by the ring electrode pair.

Main Results:

The ionic transistors successfully performed analog multiply-accumulate operations.
The circuit demonstrated effective summation of currents at a global reference electrode.
The transistor design enabled gating of disk current by ring electrode voltage.

Conclusions:

The developed ionic circuit represents a significant step toward sophisticated aqueous ionics.
This work demonstrates the feasibility of large-scale ionic circuits for analog computing.
The platform shows promise for low-power artificial neural network applications.