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Dry Film Photoresist-based Electrochemical Microfluidic Biosensor Platform: Device Fabrication, On-chip Assay Preparation, and System Operation

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Label-Free Electrochemical Methods for Disease Detection.

Kira L Rahn^1,2, Umesha Peramune², Tianyi Zhang²

¹Department of Chemistry, Colorado State University, Fort Collins, Colorado, USA.

Annual Review of Analytical Chemistry (Palo Alto, Calif.)

|February 28, 2023

Summary

Label-free electrochemical biosensors offer a low-cost, sensitive, and portable method for diagnostics. This review details four key mechanisms and explores multiplexing and machine learning for enhanced information.

Keywords:

analytical chemistry biosensing diagnostics disease detection electrochemistry label-free

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Area of Science:

Electrochemistry
Biosensing
Analytical Chemistry

Background:

Label-free electrochemical biosensing combines the benefits of label-free detection (low cost, simplicity) with electrochemical analysis (sensitivity, portability).
Existing methods often rely on labels, increasing complexity and cost.
There is a need for efficient and accessible biosensing platforms.

Purpose of the Study:

To review and categorize the primary mechanisms of label-free electrochemical biosensing.
To summarize recent advancements and discuss the pros and cons of each mechanism.
To explore future directions for enhanced diagnostic capabilities.

Main Methods:

Identification and description of four core label-free electrochemical biosensing mechanisms.
Review of recent literature and technological advancements.
Analysis of advantages and disadvantages of each sensing strategy.

Main Results:

Four mechanisms were identified: electrode surface blocking, enhanced surface access, redox probe affinity modulation, and ion/electron transport changes.
Recent progress in each mechanism was summarized, with comparative analysis of their strengths and weaknesses.
The potential of multiplex analysis and machine learning for improved diagnostics was examined.

Conclusions:

Label-free electrochemical biosensing offers versatile platforms with distinct operational mechanisms.
Multiplexing and machine learning represent promising avenues for advancing diagnostic power.
Further development can lead to more sophisticated and informative biosensing devices.