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Updated: Nov 1, 2025

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

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

Published on: September 19, 2017

Optofluidic Flow-Through Biosensor Sensitivity - Model and Experiment.

Joel G Wright¹, Md Nafiz Amin², Gopikrishnan G Meena²

¹Department of Electrical and Computer Engineering, Brigham Young University, Provo, UT, 84602, USA.

Journal of Lightwave Technology : a Joint IEEE/OSA Publication

|June 28, 2021

Summary

This summary is machine-generated.

We developed a predictive model for fluorescence-based optical biosensors. This model accurately simulates detected signals, aiding in pathogen detection and biosensor design.

Keywords:

Anti-resonant reflecting optical waveguides (ARROW)integrated waveguides model design optofluidics predictive simulation

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Area of Science:

Optofluidics
Biosensing
Optical Engineering

Background:

Fluorescence-based optical biosensors are crucial for detecting analytes like pathogens.
Optofluidic waveguides offer a platform for miniaturized and sensitive biosensing.
Accurate predictive models are needed to optimize biosensor design and performance.

Purpose of the Study:

To present a predictive model and simulation for fluorescence-based optical biosensors.
To validate the model against experimental data for different biosensor designs.
To demonstrate the model's utility in pre-fabrication design and sensitivity prediction.

Main Methods:

Development of a predictive model based on biosensor geometry and optofluidic principles.
Simulation of detected signals from the fluorescence-based optical biosensor.
Comparison of model predictions with experimental results from three distinct biosensor designs.

Main Results:

The model successfully predicts signal statistics for fluorescence-based optical biosensors.
Simulated signal statistics closely matched those obtained from physical tests.
The model demonstrated accuracy across three different biosensor configurations.

Conclusions:

The developed model is a valuable tool for predicting the performance of optofluidic biosensors.
The model can guide pre-fabrication design choices and estimate detection sensitivity.
This approach enhances the development of efficient and sensitive biosensing technologies.