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A numerical platform for predicting the performance of paper-based analytical devices.

Lawrence K Q Yan¹, Sze Kee Tam¹, Ka Ming Ng¹

¹Dept. of Chemical and Biological Engineering, The Hong Kong University of Science and Technology Clear Water Bay, Hong Kong. kekmng@ust.hk.

Analytical Methods : Advancing Methods and Applications

|October 21, 2024

Summary

This study introduces a numerical platform to predict paper-based analytical device performance. This tool aids in optimizing existing designs and creating new ones with minimal experiments.

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Fabricating Cotton Analytical Devices

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Published on: August 30, 2016

Fabrication of Three-dimensional Paper-based Microfluidic Devices for Immunoassays

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Published on: March 9, 2017

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

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Published on: September 19, 2017

Area of Science:

Biomedical Engineering
Chemical Engineering
Microfluidics

Background:

Paper-based analytical devices (PADs) offer low-cost diagnostics but require optimization for reliable performance.
Predicting PAD behavior often relies on empirical testing, which is time-consuming and resource-intensive.

Purpose of the Study:

To develop and validate a comprehensive numerical platform for predicting the performance of paper-based analytical devices.
To enable the optimization of existing PADs and facilitate the design of novel devices.

Main Methods:

The platform integrates physical models for capillary flow (Darcy's law), reaction kinetics, and dissolution.
It accounts for various parameters including paper substrates, biorecognition elements, detection methods (optical, electrochemical), device geometry, and sample introduction.
Performance metrics such as assay time, signal intensity, and cost are quantified.

Main Results:

The numerical platform accurately predicts the performance of various paper-based analytical devices.
Validation against literature data confirms the tool's predictive capabilities.
The platform successfully identified potential improvements for existing device designs.

Conclusions:

The developed numerical platform serves as a powerful tool for understanding and predicting PAD performance.
It significantly reduces experimental effort in the design and optimization of new paper-based analytical devices.
This computational approach accelerates the development of advanced diagnostic tools.