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In Vitro Multiparametric Cellular Analysis by Micro Organic Charge-modulated Field-effect Transistor Arrays

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Complementary Metal-Oxide-Semiconductor Potentiometric Field-Effect Transistor Array Platform Using Sensor Learning

Nicolas Moser¹, Chi Leng Leong², Yuanqi Hu¹

¹Department of Electrical and Electronic Engineering and Institute of Biomedical Engineering, Imperial College London, London SW7 2AZ, United Kingdom.

Analytical Chemistry

|March 7, 2020

Summary

This study introduces a novel sensor array for simultaneous multi-ion imaging, detecting potassium, sodium, calcium, and pH using ion-sensitive field-effect transistors (ISFETs) and machine learning. The platform demonstrates high accuracy in real-world samples, paving the way for advanced biosensing applications.

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Multi-analyte Biochip MAB Based on All-solid-state Ion-selective Electrodes ASSISE for Physiological Research

Multi-analyte Biochip MAB Based on All-solid-state Ion-selective Electrodes ASSISE for Physiological Research

Published on: April 18, 2013

Area of Science:

Materials Science
Biomedical Engineering
Analytical Chemistry

Background:

Ion-sensitive field-effect transistors (ISFETs) are crucial for electrochemical sensing.
Accurate, real-time multi-ion detection in complex biological samples remains a challenge.
Sensor-learning techniques offer potential for enhancing the performance of ISFET arrays.

Purpose of the Study:

To develop and validate an ISFET array platform for concurrent imaging of multiple ions (K+, Na+, Ca2+, H+).
To employ sensor-learning algorithms for pixel calibration and multi-ion quantification.
To assess the platform's performance in biological samples and compare it with established methods.

Main Methods:

Fabrication of a 1024-pixel ISFET array with selective ionophore membranes and bare pixels.
Offline training of the sensor array using k-means clustering and DBSCAN algorithms.
Inducing single-ion concentration changes to map pixel sensitivity.
Validation using brain dialysate fluid and comparison with spectrometry.

Main Results:

Achieved high accuracy for K+ (3.7% error), Na+ (4.6% error), and pH (1.8% error).
Demonstrated versatility with Ca2+ detection, despite a higher error rate (24.2%).
Successfully validated the platform with a complex biological fluid sample.

Conclusions:

The developed ISFET array with sensor learning enables accurate multi-ion imaging.
This platform shows promise for real-time monitoring of physiologically relevant ions in biological fluids.
The approach offers a versatile and potentially cost-effective alternative to traditional analytical techniques.