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Ultrasensitive Detection of Biomarkers by Using a Molecular Imprinting Based Capacitive Biosensor

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Published on: February 16, 2018

Acetone Sensor Based on FAIMS-MEMS.

Junna Zhang¹, Cheng Lei¹, Ting Liang¹

¹State Key Laboratory of Dynamic Measurement Technology, North University of China, Taiyuan 030051, China.

|December 24, 2021

Summary

This summary is machine-generated.

This study introduces a novel non-invasive sensor for detecting type 1 diabetes by measuring exhaled acetone. The developed FAIMS-MEMS gas sensor offers high sensitivity and rapid detection, addressing limitations of current invasive methods.

Keywords:

FAIMS-MEMS acetone diabetes reproducibility stability

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Area of Science:

Biomedical Engineering
Chemical Sensing
Diabetes Diagnostics

Background:

Invasive diabetes testing poses challenges like large blood draws and long wait times.
Type 1 diabetes patients exhibit elevated acetone levels in their breath.
Current diagnostic methods lack dynamic and non-invasive capabilities.

Purpose of the Study:

To develop a highly sensitive, non-invasive gas sensor for detecting type 1 diabetes.
To utilize the principle of increased acetone exhalation in type 1 diabetic patients.
To overcome the limitations of current invasive diabetes testing methods.

Main Methods:

Design and fabrication of a Field Asymmetric Ion Mobility Spectrometry-Microelectromechanical Systems (FAIMS-MEMS) gas sensor.
Utilization of MEMS processes including photolithography, etching, and sputtering for sensor preparation.
Development of a test system for detecting acetone gas in the breath.

Main Results:

The FAIMS-MEMS sensor demonstrates significant voltage changes above 1.8 ppm acetone.
Achieved a detection accuracy of approximately 0.02 ppm/mV with a response time of 3 seconds.
Exhibited excellent repeatability, stability, and a recovery time of 4 seconds under 20 ppm acetone.

Conclusions:

The developed FAIMS-MEMS gas sensor is a promising tool for non-invasive type 1 diabetes detection.
The sensor's high sensitivity, fast response, and stability overcome traditional testing drawbacks.
This technology offers a significant advancement in diabetes diagnostics and monitoring.