Issue 26, 2024

A salivary urea sensor based on a microsieve disposable gate AlGaN/GaN high electron mobility transistor

Abstract

The abundant bio-markers in saliva provide a new option for non-invasive testing. However, due to the presence of impurities in the saliva background, most of the existing saliva testing methods rely on pre-processing, which limits the application of saliva testing as a convenient means of testing in daily life. Herein, a disposable-gate AlGaN/GaN high electron mobility transistor (HEMT) biosensor integrated with a micro-sieve was introduced to solve the problem of signal interference caused by charged impurities in saliva for HEMT based biosensors, where the micro-sieve was utilized as a pre-treatment unit to remove large particles of impurities from saliva through the size effect and thus greatly improving the accuracy of detection. The experimental results showed that the HEMT based biosensor has excellent linearity (R2 = 0.9977) and a high sensitivity of 6.552 μA dec−1 for urea sensing from 1 fM to 100 mM in 0.1× PBS solution. When it comes to artificial saliva detection, compared to the HEMT sensor without the micro-sieve (sensitivity = 3.07432 μA dec−1), the sensitivity of the HEMT sensor integrated with the micro-sieve showed almost no change. Moreover, to verify that urea can be detected in actual saliva, urea is sensed directly in human saliva. The addition of the microsieve module provides a new way for biosensors to detect specific markers in saliva in real time, and the designed HEMT biosensor with the microsieve function has a wide range of application potential in rapid saliva detection.

Graphical abstract: A salivary urea sensor based on a microsieve disposable gate AlGaN/GaN high electron mobility transistor

Article information

Article type
Paper
Submitted
28 Mar 2024
Accepted
24 May 2024
First published
27 May 2024

Anal. Methods, 2024,16, 4381-4386

A salivary urea sensor based on a microsieve disposable gate AlGaN/GaN high electron mobility transistor

G. Yang, B. Xu, H. Chang, Z. Gu and J. Li, Anal. Methods, 2024, 16, 4381 DOI: 10.1039/D4AY00551A

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