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Finding Hidden Signals in Chemical Sensors Using Deep Learning.

Soo-Yeon Cho^1,2,3, Youhan Lee^1,2, Sangwon Lee^1,2

¹Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, Daehak-ro 291, Yuseong-gu, Daejeon 34141, Republic of Korea.

Analytical Chemistry

|April 15, 2020

Summary

Deep neural networks extract hidden signals from gas sensors below the limit of detection (LOD). This AI approach enhances hydrogen (H2) sensing, offering a universal method for next-generation chemical detection.

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Area of Science:

Analytical Chemistry
Materials Science
Artificial Intelligence

Background:

High signal-to-noise ratio is crucial for accurate analyte detection in chemical sensors.
Detecting analytes below the limit of detection (LOD) using conventional methods is challenging.
Extracting meaningful data from low-concentration analytes requires advanced techniques.

Purpose of the Study:

To investigate the use of deep neural networks (DNNs) for enhancing gas sensing below the LOD.
To demonstrate the capability of DNNs in extracting "hidden signals" from sensor data.
To explore the universal applicability of this DNN-based approach across various sensors and analytes.

Main Methods:

Experimental setup for H2 gas sensing across six metallic channels (Au, Cu, Mo, Ni, Pt, Pd).
Application of deep neural network algorithms to analyze sensor signals.
Evaluation of DNN performance in detecting H2 concentrations below the established LOD.

Main Results:

DNNs successfully enhanced H2 sensing capabilities below the LOD in metallic channels.
The "hidden signals" below the LOD contained extractable and useful information.
The developed technique showed potential for universal application in diverse sensing scenarios.

Conclusions:

Deep neural networks offer a powerful method for overcoming the LOD limitations in chemical sensing.
This AI-driven approach can unlock new information from low-concentration analytes.
The technique holds significant promise for advancing future chemical sensing technologies and analytical chemistry.