Finding Hidden Signals in Chemical Sensors Using Deep Learning.

Achieving high signal-to-noise ratio in chemical and biological sensors enables accurate detection of target analytes. Unfortunately, below the limit of detection (LOD), it becomes difficult to detect the presence of small amounts of analytes and extract useful information via any of the conventional methods. In this work, we examine the possibility of extracting "hidden signals" using deep neural network to enhance gas sensing below the LOD region. As a test case system, we conduct experiments for H-2 sensing in six different metallic channels (Au, Cu, Mo, Ni, Pt, Pd) and demonstrate that deep neural network can enhance the sensing capabilities for H-2 concentration below the LOD. We demonstrate that this technique could be universally used for different types of sensors and target analytes. Our approach can extract new information from the hidden signals, which can be crucial for next-generation chemical sensing applications and analytical chemistry.