Top-Down Approaches for 10 nm-Scale Nanochannel: Toward Exceptional H2S Detection.

Metal oxide semiconductors (MOS) have proven to be most powerful sensing materials to detect hydrogen sulfide (H2S), achieving part per billion (ppb) level sensitivity and selectivity. However, there has not been a way of extending this approach to the top-down H2S sensor fabrication process, completely limiting their commercial-level productions. In this study, we developed a top-down lithographic process of a 10 nm-scale SnO2 nanochannel for H2S sensor production. Due to high-resolution (15 nm thickness) and high aspect ratio (>20) structures, the nanochannel exhibited highly sensitive H2S detection performances (Ra/Rg = 116.62, τres = 31 s at 0.5 ppm) with selectivity (RH2S/Racetone = 23 against 5 ppm acetone). In addition, we demonstrated that the nanochannel could be efficiently sensitized with the p-n heterojunction without any postmodification or an additional process during one-step lithography. As an example, we demonstrated that the H2S sensor performance can be drastically enhanced with the NiO nanoheterojunction (Ra/Rg = 166.2, τres = 21 s at 0.5 ppm), showing the highest range of sensitivity demonstrated to date for state-of-the-art H2S sensors. These results in total constitute a high-throughput fabrication platform to commercialize the H2S sensor that can accelerate the development time and interface in real-life situations.

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 H2 sensing in six different metallic channels (Au, Cu, Mo, Ni, Pt, Pd) and demonstrate that deep neural network can enhance the sensing capabilities for H2 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.