Deep Learning-Assisted Colorimetric/Electrical Dual-Sensing System for Ultrafast Detection of Hydrogen Sulfide.

This study presents a colorimetric/electrical dual-sensing system (CEDS) for low-power, high-precision, adaptable, and real-time detection of hydrogen sulfide (H2S) gas. The lead acetate/poly(vinyl alcohol) (Pb(Ac)2/PVA) nanofiber film was transferred onto a polyethylene terephthalate (PET) flexible substrate by electrospinning to obtain colorimetric/electrical sensors. The CEDS was constructed to simultaneously record both the visual and electrical response of the sensor, and the improved Manhattan segmentation algorithm and deep neural network (DNN) were used as its intelligent algorithmic aids to achieve quantitative exposure to H2S. By exploring the mechanism of color change and resistance response of the sensor, a dual-sensitivity mechanism explanation model was proposed to verify that the system, as a dual-mode parallel system, can adequately solve the sensor redundancy problem. The results show that the CEDS can achieve a wide detection range of H2S from 0.1-100 ppm and identify the H2S concentration in 4 s at the fastest. The sensor can be stabilized for 180 days with excellent selectivity and a low limit of detection (LOD) to 0.1 ppm of H2S. In addition, the feasibility of the CEDS for measuring H2S levels in underground waterways was validated. This work provides a new method for adaptable, wide range of applications and low-power, high-precision H2S gas detection.