Chemical sensing with a mid-infrared metasurface microspectrometer

Considerable demand exists for smart, low-cost and portable gas sensors. Although IR spectroscopy-based sensors work well for this application, their deployment is limited by their size and cost. We demonstrate a smart, low-cost, multi-gas sensing system comprising a mid-infrared microspectrometer and a machine learning algorithm. The microspectrometer is a metasurface filter array integrated with an IR camera. A machine learning algorithm is trained to analyze the data from the microspectrometer and predict the gases present. The system detects greenhouse gases carbon dioxide and methane at concentrations ranging from 10 - 100% with 100% accuracy. It also detects hazardous gases at low concentration levels with an accuracy of 98.4%. We detect ammonia at a concentration of 100 ppm. We detect methyl-ethyl-ketone at the permissible exposure limit (200 ppm). This work demonstrates the viability of using machine learning with IR spectrocopy to deliver a smart and low-cost multi-gas sensing platform.