Microsystems & Nanoengineering                          volume  10, Article number: 74  (2024 )             Cite

Smart mid-infrared metasurface microspectrometer gas sensing system

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2024-06-14 15:00:05

Microsystems & Nanoengineering volume  10, Article number: 74 (2024 ) Cite this article

Smart, low-cost and portable gas sensors are highly desired due to the importance of air quality monitoring for environmental and defense-related applications. Traditionally, electrochemical and nondispersive infrared (IR) gas sensors are designed to detect a single specific analyte. Although IR spectroscopy-based sensors provide superior performance, their deployment is limited due to their large size and high cost. In this study, a smart, low-cost, multigas sensing system is demonstrated consisting of a mid-infrared microspectrometer and a machine learning algorithm. The microspectrometer is a metasurface filter array integrated with a commercial IR camera that is consumable-free, compact ( ~ 1 cm3) and lightweight ( ~ 1 g). The machine learning algorithm is trained to analyze the data from the microspectrometer and predict the gases present. The system detects the greenhouse gases carbon dioxide and methane at concentrations ranging from 10 to 100% with 100% accuracy. It also detects hazardous gases at low concentrations with an accuracy of 98.4%. Ammonia can be detected at a concentration of 100 ppm. Additionally, methyl-ethyl-ketone can be detected at its permissible exposure limit (200 ppm); this concentration is considered low and nonhazardous. This study demonstrates the viability of using machine learning with IR spectroscopy to provide a smart and low-cost multigas sensing platform.

There is currently considerable demand for gas sensing technology due to its numerous applications; these include atmospheric pollution monitoring, the detection of hazardous gas leaks in industry, and the detection of harmful volatile organic compounds in indoor locations1,2. Moreover, in recent years, the emergence of the Internet of Things (IoT) has spurred interest in developing smart gas sensor systems. These systems combine sensors with advanced signal processing techniques and machine learning algorithms3, enabling the resultant system to perform real-time analysis of the gases present. While much progress has been made, low-cost smart gas sensors that can simultaneously achieve low detection limits and low cross-sensitivity in situations where multiple gases need to be detected have yet to be developed. Current gas sensing techniques can be categorized as follows: electrochemical gas sensors, optical gas sensors, acoustic-based sensors, gas chromatography (GC) sensors and calorimetric-based sensors1. Electrochemical-based sensors have been attained using materials that include carbon nanotubes4, semiconductor nanowires5,6, and 2D materials such as α-MoO3, graphene and MXene7,8,9. These sensors have high sensitivity but suffer from limited lifetimes and cross-response issues10. Acoustic gas sensors typically detect gases by measuring the ultrasonic wave velocity, attenuation and acoustic impedance11; however, these sensors are limited by high power consumption. GC is commonly used for laboratory chemical analysis and has excellent chemical separation performance, with high sensitivity and selectivity12. GC systems generally have large footprints and are nonportable; thus, they are unsuitable for use as smart gas sensors. Calorimetric gas sensors detect gases based on differences in their heat of combustion13. However, this approach tends to have low sensitivity and selectivity for gas sensing. Optical gas sensing methods include fluorescent chemosensors14, nondiffractive infrared (NDIR) sensors15, and absorption spectroscopy-based sensors2. Fluorescent chemosensors convert chemical stimuli into a detectable fluorescent response. Although this technique requires low power consumption, it faces challenges such as poor reusability and slow response time16. Both NDIR and absorption spectroscopy-based gas sensing detect gases based on their mid-infrared (MIR) “fingerprints,” i.e., the unique absorption spectra of chemicals due to the molecular vibrational modes excited by infrared radiation. This approach can provide a fast response, minimal drift, high specificity, long lifetime and robustness to changes in the ambient environment10. NDIR is usually implemented by monitoring the intensity of analyte IR absorption at a single (or a few) wavelength(s) achieved by filtering the IR source to match the absorption line(s). The current workhorse tool for IR spectroscopy is the Fourier transform infrared (FTIR) spectrometer. FTIR effectively performs for gas sensing, but its platform is generally large, has high power consumption and is expensive. Thus, the development of gas sensors that use IR spectroscopy as a sensing mechanism with favorable size, weight, power consumption and cost is the topic of this paper.

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