Selective monitoring of natural gas sulphur-based odorant mixture of t-butyl mercaptan and methyl ethyl sulphide using an array of microfluidic gas sensors.

A natural gas (NG) odorization system requires continuous monitoring as well as an optimal injection to satisfy the odorization guidelines, minimize over-odorization, and prevent hazardous gas leaks. NG consists of hydrocarbons such as methane, odorants such as tert-butyl mercaptan, and other sulphur-based VOCs such as hydrogen sulphide; therefore, selectivity is paramount for the reliable and accurate monitoring of odorants. In this study, we developed a portable device integrated with an array of five different sensors to detect a mixture of tert-butyl mercaptan and methyl ethyl sulphide for a concentration range of 1 ppm to 10 ppm. A machine learning model was developed to predict the presence and concentration of NG odorants from the sensor data. The best-performing sensors in the array achieved high sensitivity and selectivity indicators (measured using the Davies-Bouldin index) of 0.3667 (1/ppm) and 0.125, respectively. The sensor system achieved a classification accuracy of 98.75% between NG odorants and hydrogen sulphide, with an overall Mean Squared Error (MSE) and R2 error (for the regression model) of 0.50 and 95.16%. These results indicate that the developed portable device and the machine learning model have promising applications for the selective monitoring of NG odorants.

Selective detection of VOCs using microfluidic gas sensor with embedded cylindrical microfeatures coated with graphene oxide.

Volatile organic compounds (VOCs) are major environmental pollutants. Exposure to VOCs has been associated with adverse health outcomes. The monitoring of hazardous VOCs is a vital step towards identifying their presence and preventing the risk of acute or chronic exposure and polluting the environment. One of the challenges associated with monitoring VOCs is selectivity of the sensor. Microfluidic gas sensors offer selective and sensitive detection capabilities that have been recently applied for detection of VOCs. In this study, we achieve improved selectivity for detection of a range of VOCs by adding micro- and nanofeatures to the microchannel of microfluidic gas sensors. First, microfeatures are embedded into the microchannel and their geometries are optimized using Taguchi design of experiment method. In the next step the microfeatures embedded microchannel is coated with graphene oxide, to increase the surface to volume ratio by introducing nanofeatures to the surfaces. The nano- and microfeatures are characterized by SEM, XPS, and water contact angle measurement. Finally, the changes in the sensor response are compared to plain microfluidic gas sensor, the results show an average of 64.4% and 120.9% improvement in the selectivity of the sensor with microfeatures and both nano- and microfeatures, respectively.

A selective polypyrrole-based sub-ppm impedimetric sensor for the detection of dissolved hydrogen sulfide and ammonia in a mixture.

An impedance-transducer sensor was developed for in situ detection of hydrogen sulfide (H2S) and ammonia (NH3) in aqueous media. Using cyclic voltammetry (CV), polypyrrole (PPy) was deposited on the surface of the microfabricated interdigitated gold electrode. Due to the proton acid doping effect of H2S on PPy and ionic conduction of the film, the sensor showed a decreasing impedance response to H2S unlike other reducing chemicals, i.e., ammonia (NH3). The recorded faradaic data was then associated with an equivalent circuit and compared with that of NH3 to examine the selectivity of the sensor. An electrochemical impedance spectroscopy (EIS) analysis was applied to the mixture of H2S and NH3 prepared at different ratios for the concentrations ranging from 2 ppm to 20 ppm (below 2-ppm, no response was observed due to the formation of NH4HS, not sensible with PPy). The principal component analysis (PCA) was used to train a real-time prediction model for both classification (for the type of the analyte) and regression (the concentration of the analyte). The results showed the high performance of the sensor in determining individual analytes while the model was able to accurately predict the amount of H2S and NH3 in the mixture.