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

Development of an AI-based image/ultrasonic convergence camera system for accurate gas leak detection in petrochemical plants

Outdoor pipeline leaks are difficult to accurately measure using existing concentration measurement systems installed in petrochemical plants owing to external air currents. Besides, leak detection is only possible for a specific gas. The purpose of this study was to develop an image/ultrasonic convergence camera system that incorporates artificial intelligence (AI) to improve pipe leak detection and establish a real-time monitoring system. Our system includes an advanced ultrasonic camera coupled with a deep learning-based object-detection algorithm trained on pipe image data from petrochemical plants. The collected data improved the accuracy of detected gas leak localization through deep learning. Our detection model achieves an mAP50 (Mean average precision calculated at an intersection over union (IoU) threshold of 0.50)score of 0.45 on our data and is able to detect the majority of leak points within a system. The petrochemical plant environment was simulated by visiting petrochemical plants and reviewing drawings, and an outdoor experimental demonstration site was established. Scenarios such as flange connection failure were set under medium-/low-pressure conditions, and the developed product was experimented under gas leak conditions that simulated leakage accidents. These experiments enabled the removal of potentially confounding surrounding noise sources, which led to the false detection of actual gas leaks using the AI piping detection technique.

A three-in-one versatile sensor for concise detecting biogenic amines and beef freshness

Biogenic amines (BAs), as important indicators for evaluating food spoilage caused by fermentation processes or microbial activities, present significant risks of food safety. Consequently, the development of a simple, sensitive, and selective detection method for amines is of great importance. In this study, we proposed a three-in-one sensor 3,6-bis(dimethylamino)-9-(ethylthio)xanthylium (PSE) for high sensitivity and selectivity detecting BAs with multimodal responses, including olfactory, colorimetric, and fluorescent signals, thus facilitating convenient real-time detection of BAs. Mechanism study indicated that the nucleophilic substitution of PSE with BAs induced such rapid multi-responses with a low detection limit (LOD = 0.03 μM). We further fabricated PSE loaded paper for portable detection of BAs vapors. And the accurate determination of BAs levels is achieved through analyzing the RGB color mode. Finally, we successfully applied these test strips for non-destructive assessing meat beef freshness with the assistance of a smartphone in on-site scenarios.

Highly selective molecularly imprinted polymer nanoparticles (MIP NPs)-based microfluidic gas sensor for tetrahydrocannabinol (THC) detection

A highly selective microfluidic integrated metal oxide gas sensor for THC detection is reported based on MIP nanoparticles (MIP NPs). We synthesized MIP NPs with THC recognition sites and coated them on a 3D-printed microfluidic channel surface. The sensitivity and selectivity of coated microfluidic integrated gas sensors were evaluated by exposure to THC, cannabidiol (CBD), methanol, and ethanol analytes in 300-700 ppm at 300 °C. For comparison, reference signals were obtained from a microfluidic channel coated with nonimprinted polymers (NIP NPs). The MIP and NIP NPs were characterized using scanning electron microscopy (SEM) and Raman spectroscopy. MIP and NIP NPs channels response data were combined and classified with 96.3% accuracy using the Fine KNN classification model in MATLAB R2021b Classification Learner App. Compared to the MIP NPs coated channel, the NIP NPs channel had poor selectivity towards THC, demonstrating that the THC recognition sites in the MIP structure enabled selective detection of THC. The findings demonstrated that the recognition sites of MIP NPs properly captured THC molecules, enabling the selective detection of THC compared to CBD, methanol, and ethanol.

Microfluidic integrated gas sensors for smart analyte detection: a comprehensive review

The utilization of gas sensors has the potential to enhance worker safety, mitigate environmental issues, and enable early diagnosis of chronic diseases. However, traditional sensors designed for such applications are often bulky, expensive, difficult to operate, and require large sample volumes. By employing microfluidic technology to miniaturize gas sensors, we can address these challenges and usher in a new era of gas sensors suitable for point-of-care and point-of-use applications. In this review paper, we systematically categorize microfluidic gas sensors according to their applications in safety, biomedical, and environmental contexts. Furthermore, we delve into the integration of various types of gas sensors, such as optical, chemical, and physical sensors, within microfluidic platforms, highlighting the resultant enhancements in performance within these domains.