A Portable Device for Methane Measurement Using a Low-Cost Semiconductor Sensor: Development, Calibration and Environmental Applications

Utilization of a Low-Cost Sensor Array for Mobile Methane Monitoring

The use of low-cost sensors (LCSs) for the mobile monitoring of oil and gas emissions is an understudied application of low-cost air quality monitoring devices. To assess the efficacy of low-cost sensors as a screening tool for the mobile monitoring of fugitive methane emissions stemming from well sites in eastern Colorado, we colocated an array of low-cost sensors (XPOD) with a reference grade methane monitor (Aeris Ultra) on a mobile monitoring vehicle from 15 August through 27 September 2023. Fitting our low-cost sensor data with a bootstrap and aggregated random forest model, we found a high correlation between the reference and XPOD CH4 concentrations (r = 0.719) and a low experimental error (RMSD = 0.3673 ppm). Other calibration models, including multilinear regression and artificial neural networks (ANN), were either unable to distinguish individual methane spikes above baseline or had a significantly elevated error (RMSDANN = 0.4669 ppm) when compared to the random forest model. Using out-of-bag predictor permutations, we found that sensors that showed the highest correlation with methane displayed the greatest significance in our random forest model. As we reduced the percentage of colocation data employed in the random forest model, errors did not significantly increase until a specific threshold (50 percent of total calibration data). Using a peakfinding algorithm, we found that our model was able to predict 80 percent of methane spikes above 2.5 ppm throughout the duration of our field campaign, with a false response rate of 35 percent.

Gas nanosensors for health and safety applications in mining

The ever-increasing demand for accurate, miniaturized, and cost-effective gas sensing systems has eclipsed basic research across many disciplines. Along with the rapid progress in nanotechnology, the latest development in gas sensing technology is dominated by the incorporation of nanomaterials with different properties and structures. Such nanomaterials provide a variety of sensing interfaces operating on different principles ranging from chemiresistive and electrochemical to optical modules. Compared to thick film and bulk structures currently used for gas sensing, nanomaterials are advantageous in terms of surface-to-volume ratio, response time, and power consumption. However, designing nanostructured gas sensors for the marketplace requires understanding of key mechanisms in detecting certain gaseous analytes. Herein, we provide an overview of different sensing modules and nanomaterials under development for sensing critical gases in the mining industry, specifically for health and safety monitoring of mining workers. The interactions between target gas molecules and the sensing interface and strategies to tailor the gas sensing interfacial properties are highlighted throughout the review. Finally, challenges of existing nanomaterial-based sensing systems, directions for future studies, and conclusions are discussed.

Application of Semiconductor Metal Oxide in Chemiresistive Methane Gas Sensor: Recent Developments and Future Perspectives

The application of semiconductor metal oxides in chemiresistive methane gas sensors has seen significant progress in recent years, driven by their promising sensitivity, miniaturization potential, and cost-effectiveness. This paper presents a comprehensive review of recent developments and future perspectives in this field. The main findings highlight the advancements in material science, sensor fabrication techniques, and integration methods that have led to enhanced methane-sensing capabilities. Notably, the incorporation of noble metal dopants, nanostructuring, and hybrid materials has significantly improved sensitivity and selectivity. Furthermore, innovative sensor fabrication techniques, such as thin-film deposition and screen printing, have enabled cost-effective and scalable production. The challenges and limitations facing metal oxide-based methane sensors were identified, including issues with sensitivity, selectivity, operating temperature, long-term stability, and response times. To address these challenges, advanced material science techniques were explored, leading to novel metal oxide materials with unique properties. Design improvements, such as integrated heating elements for precise temperature control, were investigated to enhance sensor stability. Additionally, data processing algorithms and machine learning methods were employed to improve selectivity and mitigate baseline drift. The recent developments in semiconductor metal oxide-based chemiresistive methane gas sensors show promising potential for practical applications. The improvements in sensitivity, selectivity, and stability achieved through material innovations and design modifications pave the way for real-world deployment. The integration of machine learning and data processing techniques further enhances the reliability and accuracy of methane detection. However, challenges remain, and future research should focus on overcoming the limitations to fully unlock the capabilities of these sensors. Green manufacturing practices should also be explored to align with increasing environmental consciousness. Overall, the advances in this field open up new opportunities for efficient methane monitoring, leak prevention, and environmental protection.

Detection of Counterfeit Perfumes by Using GC-MS Technique and Electronic Nose System Combined with Chemometric Tools

The Scientific Committee on Cosmetic and Non-Food Products has identified 26 compounds that may cause contact allergy in consumers when present in concentrations above certain legal thresholds in a product. Twenty-four of these compounds are volatiles and can be analyzed by gas chromatography-mass spectrometry (GC-MS) or electronic nose (e-nose) technologies. This manuscript first describes the use of the GC-MS approach to identify the main volatile compounds present in the original perfumes and their counterfeit samples. The second part of this work focusses on the ability of an e-nose system to discriminate between the original fragrances and their counterfeits. The analyses were carried out using the headspace of the aqueous solutions. GC-MS analysis revealed the identification of 10 allergens in the perfume samples, some of which were only found in the imitated fragrances. The e-nose system achieved a fair discrimination between most of the fragrances analyzed, with the counterfeit fragrances being clearly separated from the original perfumes. It is shown that associating the e-nose system to the appropriate classifier successfully solved the classification task. With Principal Component Analysis (PCA), the three first principal components represented 98.09% of the information in the database.

Comparison of Optimisation Algorithms for Centralised Anaerobic Co-Digestion in a Real River Basin Case Study in Catalonia

Anaerobic digestion (AnD) is a process that allows the conversion of organic waste into a source of energy such as biogas, introducing sustainability and circular economy in waste treatment. AnD is an intricate process because of multiple parameters involved, and its complexity increases when the wastes are from different types of generators. In this case, a key point to achieve good performance is optimisation methods. Currently, many tools have been developed to optimise a single AnD plant. However, the study of a network of AnD plants and multiple waste generators, all in different locations, remains unexplored. This novel approach requires the use of optimisation methodologies with the capacity to deal with a highly complex combinatorial problem. This paper proposes and compares the use of three evolutionary algorithms: ant colony optimisation (ACO), genetic algorithm (GA) and particle swarm optimisation (PSO), which are especially suited for this type of application. The algorithms successfully solve the problem, using an objective function that includes terms related to quality and logistics. Their application to a real case study in Catalonia (Spain) shows their usefulness (ACO and GA to achieve maximum biogas production and PSO for safer operation conditions) for AnD facilities.

Chemoresistive Sensor Readout Circuit Design for Detecting Gases with Slow Response Time Characteristics

Based on an analysis of the signal characteristics of gas sensors, this work presents a chemoresistive sensor readout circuit design for detecting gases with slow response time characteristics. The proposed readout circuit directly generates a reference voltage corresponding to the initial value of the gas sensor and extracts only the amount of gas concentration change in the sensor. Because the proposed readout circuit can adaptively regenerate the suitable reference voltage under various changing ambient conditions, it can alleviate the variation in output values at the same gas concentration caused by non-uniformities among gas sensors. Furthermore, this readout circuit effectively eliminates the initial value shifts due to the poor reproducibility of the gas sensor itself without requiring complex digital signal calibrations. This work focuses on a commercially viable readout circuit structure that can effectively obtain slow response gas information without requiring a large capacitor. The proposed readout circuit operation was verified by simulations using spectre in cadence simulation software. It was then implemented on a printed circuit board with discrete components to confirm the effectiveness with existing gas sensor systems and its commercial viability.