amirhosein asilian

This study addresses the imperative for advanced gas sensors, particularly for monitoring ‎hazardous carbon monoxide (CO), by enhancing nanocrystalline SnO2 thin film fabrication. ‎Control of parameters in the sol-gel synthesis process is systematically explored to mitigate crack ‎formation and enhance SnO2 film quality on uncoated Al2O3 substrates. Leveraging SnO2's n-‎type semiconductor properties, traditional thin film methods are employed, with a specific focus ‎on overcoming drawbacks through glycerin. Among various fabrication techniques, sol-gel proves ‎cost-effective for producing high-quality, crack-free SnO2 layers tailored for gas sensor ‎applications. The study evaluates sensitivity to CO gas concentrations, improving structural ‎integrity, sensitivity, and stability. X-ray powder diffraction and SEM imaging confirm phase ‎purity and surface morphology, ensuring the absence of impurities or cracks. Integrated with ‎microcontroller-based circuits, the sensors exhibit rapid response and recovery times crucial for ‎real-time gas sensing. The addition of an output circuit with enhanced resolution and stability ‎further enhances sensor performance. Results demonstrate the proposed sensor's capability for ‎rapid response (less than 30 seconds) and recovery times (~39 seconds), crucial for real-time gas ‎sensing. Notably, the sensors demonstrate an admirable sensitivity with a minimum detection ‎limit of as low as 1 ppm of CO gas. Additionally, the study validates the sensor's stability and ‎reliability during prolonged exposure to N2 and 1% CO mixtures, highlighting its potential for ‎personal safety detectors and environmental safety monitoring.‎

The unique properties of carbon monoxide and its high combustibility have led to the creation of various ‎sensors, such as electrochemical sensors and different circuits, to read its output. In this article, a deflection-type ‎Wheatstone bridge is used to measure changes in the sensor resistance, and the output voltage is connected to a 12-‎bit analog-to-digital converter through an adjustable precision amplifier. Next, a new method is proposed for self-calibrating the CO sensor. The Levenberg-Marquardt backpropagation algorithm (LMBP) is utilized in the Artificial ‎Neural Network model to minimize the Mean Squared Error (MSE) and identify the most suitable parameters in the ‎proposed method.‎‏ ‏The model under consideration has been developed and trained using real-time data.‎‏ ‏Based on ‎the experimental and evaluation outcomes, it can be concluded that the suggested model has an MSE value of ‎‎0.28249 and an R2 coefficient of determination of 0.99992, indicating high accuracy and precision. The proposed ‎sensor and calibration method have potential applications in various applications, including industrial and domestic ‎environments where CO monitoring is necessary.‎