E-Nose: Time-Frequency Attention Convolutional Neural Network for Gas Classification and Concentration Prediction.

In the electronic nose (E-nose) systems, gas type recognition and accurate concentration prediction are some of the most challenging issues. This study introduced an innovative pattern recognition method of time-frequency attention convolutional neural network (TFA-CNN). A time-frequency attention block was designed in the network, aiming to excavate and effectively integrate the temporal and frequency domain information in the E-nose signals to enhance the performance of gas classification and concentration prediction tasks. Additionally, a novel data augmentation strategy was developed, manipulating the feature channels and time dimensions to reduce the interference of sensor drift and redundant information, thereby enhancing the model's robustness and adaptability. Utilizing two types of metal-oxide-semiconductor gas sensors, this research conducted qualitative and quantitative analysis on five target gases. The evaluation results showed that the classification accuracy could reach 100%, and the coefficient of the determination (R2) score of the regression task was up to 0.99. The Pearson correlation coefficient (r) was 0.99, and the mean absolute error (MAE) was 1.54 ppm. The experimental test results were almost consistent with the system predictions, and the MAE was 1.39 ppm. This study provides a method of network learning that combines time-frequency domain information, exhibiting high performance in gas classification and concentration prediction within the E-nose system.

Acidic Gas Determination Using Indium Tin Oxide-Based Gas Sensors.

This work has presented gas sensors based on indium tin oxide (ITO) for the detection of SO2 and NO2. The ITO gas-sensing material was deposited by radio frequency (RF) magnetron sputtering. The properties of gas sensing could be improved by increasing the ratio of SnO2. The response characteristics of the gas sensor for detecting different concentrations of NO2 and SO2 were investigated. In the detection of NO2, the sensitivity was significantly improved by increasing the SnO2 ratio in ITO by 5%, and the response and recovery time were reduced significantly. However, the sensitivity of the sensor decreased with increasing SO2 concentration. From X-ray photoelectron spectroscopy (XPS) analysis, the gas-sensitive response mechanisms were different in the atmosphere of NO2 and SO2. The NO2 was adsorbed by ITO via physisorption but the SO2 had a chemical reaction with the ITO surface. The gas selectivity, temperature dependence, and environmental humidity of ITO-based gas sensors were systematically analyzed. The high detection sensitivity for acidic gas of the prepared sensor presented great potential for acid rain monitoring.