The excellent conductivity of Ti₃C₂T_x and the rich charge carriers of ZnO under UV illumination can endow the gas sensors with greatly enhanced performances in response/recovery speed, sensitivity and cycle stability toward NO₂ at room temperature.

Real-time and accurate biomarker detection is highly desired in point-of-care diagnosis, food freshness monitoring, and hazardous leakage warning. However, achieving such an objective with existing technologies is still challenging. Herein, we demonstrate a wireless inductor-capacitor (LC) chemical sensor based on platinum-doped partially deprotonated-polypyrrole (Pt-PPy⁺ and PPy⁰) for real-time and accurate ammonia (NH₃) detection. With the chemically wide-range tunability of PPy in conductivity to modulate the impedance, the LC sensor exhibits an up-to-180% improvement in return loss (S11). The Pt-PPy⁺ and PPy⁰ shows the p-type semiconductor nature with greatly-manifested adsorption-charge transfer dynamics toward NH₃, leading to an unprecedented NH₃ sensing range. The S11 and frequency of the Pt-PPy⁺ and PPy⁰-based sensor exhibit discriminative response behaviors to humidity and NH₃, enabling the without-external-calibration compensation and accurate NH₃ detection. A portable system combining the proposed wireless chemical sensor and a handheld instrument is validated, which aids in rationalizing strategies for individuals toward various scenarios.

Ag-doped TaS₂ monolayers show an obvious change in work function and short recovery time for NO₂, which indicates that TaS₂-based nanomaterials might be a promising candidate for constructing high-performance NO₂ gas sensors.

This work presents an electronic nose-based gas identification and concentration estimation method for detection of food spoilage. The response data of sensors were acquired through a commercial gas sensor array and data acquisition circuit board, and transformed into pictures with the form of the Fourier series. A convolutional neural network model was used to identify the pictures from the conversion of sensor data, thus achieving the purpose of identifying the gases (C ₂ H ₅ OH, NH ₃ and H ₂ S). In order to solve the problem of sample imbalance and to improve the generalization performance of classification models, the synthetic minority oversampling technique and dropout technique were employed. Five-fold cross-validation was used to evaluate the performance of the model, of which the gas identification accuracy rate reached 96.67%. Moreover, a gas concentration regression model with the advantages of simplicity and strong interpretability was further proposed to estimate the concentrations of C ₂ H ₅ OH, NH ₃ and H ₂ S. The mean absolute errors and coefficients of determination for the concentration estimation of C ₂ H ₅ OH, NH ₃ and H ₂ S are (3.71 ppm, 0.968), (0.50 ppm, 0.968) and (0.13 ppm, 0.99), respectively. Furthermore, we used our model to evaluate the freshness of kiwifruit, pork and beef, and it showed satisfactory predictive performance. The method proposed in this work realizes high-precision detection of gases from food spoilage, and has a good application prospect in the rapid judgment of food freshness on the electronic nose system.

An electronic nose (E-nose) based on three metal oxide semiconductor (MOS) gas sensors is designed to quantitatively analyse six types of volatile organic compounds (VOCs). Support vector machine (SVM), extreme learning machine (ELM), and back-propagation (BP) neural network, are used to design different classifiers and regressors for integrating a suitable pattern recognition model of the E-nose system. By using the output of the classifier as one of the input features of the regressor, the models can predict the concentration of different types of VOCs at the same time. The 5-fold cross-validation is applied to search optimal parameters of each model and the independent test is conducted to evaluate the generalization performance. A pipeline is used to connect the best classifier and the best regressor, constructing an integrated model for pattern recognition. The integrated model based on ELM-ELM structure exhibits the best performance. Classification accuracy can be as high as 99% and the R ² score of regression as high as 0.97 in the 5-fold cross-validation. Classification accuracy is up to 93% and the R ² score of regression is up to 0.94 in the independent tests. Furthermore, the integrated model has prominent training and test efficiency, of which time consumptions are both shorter than 0.1 s. This work provides an approach to design an efficient integrated model with high performance for gas type identification and concentration prediction in the E-nose system.

An efficient formaldehyde (HCHO) gas sensor system based on nickel-doped tin oxide (Ni-doped SnO ₂ ) nanoparticle sensor and back-end circuit has been developed. Ni-doped SnO ₂ nanoparticles are prepared by a simple one-step hydrothermal method. To complete the function of detecting HCHO gas in real time, a back-end circuit suitable for resistance measurement of nanoparticle sensors and an organic light emitting diode screen which can display the output of gas concentration data are integrated as a portable device. Compared with the traditional HCHO detection device, this designed system exhibits better stability, excellent selectivity, and high response. Its detection limit is as low as 90 ppb. The gas response to 50 ppm HCHO is 67.63. Further for the quantitative evaluation, we refer to and modify the existing HCHO diffusion model, then carry out a practical test on the HCHO diffusion at the same time. The comparison between the theoretical fitting curve and the actual test curve matched well enough to reflect the potential of this system for the practical HCHO evaluation.