Low-Power, Multi-Transduction Nanosensor Array for Accurate Sensing of Flammable and Toxic Gases

Gas Graph Convolutional Transformer for Robust Generalization in Adaptive Gas Mixture Concentration Estimation

Gas concentration estimation has a tremendous research significance in various fields. However, existing methods for estimating the concentration of mixed gases generally depend on specific data-preprocessing methods and suffer from poor generalizability to diverse types of gases. This paper proposes a graph neural network-based gas graph convolutional transformer model (GGCT) incorporating the information propagation properties and the physical characteristics of temporal sensor data. GGCT accurately predicts mixed gas concentrations and enhances its generalizability by analyzing the concentration tokens. The experimental results highlight the GGCT's robust performance, achieving exceptional levels of accuracy across most tested gas components, underscoring its strong potential for practical applications in mixed gas analysis.

Chemresistive Detection of NO2 of ppb Level in Humid Air at 350 K Using Azo-Spaced Polycroconamide

Organic molecules are of great interest for gas sensing applications. However, achieving high-performance gas sensors with high sensitivity, fast response, low consumption, and workability in humid conditions is still challenging. Herein, we report the rational design and synthesis of an ion-in-conjugation polymer, PADC (poly-4,4'-azodianiline-croconamide), obtained by the condensation of croconic acid with 4-4'diaminoazobenzene for gas sensing under humid conditions. The as-fabricated PADC-based gas sensor exhibits ultrahigh sensitivity (802.7 ppm-1 at 1 ppm), subppb detection limit, and high selectivity under humid air with an 80% humidity effect at a temperature down to 350 K. PADC shows good planarity, excellent thermostability, and a narrow band gap of 1.2 eV because of azobenzene fragments spacing previously repulsed biphenyl rings. Compared to previous humidity immunity works, PADC-based sensors realized humidity immunity at a relatively lower temperature, resulting in lower energy consumption.

Pattern Recognition with Temperature Regulation: A Single YSZ-Based Mixed Potential Sensor Classifies Multiple Mixtures of Isoprene, n-Propanol, and Acetone

Gas sensors integrated with machine learning algorithms have aroused keen interest in pattern recognition, which ameliorates the drawback of poor selectivity on a sensor. Among various kinds of gas sensors, the yttria-stabilized zirconia (YSZ)-based mixed potential-type sensor possesses advantages of low cost, simple structure, high sensitivity, and superior stability. However, as the number of sensors increases, the increased power consumption and more complicated integration technology may impede their extensive application. Herein, we focus on the development of a single YSZ-based mixed potential sensor from sensing material to machine learning for effective detection and discrimination of unary, binary, and ternary gas mixtures. The sensor that is sensitive to isoprene, n-propanol, and acetone is manufactured with the MgSb2O6 sensing electrode prepared by a simple sol-gel method. Unique response patterns for specific gas mixtures could be generated with temperature regulation. We chose seven algorithm models to be separately trained for discrimination. In order to realize more accurate discrimination, we further discuss the selection of suitable feature parameters and its reasons. With temperature regulation coefficients which are easily available as feature input to model, a single sensor is verified to achieve elevated accuracy rates of 95 and 99% for the discrimination of seven gases (three unary gases, three binary gas mixtures, and one ternary gas mixture) and redefined six gas mixtures. This article provides a potential new approach via a mixed potential sensor instead of a sensor array that could provide a wide application prospect in the field of electronic nose and artificial olfaction.

A chemiresistive-potentiometric multivariate sensor for discriminative gas detection

Highly efficient gas sensors able to detect and identify hazardous gases are crucial for numerous applications. Array of conventional single-output sensors is currently limited by problems including drift, large size, and high cost. Here, we report a sensor with multiple chemiresistive and potentiometric outputs for discriminative gas detection. Such sensor is applicable to a wide range of semiconducting electrodes and solid electrolytes, which allows to tailor and optimize the sensing pattern by tuning the material combination and conditions. The sensor performance is boosted by equipping a mixed-conducting perovskite electrode with reverse potentiometric polarity. A conceptual sensor with dual sensitive electrodes achieves superior three-dimensional (sub)ppm sensing and discrimination of humidity and seven hazardous gases (2-Ethylhexanol, ethanol, acetone, toluene, ammonia, carbon monoxide, and nitrogen dioxide), and enables accurate and early warning of fire hazards. Our findings offer possibilities to design simple, compact, inexpensive, and highly efficient multivariate gas sensors.