Gas Sensor with Different Morphology of PANI Layer.

This work presents the design of a polymer-film-based sensor for gas detection. Different types of polyaniline are used as active layers. The advantages of resistive sensors with PANI layers are easy preparation and low production cost. At room temperature, polymer films have a high sensitivity to gas concentrations. The developed sensor works on the idea of electrical resistance shifting with gas concentration. Three different polymerization solutions are employed to synthesize the polyaniline (PANI) active layers (aqueous solution, sulfuric acid solution, and acetic acid solution). Active layers are evaluated in a controlled environment for their ability to detect ammonia, carbon monoxide, nitrogen monoxide, acetone, toluene, and relative humidity in synthetic air. PANI layers polymerized in acetic acid solutions exhibit good sensitivity toward ammonia.

Hydrogen-Terminated Diamond Surface as a Gas Sensor: A Comparative Study of Its Sensitivities.

A nanocrystalline diamond (NCD) layer is used as an active (sensing) part of a conductivity gas sensor. The properties of the sensor with an NCD with H-termination (response and time characteristic of resistance change) are measured by the same equipment with a similar setup and compared with commercial sensors, a conductivity sensor with a metal oxide (MOX) active material (resistance change), and an infrared pyroelectric sensor (output voltage change) in this study. The deposited layer structure is characterized and analyzed by Scanning Electron Microscopy (SEM) and Raman spectroscopy. Electrical properties (resistance change for conductivity sensors and output voltage change for the IR pyroelectric sensor) are examined for two types of gases, oxidizing (NO2) and reducing (NH3). The parameters of the tested sensors are compared and critically evaluated. Subsequently, differences in the gas sensing principles of these conductivity sensors, namely H-terminated NCD and SnO2, are described.

Improved Gas Sensing Capabilities of MoS2/Diamond Heterostructures at Room Temperature.

Molybdenum disulfide (MoS2) and nanocrystalline diamond (NCD) have attracted considerable attention due to their unique electronic structure and extraordinary physical and chemical properties in many applications, including sensor devices in gas sensing applications. Combining MoS2 and H-terminated NCD (H-NCD) in a heterostructure design can improve the sensing performance due to their mutual advantages. In this study, the synthesis of MoS2 and H-NCD thin films using appropriate physical/chemical deposition methods and their analysis in terms of gas sensing properties in their individual and combined forms are demonstrated. The sensitivity and time domain characteristics of the sensors were investigated for three gases: oxidizing NO2, reducing NH3, and neutral synthetic air. It was observed that the MoS2/H-NCD heterostructure-based gas sensor exhibits improved sensitivity to oxidizing NO2 (0.157%·ppm-1) and reducing NH3 (0.188%·ppm-1) gases compared to pure active materials (pure MoS2 achieves responses of 0.018%·ppm-1 for NO2 and -0.0072%·ppm-1 for NH3, respectively, and almost no response for pure H-NCD at room temperature). Different gas interaction model pathways were developed to describe the current flow mechanism through the sensing area with/without the heterostructure. The gas interaction model independently considers the influence of each material (chemisorption for MoS2 and surface doping mechanism for H-NCD) as well as the current flow mechanism through the formed P-N heterojunction.

A chemiresistive sensor array based on polyaniline nanocomposites and machine learning classification.

The selective detection of ammonia (NH3), nitrogen dioxide (NO2), carbon oxides (CO2 and CO), acetone ((CH3)2CO), and toluene (C6H5CH3) is investigated by means of a gas sensor array based on polyaniline nanocomposites. The array composed by seven different conductive sensors with composite sensing layers are measured and analyzed using machine learning. Statistical tools, such as principal component analysis and linear discriminant analysis, are used as dimensionality reduction methods. Five different classification methods, namely k-nearest neighbors algorithm, support vector machine, random forest, decision tree classifier, and Gaussian process classification (GPC) are compared to evaluate the accuracy of target gas determination. We found the Gaussian process classification model trained on features extracted from the data by principal component analysis to be a highly accurate method reach to 99% of the classification of six different gases.