Electrochemical amperometric gas sensors represent a well-established and versatile type of devices with unique features: good sensitivity and stability, short response/recovery times, and low power consumption. These sensors operate at room temperature, and therefore have been applied in monitoring air pollutants and detection of toxic and hazardous gases in a number of areas. Some drawbacks of classical electrochemical sensors are overcome by the solid polymer electrolyte (SPE) based on ionic liquids. This work presents evaluation of an SPE-based amperometric sensor from the point of view of current fluctuations. The sensor is based on a novel three-electrode sensor platform with solid polymer electrolytes containing ionic liquid for detection of nitrogen dioxide − a highly toxic gas that is harmful to the environment and presenting a possible threat to human health even at low concentrations. The paper focuses on using noise measurement (electric current fluctuation measurement) for evaluation of electrochemical sensors which were constructed by different fabrication processes: (i) lift-off and drop-casting technology, (ii) screen printing technology on a ceramic substrate and (iii) screen printing on a flexible substrate.

In recent years, smog and poor air quality have become a growing environmental problem. There is a need to continuously monitor the quality of the air. The lack of selectivity is one of the most important problems limiting the use of gas sensors for this purpose. In this study, the selectivity of six amperometric gas sensors is investigated. First, the sensors were calibrated in order to find a correlation between the concentration level and sensor output. Afterwards, the responses of each sensor to single or multicomponent gas mixtures with concentrations from 50 ppb to 1 ppm were measured. The sensors were studied under controlled conditions, a constant gas flow rate of 100 mL/min and 50 % relative humidity. Single Gas Sensor Response Interpretation, Multiple Linear Regression, and Artificial Neural Network algorithms were used to predict the concentrations of SO₂ and NO₂. The main goal was to study different interactions between sensors and gases in multicomponent gas mixtures and show that it is insufficient to calibrate sensors in only a single gas.