This article presents the design of a miniature detection system and its associated signal processing electronics, which can detect and selectively recognize vapor traces of different explosives in the air. It is based on the array of surface-functionalized COMB capacitive sensors and extremely low noise, analog, integrated electronic circuit, hard-wired digital signal processing hardware and additional software running on a PC. The instrument is sensitive and selective, consumes a minimum amount of energy, is very small (few mm3), and is insensitive to mechanical influences. Using an electronic detection system built of low noise analog front-end and hard-wired digital signal processing, it is possible to detect less than 3 TNT molecules in 1012 molecules of the air at 25oC on a 1 Hz bandwidth using 10 mA current from a 5V supply voltage.
COBISS.SI-ID: 11045716
In this work we describe a high level Matalb/Simulink model of a sensor system for vapor trace detection of different molecules using array of differently functionalized capacitive sensors and extremely sensitive electronic detection system. The model includes very simple model of capacitance changes due to adsorption, and a high level model of low noise analog and digital signal processing electronics including most important non-ideal effects and bit-true model of the DSP algorithms. The proposed model makes possible to study the selectivity and the sensitivity of the sensor system in an efficient way and forms the basis for efficient design of different modules of the sensor system.
COBISS.SI-ID: 10964820
In this work we describe a high level Matlab-Simulink model of a sensor system for vapor trace detection of different molecules in the air using array of differently functionalized capacitive sensors and extremely sensitive integrated electronic detection system. The model includes the adsorption/desorption process of differently modified sensors. The proposed model makes possible to study the interactions, selectivity and sensitivity of the sensor system efficiently and forms the basis for the design of different modules of the sensor system, including pattern recognition algorithms.
COBISS.SI-ID: 11118164