Good chemical selectivity of sensors for detecting vapour traces of targeted molecules is vital to reliable detection systems for explosives and other harmful materials. We present the design, construction and measurements of the electronic response of a 16 channel electronic nose based on 16 differential microcapacitors, which were surface-functionalized by different silanes. The e-nose detects less than 1 molecule of TNT out of 10+12 N2 molecules in a carrier gas in 1 s. Differently silanized sensors give different responses to different molecules. Electronic responses are presented for TNT, RDX, DNT, H2S, HCN, FeS, NH3, propane, methanol, acetone, ethanol, methane, toluene and water. We consider the number density of these molecules and find that silane surfaces show extreme affinity for attracting molecules of TNT, DNT and RDX. The probability to bind these molecules and form a surface-adsorbate is typically 10+7 times larger than the probability to bind water molecules, for example. We present a matrix of responses of differently functionalized microcapacitors and we propose that chemical selectivity of multichannel e-nose could be enhanced by using artificial intelligence deep learning methods.
COBISS.SI-ID: 11909716
We used a 16-channel e-nose demonstrator based on micro-capacitive sensors with functionalized surfaces to measure the response of 30 different sensors to the vapours from 11 different substances, including the explosives 1,3,5-trinitro-1,3,5-triazinane (RDX), 1-methyl-2,4-dinitrobenzene (DNT) and 2-methyl-1,3,5-trinitrobenzene (TNT). A classification model was developed using the Random Forest machine-learning algorithm and trained the models on a set of signals, where the concentration and flow of a selected single vapour were varied independently. It is demonstrated that our classification models are successful in recognizing the signal pattern of different sets of substances. An excellent accuracy of 96% was achieved for identifying the explosives from among the other substances. These experiments clearly demonstrate that the silane monolayers used in our sensors as receptor layers are particularly well suited to selecting and recognizing TNT and similar types of explosives from among other substances.
COBISS.SI-ID: 32908327
In this article, we report on the selectivity measurements of vapor trace detection system, which operates on the array of differently modified comb capacitive sensors. We present measured responses of 16 differently functionalized comb capacitive sensors caused by 14 different gas compositions that are composed of the carrier gas with different target molecules and different solvents. The responses of 16 differently functionalized sensors in the array were measured in real time. The measurements show very good selectivity properties.
COBISS.SI-ID: 11697236
In this article we present a method to improve the selectivity of the vapor trace detection system based on the array of differently modified comb capacitive sensors. Selectivity measurements on our multi-channel demonstrator (detection system) show that certain level of the selectivity is assured with different characteristics of different modification layers. However, the attenuation of the response to disturbing molecules (for example water) is in the range of 120dB compared to the TNT molecules. Observing big number of responses in real time is very difficult for human operator. Therefore, we decided to build automated recognition software system based on machine learning.
COBISS.SI-ID: 12008788