The design of functionalized silicone polymers for chemical sensor detection of nitroaromatic compounds

McGill, R. Andrew; Mlsna, Todd; Chung, R.; Nguyen, Viet; Stepnowski, Jennifer L.

doi:10.1016/s0925-4005(99)00351-2

Cited by 65 publications

(41 citation statements)

References 2 publications

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“…The second type of interesting materials with high adsorption efficiencies are the organic materials, such as polymers. Silicones have been used successfully for nitroaromatic detection in the case of explosive application [25]. Such organic adsorbing materials have been deposited within microchannels for preconcentration [26][27][28].…”

Section: Discussionmentioning

confidence: 99%

Application of carbon nano-powders for a gas micro-preconcentrator

Pijolat

Camara

Courbat

et al. 2007

Sensors and Actuators B: Chemical

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This paper presents a feasibility study on the development of a gas preconcentrator based on micro-reactor technology on silicon. The objectives are to select a gas adsorbent material, to produce a silicon micro-reactor with an integrated heater, and finally to introduce the most suitable adsorbent into the micro-channels of the device. Preliminary results related to the characterization of a carbon adsorbent for the development of a device for the preconcentration of benzene are reported. Carbon nano-powders have been tested as adsorbent material by the determination of the breakthrough time on a dedicated test bench consisting of gas sensors and a non-selective photoionization detector (micro-PID) analyzer. A fluidic deposition process allows filling up the silicon micro-channels with the carbon nano-powder. The interest in using porous silicon to enhance the binding of the carbon nano-particles in the micro-channels was also investigated. A silicon micromachined preconcentrator filled with 0.30 mg of commercial activated charcoal powder (Aldrich, 30-100 nm) was designed and built up. The total capacity of adsorption was determined by using the breakthrough time, which is of 2.2 min under a gas flow of 100 ppm of benzene at 1 l/h. Preliminary tests of preconcentration with 100 and 1.3 ppm benzene in dry air were performed.

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Section: Discussionmentioning

confidence: 99%

Application of carbon nano-powders for a gas micro-preconcentrator

Pijolat

Camara

Courbat

et al. 2007

Sensors and Actuators B: Chemical

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“…The sensor array response matrix is obtained by adding the noise and outlier components to the signal generated by Eqn (2). 38 , and using the available solvation parameters in published literature 28,29,39,40 . The calculated partition coefficient for different vapourpolymer pairs is shown in Table 3.…”

Section: Addition Of Noise Sources and Outliermentioning

confidence: 99%

“…The goal of a pattern recognition system is to build a set of mathematical descriptors (each being some combination of characteristic solvation Table 3. Equilibrium partition coefficients (log K) calculated using LSER model and the solvation parameters 28,29,39,40 descriptors) from the multisensor data such that they represent the vapour identity in unique manner. It can be noted that the set of characteristics sensitivities a ij itself would be unique to different molecules.…”

Section: Pattern Recognition Systemmentioning

confidence: 99%

“…Some SAW sensor array-based electronic nose products have been launched and many more have been reported from research and development laboratories [23][24][25][26] . Having a fairly mature SAW device technology, the critical bottleneck in the performance of SAW sensor array-based electronic nose systems comes from selection of chemical selective polymer coatings and signal processing for vapour pattern recognition [27][28][29][30][31][32] . Most of the electronic components, electromechanical parts and data acquisition systems involved in the construction of an electronic nose system are commercially available.…”

Section: Introductionmentioning

confidence: 99%

“…However, selection of proper polymers requires experimenting with a large set of potentially useful polymers, either by making sensor arrays in various combinations and evaluating these for target vapour discrimination [27][28][29][30][31][32] or by alternate thermochemical characterisation such as thermogravimetric analysis, Fourier transform spectrometry, and quartz crystal microbalance-based sorption-desorption analysis 33,34 . This is quite an expensive and time-consuming process.…”

Section: Introductionmentioning

confidence: 99%

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Development of Surface Acoustic Wave Electronic Nose

Jha

Yadava

2010

DSJ

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The paper proposes an effective method to design and develop surface acoustic wave (SAW) sensor array-based electronic nose systems for specific target applications. The paper suggests that before undertaking full hardware development empirically through hit and trial for sensor selection, it is prudent to develop accurate sensor array simulator for generating synthetic data and optimising sensor array design and pattern recognition system. The latter aspects are most time-consuming and cost-intensive parts in the development of an electronic nose system. This is because most of the electronic sensor platforms, circuit components, and electromechanical parts are available commercially-off-the-shelve (COTS), whereas knowledge about specific polymers and data analysis software are often guarded due to commercial or strategic interests. In this study, an 11-element SAW sensor array is modelled to detect and identify trinitrotoluene (TNT) and dinitrotoluene (DNT) explosive vapours in the presence of toluene, benzene, dimethylmethylphosphonate (DMMP) and humidity as interferents. Additive noise sources and outliers were included in the model for data generation. The pattern recognition system consists of: (i) a preprocessor based on logarithmic data scaling, dimensional autoscaling, and singular value decomposition-based denoising, (ii) principal component analysis (PCA)-based feature extractor, and (iii) an artificial neural network (ANN) classifier. The efficacy of this approach is illustrated by presenting detailed PCA analysis and classification results under varied conditions of noise and outlier, and by analysing comparative performance of four classifiers (neural network, k-nearest neighbour, naïve Bayes, and support vector machine).

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Detection of Nitrobenzene, DNT, and TNT Vapors by Quenching of Porous Silicon Photoluminescence

2000

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The detection of nitroaromatic molecules in air by the quenching of the photoluminescence of porous silicon (porous Si) films has been explored. Detection is achieved by monitoring the photoluminescence (PL) of a nanocrystalline porous Si film on exposure to the analyte of interest in a flowing air stream. The photoluminescence is quenched on exposure to the nitroaromatic, presumably by an electron-transfer mechanism. Detection limits of 500 parts-per-billion (ppb), 2 ppb, and 1 ppb were observed for nitrobenzene, 2.4-dinitrotoluene (DNT), and 2,4,6-trinitrotoluene (TNT), respectively (exposure times of 5 min for each, in air). Specificity for detection is achieved by catalytic oxidation of the nitroaromatic compound. A platinum oxide (PtO2) or palladium oxide (PdO) catalyst at 250 degrees C. placed in the carrier gas line upstream of the porous Si detector, causes oxidation of all the nitroaromatic compounds studied. The catalyst does not oxidize benzene vapor, and control experiments show no difference in the extent of PL quenching by benzene with or without an upstream catalyst. The PL quenching by NO2, released in the catalytic oxidation of nitroaromatic compounds, is less efficient than the quenching of the intact nitroaromatic compound. This provides a means to discriminate nitro-containing molecules from other organic species.

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The design of functionalized silicone polymers for chemical sensor detection of nitroaromatic compounds

Cited by 65 publications

References 2 publications

Application of carbon nano-powders for a gas micro-preconcentrator

Application of carbon nano-powders for a gas micro-preconcentrator

Development of Surface Acoustic Wave Electronic Nose

Detection of Nitrobenzene, DNT, and TNT Vapors by Quenching of Porous Silicon Photoluminescence

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