Development of handheld SAW vapor sensors for explosives and CW agents

Nimal, A.T.; Mittal, Upendra; Singh, Mohan; Khaneja, Mamta; Kannan, Govind; Kapoor, J. C.; Dubey, Vinita; Gutch, P. K.; Lal, Gobardhan; Vyas, K. D.; Gupta, Deepa

doi:10.1016/j.snb.2008.08.040

Cited by 72 publications

(45 citation statements)

References 14 publications

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“…An ideal chemical agent simulant would mimic all relevant chemical and physical properties of the CWA without its associated toxicological properties. Few articles were reported in literature related to the detection of DMMP using SAW sensors [7][8][9][10]. Recently diamond nanoparticles dispersed on SAW devices were used for the detection of DMMP vapors [7].…”

Section: Introductionmentioning

confidence: 99%

“…However, sensor exhibits poor selectivity and was sensitive to other vapors (NH 3 , ethanol etc.). Various polymers (fluoropolyol, polyisobutylene, bisphenol) were also utilized as chemical interface layer on the surface of SAW devices for the detection of DMMP vapors [8][9][10]. The low sensitivity, cross selectivity and poor stability were major concern.…”

Section: Introductionmentioning

confidence: 99%

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Origin and role of elasticity in the enhanced DMMP detection by ZnO/SAW sensor

Raj

Singh

Nimal

et al. 2015

Sensors and Actuators B: Chemical

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Origin and role of elasticity in the enhanced DMMP detection by ZnO/SAW sensor

Raj

Singh

Nimal

et al. 2015

Sensors and Actuators B: Chemical

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“…In the past decade, a large number of analytical methods for trace explosive screening have been investigated, including gas chromatography-mass spectrometry (GC-MS) [7][8][9][10], electronic noses [11][12][13][14], ion mobility spectrometry [5,[15][16][17][18], surface acoustic wave devices [19][20][21] and fluorimetry [22][23][24][25]. Some effective approaches, such as solid-phase microextraction (SPME) [26][27][28], have been extensively explored for ultrasensitive gaseous detection of non-volatile explosives at the ppb or ppt level.…”

Section: Introductionmentioning

confidence: 99%

Nanoporous Silica-Dye Microspheres for Enhanced Colorimetric Detection of Cyclohexanone

2018

Chemosensors

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Forensic detection of non-volatile nitro explosives poses a difficult analytical challenge. A colorimetric sensor comprising of ultrasonically prepared silica-dye microspheres was developed for the sensitive gas detection of cyclohexanone, a volatile marker of explosives 1,3,5-trinitro-1,3,5-triazinane (RDX) and 1,3,5,7-tetranitro-1,3,5,7-tetrazocane (HMX). The silica-dye composites were synthesized from the hydrolysis of ultrasonically sprayed organosiloxanes under mild heating conditions (150 • C), which yielded microspherical, nanoporous structures with high surface area (~300 m 2 /g) for gas exposure. The sensor inks were deposited on cellulose paper and given sensitive colorimetric responses to trace the amount of cyclohexanone vapors even at sub-ppm levels, with a detection limit down to~150 ppb. The sensor showed high chemical specificity towards cyclohexanone against humidity and other classes of common solvents, including ethanol, acetonitrile, ether, ethyl acetate, and ammonia. Paper-based colorimetric sensors with hierarchical nanostructures could represent an alternative sensing material for practical applications in the detection of explosives.

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“…In the modern era, the solid state artificial olfactory sensing technology coupled with pattern recognition techniques has been used for many applications such as environmental monitoring [2], food safety [3], space technology [4], military applications [5] and medicine [6]. In particular, artificial olfactory sensing technology can be a useful tool in controlling the microbial outbreaks as well as estimating the quality status of food products.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous prediction of acetic acid/ethanol concentrations in their binary mixtures using metalloporphyrin based opto-electronic nose for meat safety applications

Amamcharla

Panigrahi

2010

Sens. & Instrumen. Food Qual.

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This paper presents a comprehensive study on the simultaneous prediction of volatile organic compound (VOC) concentrations in their binary mixtures (acetic acid and ethanol) using partial least square regression (PLSR) and multilayer perceptron neural network (MLP-NN). A metalloporphyrin based opto-electronic nose was developed to record the reflectance from metalloporphyrin sensing film. A ruthenium based metalloporphyrin, 2,3,7,8, 12,13,17,18-octaethyl-21H, 23H-porphine ruthenium(II) carbonyl (RuOEPCO), was used as sensing material. The percent change in the reflectance (%DR) before and after exposure to different combinations of analyte concentrations were used as the input to the prediction models. The relative standard error of prediction (RSEP, %) of the PLS model was found to be 18.51 and 21.77% for acetic acid and ethanol prediction validated using independent test set, respectively. On the other hand, neural network (multilayer perceptron) produced an average RSEP of 7.27 and 9.13% for acetic acid and ethanol prediction validated using independent test set, respectively. Neural networks produced comparatively lower prediction errors using independent test set validation method and shows potential for further investigation and validation on larger dataset.

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Development of handheld SAW vapor sensors for explosives and CW agents

Cited by 72 publications

References 14 publications

Origin and role of elasticity in the enhanced DMMP detection by ZnO/SAW sensor

Origin and role of elasticity in the enhanced DMMP detection by ZnO/SAW sensor

Nanoporous Silica-Dye Microspheres for Enhanced Colorimetric Detection of Cyclohexanone

Simultaneous prediction of acetic acid/ethanol concentrations in their binary mixtures using metalloporphyrin based opto-electronic nose for meat safety applications

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