zeolite enhanced explosive detection

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Detection of Explosive Markers Using Zeolite Modified Gas Sensors William J. Peveler, a Russell Binions, b , Stephen M.V. Hailes, c and Ivan P. Parkin *d Received Xth XXXXXXXXXX 20XX, Accepted Xth XXXXXXXXX 20XX First published on the web Xth XXXXXXXXXX 200X DOI: 10.1039/b000000x Detection of hidden explosive devices is a key priority for security and defence personnel around the globe. Electronic noses, based on metal oxide semiconductors (MOS), are a promising technology for creating inexpensive, portable and sensitive devices for such a purpose. An array of seven MOS gas sensors was fabricated by screen printing, based on WO 3 and In 2 O 3 inks. The sensors were tested against six gases, including four explosive markers: nitromethane, DMNB (2,3-dimetheyl-2,3-dinitrobutane), 2-ethylhexanol and ammonia. The gases were successfully detected with good sensitivity and selectivity from the array. Sen- sitivity was improved by overlaying or admixing the oxides with two zeolites, H-ZSM-5 and TS-1, and each showed improved responses to -NO 2 and -OH moieties respectively. Admixtures in particular showed promise, with excellent sensitivity and good stability to humidity. Machine learning techniques were applied to a subset of the data and could accurately classify the gases detected, even when confounding factors were introduced. 1 Introduction Explosives are the primary tool of terrorists in the world. In the period 2009-2011, improvised explosive devices (IEDs) were consistently responsible for over 50% of coalition deaths in Operation Enduring Freedom, Afghanistan. 1,2 Key threats include car bombs, suicide bombs and other IEDs, as well as mines and unexploded munitions from previous conflicts. Many methods for the detection of explosives exist, and bulk detection methods (x-ray, terrahertz or neutron absorp- tion) are routinely used in ports and airports. However, vapour detection is also a useful tool. It allows high-throughput, non- invasive detection of a range of chemicals, without having to subject every person or object to close individual scrutiny. It can also provide information on the types of material detected to allow rapid assessment of the threat level. There are many different methods to detect explosive vapour in current development, such as Ion Mobility Spec- trometry (IMS) and fluorescent polymers, and the “gold stan- dard” - the sniffer dog - is still favoured. 3–9 However these detectors are expensive to run and maintain, for example a ca- nine for explosives detection costs up to $6,000 to train and a Dept. of Security and Crime Science, University College London, 35 Tavis- tock Sq., London, UK, WC1H 9EZ b School of Engineering and Materials Science, Queen Mary University of London, Mile End Road, London, UK, E1 4NS c Dept. of Computer Science, University College London, Gower St., London, UK, WC1E 6BT d Dept. of Chemistry, University College London, 20 Gordon St., London, UK, WC1H 0AJ Fax: +44 (0)20 7679 7463; Tel: +44 (0)20 7679 4669; E-mail: [email protected] Electronic Supplementary Information (ESI) available: See DOI: 10.1039/b000000x/ $2,000 p.a. to maintain, in addition to the cost of a trained and dedicated handler. 10 Metal oxide semiconducting gas sensors (MOS sensors) are a low cost and increasingly reliable form of vapour detector. Their ease of production and small size gives them great po- tential for a commercial explosives detector; however, they often lack the crucial sensitivity and selectivity to detect low vapour pressure materials such as RDX (1,3,5-trinitro-1,3,5- triazacyclohexane) or TNT (2,4,6-trinitrotoluene). 11,12 In recent years a lot of attention has been paid to the use of filtering or transformational elements, overlaid on the metal oxide to improve sensitivity and selectivity. These elements filter the gas that reaches the sensing oxide by size and molec- ular structure. One set of very successful materials for this purpose have been zeolites, which act simultaneously as both filter and catalyst. 13–17 . To gain maximum selectivity from these sensors it is neces- sary to incorporate them into an electronic nose (e-nose). 18–22 Sensors are varied by material, coatings or temperature and measurements from each sensor, such as the change in resis- tance, the speed of response, or functions thereof (for example linear combinations), can be used to create a fingerprint of an analyte. Even the position in an array can add discriminatory effects. 20,23 The data collected can be processed with mul- tivariate clustering techniques, such as principal component analysis, or classifying techniques, such as neural networks and support vector machines (SVMs). 24–30 The aim of this study was to develop an array of MOS sen- sors to detect a set explosive marker gases and develop an automated classification of gases detected. This is the first step towards a prototype portable e-nose for explosives. It is 1–8 | 1

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This article discusses the use of zeolites and zeolite related nanomaterials to improve detection of explosive residues and more importantly to detect explosive vapors in real time by improving pre-existing vapor detectors by adsorption buffer technique. The zeolite or nanomaterial acts as a physical concentrator,adsorption-release buffer to present a higher and detectable concentration of explosive molecules to the detector system.

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Detection of Explosive Markers Using Zeolite Modied Gas SensorsWilliam J. Peveler,aRussell Binions,b, Stephen M.V. Hailes,cand Ivan P. ParkindReceived Xth XXXXXXXXXX 20XX, Accepted Xth XXXXXXXXX 20XXFirst published on the web Xth XXXXXXXXXX 200XDOI: 10.1039/b000000xDetection of hidden explosive devices is a key priority for security and defence personnel around the globe. Electronic noses,based on metal oxide semiconductors (MOS), are a promising technology for creating inexpensive, portable and sensitive devicesfor such a purpose. An array of seven MOS gas sensors was fabricated by screen printing, based on WO3 and In2O3 inks. Thesensors were tested against six gases, including four explosive markers: nitromethane, DMNB (2,3-dimetheyl-2,3-dinitrobutane),2-ethylhexanol and ammonia. The gases were successfully detected with good sensitivity and selectivity from the array. Sen-sitivity was improved by overlaying or admixing the oxides with two zeolites, H-ZSM-5 and TS-1, and each showed improvedresponses to -NO2 and -OH moieties respectively. Admixtures in particular showed promise, with excellent sensitivity and goodstability to humidity. Machine learning techniques were applied to a subset of the data and could accurately classify the gasesdetected, even when confounding factors were introduced.1 IntroductionExplosives are the primary tool of terrorists in the world. Intheperiod2009-2011, improvisedexplosivedevices(IEDs)were consistently responsible for over 50% of coalition deathsin Operation Enduring Freedom, Afghanistan.1,2Key threatsinclude car bombs, suicide bombs and other IEDs, as well asmines and unexploded munitions from previous conicts.Manymethodsforthedetectionofexplosivesexist, andbulk detection methods (x-ray,terrahertz or neutron absorp-tion) are routinely used in ports and airports. However, vapourdetection is also a useful tool. It allows high-throughput, non-invasive detection of a range of chemicals, without having tosubject every person or object to close individual scrutiny. Itcan also provide information on the types of material detectedto allow rapid assessment of the threat level.There are many different methods to detect explosivevapourincurrentdevelopment, suchasIonMobilitySpec-trometry (IMS) and uorescent polymers, and the gold stan-dard - the sniffer dog - is still favoured.39However thesedetectors are expensive to run and maintain, for example a ca-nine for explosives detection costs up to $6,000 to train andaDept. of Security and Crime Science, University College London, 35 Tavis-tock Sq., London, UK, WC1H 9EZbSchool of Engineering and Materials Science,Queen Mary University ofLondon, Mile End Road, London, UK, E1 4NScDept. of Computer Science, University College London, Gower St., London,UK, WC1E 6BTdDept. of Chemistry, University College London, 20 Gordon St., London, UK,WC1H 0AJ Fax:+44 (0)20 7679 7463; Tel:+44 (0)20 7679 4669; E-mail:[email protected] Electronic Supplementary Information (ESI) available: See DOI:10.1039/b000000x/$2,000 p.a. to maintain, in addition to the cost of a trained anddedicated handler.10Metal oxide semiconducting gas sensors (MOS sensors) area low cost and increasingly reliable form of vapour detector.Their ease of production and small size gives them great po-tentialforacommercialexplosivesdetector; however, theyoften lack the crucial sensitivity and selectivity to detect lowvapour pressure materials such as RDX (1,3,5-trinitro-1,3,5-triazacyclohexane) or TNT (2,4,6-trinitrotoluene).11,12In recent years a lot of attention has been paid to the use ofltering or transformational elements, overlaid on the metaloxide to improve sensitivity and selectivity. These elementslter the gas that reaches the sensing oxide by size and molec-ular structure. One set of very successful materials for thispurpose have been zeolites, which act simultaneously as bothlter and catalyst.1317.To gain maximum selectivity from these sensors it is neces-sary to incorporate them into an electronic nose (e-nose).1822Sensors are varied by material, coatings or temperature andmeasurements from each sensor, such as the change in resis-tance, the speed of response, or functions thereof (for examplelinear combinations), can be used to create a ngerprint of ananalyte. Even the position in an array can add discriminatoryeffects.20,23Thedatacollectedcanbeprocessedwithmul-tivariateclusteringtechniques, suchasprincipalcomponentanalysis, or classifying techniques, such asneural networksand support vector machines (SVMs).2430The aim of this study was to develop an array of MOS sen-sorstodetect aset explosivemarkergasesanddevelopanautomatedclassicationofgasesdetected. Thisistherststep towards a prototype portable e-nose for explosives. It is18| 1the rst time zeolitic overlayers on indium oxide sensors havebeen studied, and the rst testing of MOS sensors against sev-eral of the marker gases. The methodology is presented, fol-lowed by analysis and discussion of the sensor properties andgas testing results, as well as the machine-learning techniquesused for classication.2 Materials and Methods2.1 Material and analyte selectionTwo n-type metal oxides were used: tungsten trioxide(WO3) for its efcacy in detecting NO2and other oxidisinggases19,3134, and indium oxide (In2O3) for detection of per-oxides and other explosive gases.3539Overlayers and admix-tures were created using two types of zeolite, H-Zeolite So-cony Mobil (ZSM)-5 and Titanium Silicate (TS)-1. H-ZSM-5has been shown to improve sensitivity of WO3 sensors to NO2,so it was anticipated that it may do the same for moleculescontaining an -NO2 group.34TS-1 was used for the rst time,inanattempt toimprovesensitivitytoalcoholsandperox-ides.40,41Both zeolites have an MFI type microporous struc-ture, with Al3+centres in the H-ZSM-5 replaced by Ti4+inTS-1. The micropore size of H-ZSM-5 is around 5.5A, andTS-1 has an expanded structure due to the larger titanium ion,giving slightly larger micropores.42,43N+OO-N+OO-H3CN+OO-OH2-ethylhexanol DMNB MeNO2Fig. 1 Molecular structures of three of the six analytes used in thisstudySeven types of sensor were synthesised, and trialled againstsixgases: MeNO2is explosiveinits ownright, but hasalsobeenusedasanadditiveinammonium-nitrate/fuel-oil(ANFO)explosivesbyterrorists.44Fewstudieshaveprevi-ouslyexaminedMOSgassensingof MeNO2.45,46DMNB(2,3-dimethyl-2,3-dinitrobutane - Fig. 1) is used as a taggantinmilitaryexplosivetoassistwithdetectionandisanotherexampleofanaliphaticnitro-alkane, withamorecomplexalkylbackbone.47NO2isastrongoxidantandtoxicatmo-sphericpollutant, andwasincludedasacomparisontothealiphatic nitro-compounds.19,31,32,34The two alcohols were 2-ethylhexanol, along-chainalcohol whichoccursasaplas-ticiserinC4plasticexplosiveformulationandotherexplo-sives,10and ethanol, included as a comparison. Finally, am-moniawasincluded, asapotentialindicatorofhome-madeexplosives concocted from cleaning products.48Table 1 Table of sensors produced with abbreviated names.Bracketed numbers indicate the number of layers printed. Inindicates the metal oxide is In2O3, W indicates WO3. A +indicates an admixture of metal oxide and zeolite, and a . anoverlayer of zeolite upon metal oxide. The numbers correspond tothe number of layers deposited where applicableSensor Metal Oxide OverlayW.5 WO3(5) nilIn.5 In2O3 (5) nilW.5.TS1.2 WO3 (5) TS-1 (2)In.5.TS1.2 In2O3 (5) TS-1 (2)In.5.ZSM.2 In2O3 (5) H-ZSM-5 (2)In+TS1.5 In2O3, 30% TS-1 (5) nilIn+ZSM.5 In2O3, 30% H-ZSM-5 (5) nil10 mm 5 mm(a) (b)Fig. 2 (a) A half sheet of unprinted 3x3 mm sensing chips and aprinted 14 sensor strip. (b) Bonded WO3 sensor.2.2 Sensor fabricationSeven types of sensor were produced for the array using In2O3and WO3 powders, and H-ZSM-5 and TS-1 zeolites. All sen-sors were produced by screen printing metal oxide inks onto33 mm alumina substrate tiles, containing laser etched goldelectrodes and an integrated platinum heater track. The inkswere produced by mixing the metal oxide with an organic ve-hicle(ESL-400, Agmet. Ltd). Overlayerswerecreatedbymixing the zeolites with vehicle in a similar fashion, and ad-mixtures incorporated zeolites (30% by mass), with the metaloxide ink (Table 1). The inks were ground by pestle and mor-tar to give a smooth, homogeneous suspension. Screen print-ing was performed using a DEK1202, printing each layer ontoa strip of 14 alumina substrate tiles simultaneously (Fig. 2a).Between each application, the previous layer was dried for 10minutes under an infra-red lamp. Five layers of metal oxidewere printed for each sensor, overlayered with two zeolite lay-ers where applicable. The strip was separated into individual2 | 18sensors, and calcined in a furnace at 600C for one hour. Thisburnt off the organic vehicle and sintered the sensing elementto the substrate. Side-on scanning electron microscopy (SEM)measurements suggested a thickness of ca. 75 m.Thesensorswerebondedontobrasspinsinastandardpolyphenylenesulphidehousing(Fig. 2b); usingplatinumwire and a MacGregor DC601 parallel gap resistance welder.Metal oxides for the inks were used as supplied by SigmaAldrich. TS-1zeolitewasproducedfromasynthesisde-scribed by Uguina et al.49H-ZSM-5 zeolite was produced byring a sample of NH4-ZSM-5 at 100C for 8 hours to removemoisture, before ramping the temperature to 500C for a fur-ther 12 hours to remove ammonia.2.3 Characterisation techniquesThe sensors were subject to characterisation by X-ray Diffrac-tion (XRD), Scanning Electron Micrsocopy (SEM) and En-ergy Dispersive X-ray (EDX) spectroscopy. XRD data werecollected over the 2range 10 to 65, with step size 0.02,on a Brucker GADDS D8 diffractometer using Cu Kradia-tion (= 0.15418 nm). Powder patterns of the zeolites werecollected on a Brucker D4 powder diffractometer over the 2range 5 to 65, with step size 0.05.ScanningelectronmicrographswerecollectedonaJeolJSM-6301F microscope, in secondary electron imaging mode,usinga5keVprobevoltage. Theimages weredigitallyrecorded in SemAfore software and noise removal and resiz-ing was performed in Photoshop Elements 8.EDX analysis was performed using a 20 keV SEM probecoupledwithanOxfordInstrumentsINCAX-Sightsystemand associated software and conrmed the atomic percentagemake up of each sample.2.4 Gas testing system and protocolThe experimental setup used for testing the array is shown inFig. 3 and consists of a sensor chamber, with gas ow con-trolled by mass-ow controllers. A potential divider circuitand analog to digital converter card facilitated recording of thesensors resistance. The sensors integrated Pt heater trackswere set to 350, 400 or 500 C using separate, potentiostat-controlled, DC voltage circuitry. Dry air was used as a purgeand carrier gas, but humidity could be introduced via a hu-midier loop. Variable concentrations of test gas were intro-duced into a xed ow of 1000 cm3min1.Gases were usedat a proportion between 5 and 50% of their cylinder concentra-tion or vapour pressure, to test the lowest possible concentra-tions that could be generated by the apparatus. NO2 (1 ppm),NH3(50ppm)andEtOH(100ppm)werefromBOCsup-pliedcylinders. DMNB(3ppm), MeNO2(5000ppm)and2-ethylhexanol (296 ppm) were generated by passing air overSensor ChamberMFC1 MFC2 MFC3 MFC4SV1 SV2 SV3SV4SV5SV6To ExhaustDrechsel Bottle: WaterDrechsel Bottle: SampleDrechsel Bottle: EmptyAir Air Test Gas1 2 3 4Fig. 3 Flow diagram apparatus, with channels indicated by thecircled numbers. Channel 1 produces wet air, channel 2 dry air,channel 3 cylinder gas and channel 4 samples headspace of liquidsand solids in the Drechsel bottle. Mass-ow controllers (MFCs) andsolenoid valves (SVs) 1-4 are located on their respective channels.SVs 5 and 6 control the proportion of humidity. The empty Drechselbottle buffers air ow over the sample, ensuring a more constantconcentration.a sample of the analyte. Vapour pressures were approximatedat 25 C and 1 atm.1The sensitivity of the sensors was measured as a function oftheir base-line resistance in air. The resistance of each sensorwas measured just before the test gas was introduced to thesensor chamber (R0), and during test gas ow (R). The sensorresponse, S, was dened as S = R/R0 for oxidising gases andR0/R for reducing gases. The difference in S just before a gaspulse, denedS0, and the maximal value ofSduring a gaspulse, Smax, wascalculatedtondthemagnitudeofsensorresponse from the base-line, |S| =SmaxS0.The seven sensors in the chamber were allowed to equili-brate for 30 minutes at the selected temperature, in a ow ofdry air, to measure R0. They were then exposed to 600 s pulsesofeachtestgasatvaryingconcentrations, witha900sairpurge between each pulse. Further experiments collected dataat50%humidityandwithshorterandlongerpulselengths18| 3(150s, 300sand900s). Ifhumiditywasused, thenthebase-line was allowed to re-equilibrate for an additional 900 sbefore the next gas pulse. The response magnitude measure-ment |S| was used in construction of a Support Vector Machine(SVM) to analyse and classify the data.3 Results and Discussion3.1 Sensor material characterisationSeventypesofsensorwereproducedusingascreenprint-ing technique followed by calcination, using In2O3 and WO3powders, andH-ZSM-5andTS-1zeolites. Thesearede-scribed in Table 1, and include sensors comprised of an over-layer of zeolite on top of a metal oxide layer, and admixedsensors where the single layer contains a mix of metal oxideand zeolite.XRD patterns were collected for each sensor and powdersamples of ZSM-5 and TS-1 zeolite (Fig. 4). These patternsconrmed that WO3 and In2O3 composition were unchangedby calcination or by heating and gas testing, and that the ze-olitic materials were unchanged by incorporation into the sen-sors. The only oddity is the appearance of indium oxide peaksthrough the zeolitic overlayer in In.5.ZSM.2. This is likelydue to a slightly thinner lm at the point of sampling.SEMmicrographsaregiveninFig. 5ataround10,000xmagnication. Visual inspection showed the porous nature ofthe metal oxide or zeolite surfaces, with a variety of particlesizes evident. Pure tungsten oxide exhibits platelet-like struc-tures of circa 1 m at its surface (Fig. 5a). The pure indiumoxide sensor shows a cubic habit with a large range of grainsizes, ranging from 100 nm to 1 m (Fig. 5b). The appearanceof TS-1 zeolite is consistent in each sensor it was used on, andcharacterised by its large spherical grains of diameter 2 minterspersed with large plate-like particles (Fig. 5c + 5d). H-ZSM-5 zeolite has a substantially different structure, consist-ing of smaller, berry-like clusters, around 200 nm in diameter(Fig. 5e). On admixing with In2O3, they appear to have segre-gated to the top of the sample, with few cubic oxide particlesvisible (Fig. 5g). This difference in appearance suggests thatthe small H-ZSM-5 particles will have a larger total surfacearea, in comparison to the larger TS-1 particles, but the rangeof sizes present in the TS-1 leads to great porosity in the zeo-lite overlayer.EDX analysis shows the atom types present are as expected,withtraceimpurities- bothtypesof zeoliteshowedtracepotassium (100% change in responsebelow500C. The large difference between different tempera-tures is hypothesised to be due to the rate of diffusion of mois-turethroughtheoverlayer. Admixedindiumoxidesensorsshowed a reasonably small percentage change at each temper-ature, and tungsten oxide sensors showed almost no change inresponse to humidity at any of the temperatures investigated.How much this variation in data effects the possibility ofclassifying the data was investigated using machine learningtechniques.3.4 Training of a classication algorithmA support vector machine (SVM) was applied to a subset ofthe data. The SVM was constructed using data from four ofthesixgases, NO2, MeNO2, NH3andEtOH. Afullback-ground to SVMs and explicit details of the model constructedfor this work are available in the Electronic Supporting Infor-mation (ESI).The SVMshowed excellent classication ability whentested on the array data, with greater than 85% accuracy evenwhen confounding factors such as humidity or variable gas ex-posure times were introduced. When the SVM was tested us-ing data collected for a set gas pulse length (600s) and at 0%humidity, a classication accuracy of 85.92% was achieved.If the SVM was tested against data collected at 50% humid-ity the accuracy fell to 85.00%; however when data collectedat variable gas pulse lengths were analysed by the SVM, anaccuracy of 89.77% was achieved.The application of an SVM to the data set proved very suc-cessful. Of the four gases included, NO2 was readily recog-nised due to its oxidising response,particularly on normali-sation of the data. NH3and EtOH were the most confused,due to their more similar reducing responses. The algorithmwas capable of reasonable evaluation of gas type, even whenconcentration data was not available, and confounding factorssuch as changing humidity and variable gas pulse length wereintroduced. It should be possible to increase classication ac-curacy by the design of a more varied sensor array, using ad-ditional metal oxides and transformational elements. The in-clusion of a p-type metal oxide would be especially useful indetermining whether a detected gas was oxidising or reducing.4 ConclusionsA sensor array of seven thick-lm MOS sensors was producedusing just two basic metal oxides and two zeolites;1. It included the rst recorded use of zeolites in conjunc-tion with In2O3 for gas sensing. These sensors were par-ticularly selective towards reducing gases, although someshowed poor stability to humidity.2. The array also included the rst use of TS-1 zeolite forgas sensing purposes. This material improved sensitivityandselectivitytoalcohols, especiallyathightempera-tures, and is anticipated to also work well for the detec-tion of peroxide materials.3. Admixedmaterialscontainingametal oxideandzeo-liteshowedimprovedsensitivityandselectivityduetoa combination of open microstructure and catalytic inu-ence of the zeolites.4. H-ZSM-5 zeolite has been shown to improve sensor re-sponses to nitro-group containing materials - particularlyrelevant for the detection of explosives.5. WO3had good stability to humidity,and as previouslyreported, was very sensitive to oxidising NO2 gas.6. All theresultsweresubjectedtoclassicationwithamachine-learning tool, and high levels of accuracy werereached (>85%), even when confounding elements wereintroduced, such as humidity.18| 7Thissystemhasgreat promiseforapplicationinthede-tectionof explosivesor explosivemarkers, andshowsthepromise of e-noses based on MOS technology.5 AcknowledgmentsSteve Firth, Kevin MacDonald, Martin Vickers and Len Par-rish are thanked for their help with the instrumentation. 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