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. Thiswork was carried out under EPSRC Grant no:
EP/G037264/1as part of UCLs Security Science Doctoral Training
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