Special Feature

Odor-sensor Technology Based on an Array of Quartz Crystal
Resonators Coated with Plasma-deposited Organic Film
Michiko Seyama†, Masayuki Nakamura, and Akiyuki Tate
Abstract
Odor information is new network content that has the potential to enable highly realistic telecommunication and novel services. Our goal is to develop odor sensing technology that translates odors into
information that can be shared with others. This article reviews our odor sensing system, which is composed of an array of quartz crystal resonators coated with plasma-deposited organic film and uses a dataprocessing algorithm based on chemometrics.

1. Introduction
Odor includes useful information for various situations in our lives and is expected to be a new type ofcontent for network services (Fig. 1). For example,
odor can add realistic environmental information tosound-and-vision-based telecommunication. Odor
information can appeal to a consumer and also helps
consumers evaluate merchandise when shopping
online. Biogas odor such as breath or body odor
includes useful indicators of a person’s health [1].
Active utilization of odor in the air that we breathe
unconsciously is attracting increasing interest
because of a recent scientific report on human psychological and physiological effects of odor in aromatherapy [2]. And offensive- or nuisance-odor
assessment has become an important task for administrative organs of the Japanese government [3]. It isdifficult for humans to objectively assess nuisanceodor because the recognition of bad smell depends on
personal experience, cultural background, and one’s
personal circumstances. For example, food odors
coming from a restaurant may attract guests but irritate neighbors.
We have developed an odor-sensor that is an input
device of odor information to network. Odor is one ormore volatile molecules having a molecular weight ofabout 20 to 400 in air. The sensor must catch odorous† NTT Microsystem Integration Laboratories
Atsugi-shi, 243-0198 Japan
E-mail: michiko@aecl.ntt.co.jp

70

volatile components that are easily diffused in air anddiluted and must also generate information that we
can share with others. Therefore, a highly sensitive
sensor, a compactly assembled sensor system that is
applicable to monitoring in homes, factories, or in thefield, and an easy and clear presentation of odor information are all required. Our odor sensor system consists of a compact sensor array of quartz crystal resonators (QCRs) coated with plasma-deposited organic adsorption films (PPFs) and a data processing program that was developed based on chemometrics*1.
In this article, we introduce the concept of our odor
sensor system and then review the technical details of
its components.
2. Concept and overview of the odor sensor system
The odor sensor is analogous in concept to thehuman olfactory system, which is said to be able todiscriminate several hundred thousand odorous compounds [4]. Biological research on the human olfactory system indicates that odor molecule discrimination originates from the molecular selectivity ofolfactory receptor protein (ORP) that exists deep inthe nose and only about 350 kinds of DNA determine*1 Chemometrics: The coined term that means the fusion of “chemistry” and “metrics”. A chemical discipline that uses mathematicaland statistical methods to treat chemical data in order to provide
maximum relevant chemical information (e.g., a predicting
model) by analyzing chemical data with techniques of optimization, pattern recognition, library search, calibration, and neuralnetworks.

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Special Feature

the molecular structure of ORPs [5]. When an odor
compound binds selectively with ORP, a G protein
stimulates an enzyme and a stimuli-transmitting substance called cyclic adenosine monophosphate(cAMP) opens an ion channel through the lipid membrane so that a neuro-signal is generated, as shown in

Fig. 2. Similarly, when an odor sensor system catches an odorous compound, it generates a signal representing the odor’s molecular information.
Our odor sensor system expresses a detected odor
in a way analogous to how human olfactory systemdoes. Odor has no unit like wavelength (for light) or

Realistic communication
Home

NatureRealistic
communication

Healthcontrol

Aromatherapy
Odor sensor

Security
Odor sensor

BreathPark

Odor sensor

Company
Odor sensor
Advertisement

Hospital

Security
company

Odor sensor

Administrative
organ

WINE
Odor sensor
Field

Body odor

Yakitori*

Odor sensor
Nuisance-odor assessment*Barbecued chicken
Field

Environmental monitoring

Fig. 1. Expected services with odor sensor technology.

ORP
Odor molecule

G protein
EnzymeHuman olfactory
systemOlfactoryepithelium
GlomeruluscAMP

Judgment
Olfactory bulb

Brain
LimbicsystemGranule cellIon
channel

Neuro-signal
Lipid membrane

Odor sensor

Sensor device/sensor array
Film A

Sensor signal
Sensor A

Personal computer
Parameters
Aa,Ab,•••

Odor molecules
Film B
Odor

Chemical space
Discrimination map
Judgment

Sensor B
Ba,Bb,•••

Film C

Sensor C
Ca,Cb,•••

Pattern matching(Chemometrics)

Odor molecules
Transducer (quartz crystal resonator)
The film preferentially adsorbs
odor molecules having highaffinity with the film.

Fig. 2. Concept of odor sensor.

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Special Feature

Odor molecules
PPF
QCR

Personal computer

Sensor module

Serial interface

Fig. 3. Odor sensor system.

frequency (for sound). In our daily lives, we describe
odors by referring to a typical familiar odor. For
example, one might say that something smells likefish. The sensor system identifies odors in a similar
way: sensor devices with different molecular selectivities are assembled in an array. When the sensor
detects an odorous compound, it generates an electrical signal that carries information about the odor’smolecular structure. Next, the characteristics of sensor signals are extracted, combined, and stored as a
response pattern for a particular odor. By matching
the generated response pattern with one of the many
response patterns stored in the database, the systemjudges that the detected odor is a particular odor. Our
sensor device consists of a PPF as an adsorption layer
and a mass-sensitive transducer, QCR [6]. The compact sensor module is shown in Fig. 3. The circuitboard contains an oscillation circuit for each QCR, afrequency measurement circuit, and a data transfer
interface. Eight QCRs are set in the module alongwith one bare QCR used as a standard device for frequency measurement. The resonant frequency difference between the QCR and the standard device ismeasured using a multiplexer. The resonant frequency change data are fed into a personal computer
through a serial interface. Multiple sensor modules
can be connected together and used at the same timewith one personal computer.
3. Details of odor sensor system based on PPF
3.1 PPF-coated QCR sensor
PPFs are deposited on both sides of the QCR byradio-frequency sputtering. The total PPF thickness
is around 1 µm. As a sputtering target, we have used
many organic solids: amino acids (e.g., D-phenylala72nine), nucleotide (e.g., adenine), biological gels (e.g.,
agarose), and synthetic olefin polymer (e.g., polyethylene and poly(ethylene-co-vinyl acetate)) and synthetic fluoropolymer (e.g., poly(chlorotrifluoroethylene)) [7]. The sputtered organic molecules are excited in the plasma through reactions with electrons,
light, and ions. These excited molecules form new
chemical bonds, and polymerized film is depositedon the QCR substrate. The molecular structure of the
PPF is related to that of the starting material andchanges in plasma conditions affect the molecular
structure of the deposited film. For example, exciting
the sputtered molecules by illuminating them withultraviolet light produces a PPF with a higher molecular density. Therefore, PPFs with various kinds ofmolecular structures can be prepared.
The adsorption of odor molecules by the PPFs is
explained as a solvation phenomenon in which asmall ‘solute’ of odor molecule is adsorbed/absorbed
in the ‘solvent’ of the PPF film matrix [8]. The concept of numerical quantitative analysis of solute/solvent interaction, which is called the linear solvationenergy relationship (LSER), is applied to predict themolecular selectivity of PPFs. The solvation phenomenon is treated as the total interaction of characteristic detailed interactions, represented by polarizability (R or r), dipole-dipole interaction (s or π),hydrogen-bonding ability that is divided into acidity(a or α) and basicity (b or β), and lipophilicity (logL,l ), as follows.
Solubility = R • r + s • π + a • β + b • α + logL • l (1)Each interaction is expressed by multiple ‘solute’parameters (italics) and ‘solvent’ parameters. (ThelogL is the same value as the Ostwald coefficient
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Special Featureroom temperature and ∆f data were obtained every 5s. When odor molecules were adsorbed by the PPF,
the resonance frequency of the QCR decreased asindicated in Eq. (3). Samples were petroleum vapors
having characteristic odors that are early indicators ofoil spills in water sources [11].
The resonant frequency of the PPF-coated QCR incontact with air was stable as shown by the first 30
min in Fig. 4. When the petroleum vapor was applied
to the sensor at 30 min (up-arrow in Fig. 4), the resonant frequency shifted. The QCR sensor used in thistest was covered with a PPF prepared from polyethylene, which is lipophilic and has a high affinity forhydrocarbons. The rate of increase in ∆f was different
for different sample gases. Since gasoline and fuel oil
are mixtures of various hydrocarbons, their concentrations were estimated from the corresponding concentration of octane, which was confirmed by gas
chromatography. The QCR sensor can detect subppm to low-ppb levels of petroleum vapors. After
sorption of odor molecules by the QCRs, we applied
air to the sensor at 210 min (downward arrow) andthe ∆f began to decrease immediately, indicating thatthe adsorbed molecules were desorbing from the PPF.
Therefore, a PPF-coated QCR can be used for odor
monitoring repeatedly.
Some chiral gas molecules smell differently tohumans. Many natural and artificial flavors include
volatile chiral molecules. For example, chirallimonenes are often found in citrus flavors. (–)limonene, which rotates the optical plane counterclockwise, smells like oil, whereas (+)-limonene,
which rotates the optical plane clockwise, smells like
fresh lemon. Since volatile chiral components are relevant to flavors or aroma quality, a sensor device thatcan discriminate them would contribute to thedetermined by molecular solubility in n-hexadecane.)
Solubility is related to the partition coefficient ofsolute in a solvent, so it is directly related to theincrease in the mass of the PPF-coated QCR by theadsorption of molecules. The solubility parameters ofvarious volatile molecules have already been determined [9]. Each volatile molecule has different values of solubility parameters. Therefore, the selectivity of PPF for target odorous molecules can be adjusted by designing the molecular structure of the PPF so
as to increase or decrease the value of the solubility
parameter so that it matches the solubility parametersof target odors.
3.2 Sensor response characteristics
The raw sensor data is the resonance frequencychange (∆f) of the QCR that indicates the increase inmass (∆m) [10]. The relationship between ∆f and ∆mis expressed as∆f = –2 fo2 ∆m / A (ρqµq)1/2,(2)

where fo is the fundamental resonance frequency, A isthe electrode area, ρq is the density of quartz (2.65 gcm–3), and µq is the shear modulus of quartz (2.95 ×
106 N cm–2). We use an 8-mmφ QCR whose fo and A
are 9 MHz and 0.13 cm2, respectively. Therefore, oursensor device is theoretically sensitive to subnanogram mass changes by measuring the resonant
frequency with an accuracy of 0.1 Hz.
∆f [Hz] = –1.05 ∆m [ng](3)

Figure 4 shows examples of sensor responses
obtained by using a PPF prepared from polyethylene[7]. Odor measurement was performed at around

Gasoline(corr. to 3-ppm n-octane)

Frequency shift l∆fl (Hz)

200
160

Diesel fuel(corr. to 0.1-ppm n-octane)120

1,2,4-trimethylbenzene(0.5 ppm)

8040

n-hexadecane(3 ppb)

0
0

30

60

90

120

150

180

210

240

Time (min)Fig. 4. Odor sensor response to volatile compounds.

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Special Feature∆f = a [1–exp (–t/τ)],(4)

where a, the resonant frequency change at time t, corresponds to the mass of adsorbed molecules and τ isa time constant.
By fitting the time versus ∆f curve to Eq. (4), we
can extract the values of a and τ as characteristic
parameters for the sensor response. These response
parameters are obtained from the signals from the
QCRs in the module and are combined to make theresponse pattern of the monitored odor.
In this way, the sensor array can generate a lot ofdata as response patterns. To extract useful information for odor discrimination, we perform a principal
component analysis (PCA), which is a powerful tool
for reducing the dimensionality of a data set. We
applied our sensor array to measure various monogases [14] and also odors composed of various
volatile molecules (Fig. 5). Samples were six kinds ofwoody odors and seven kinds of citrus odors generated from essential oils, which are extracted from natural plants and used for fragrances and cosmetics andin aromatherapy. The concentrations of the tested
woody and citrus odors that include various kinds ofvolatile molecules were expressed by odor strength asdetermined by the human olfactory system. A ‘sample point’ that corresponded to one particular concentrated odor among tested odors included two parameters that were extracted by fitting the measured time
versus ∆f curve to Eq. (4). PCA of a data set consisting of ‘sample points’ found the first and second principal components (PC1, PC2) that represented theaxes to make the ‘sample points’ the most and thesecond-most dispersive, respectively. By plotting theloadings of each ‘sample point’ relative to PC1 andPC2, we obtained two-dimensional PCA score plots
as shown in Fig. 5.
74

3
2
Rosewood
Cedar wood
Star anise
Sandal wood
Pine
Eucalyptus

1
PC2

3.3 Response parameters and discrimination
The rate and amount of adsorption are related to thetotal molecular interaction between ‘solute’ odorous
molecules and the ‘solvent’ PPF matrix, as mentioned in section 3.1. The time versus ∆f curve isexpressed using the model Langmuir presented,
where the diffusion of small molecules in the solvent
PPF matrix is the rate-determining step [13]:Odors that people recognize as a similar groupoften include common molecules [15]. The PCA
score in Fig. 5(a) was obtained from QCRs coatedwith three different PPFs. Visually, plots for the sameodor make a group segregated from other groups. The
plots for two of woody odors, both of which contained a lot of α-pinene, eucalyptus, and pine, areclose together. However, the groups of star anise, sandal wood, and cedar wood, which contained their own
representative odorous components other than αpinene (anethol, santol, and cederene, respectively)
are relatively separated from each other. These results
indicate that useful data for odor discrimination wereextracted successfully by PCA. The discrimination
accuracy of plots in the PCA score was evaluatedmathematically by using the k-nearest neighbor(kNN) method, which classifies a plot to the nearest
group based on the Euclid distance between plots.
The discrimination in Fig. 5(a) was good, consideringthat 91% accuracy was obtained.
The PCA scores in Fig. 5(b), which were preparedfrom data obtained from four different PPF sensors,
indicated the odor discrimination ability of our sensor
array of citrus odors. These odors include common
volatile compounds represented by limonene and

0
–1
–2
–3
–3

–2

–1

0
PC1
(a)

1

2

3

3
2
1
PC2

advancement of high-precision odor sensing technology. We have developed a PPF prepared from chiralamino acid [12]. The QCR coated with this PPF candiscriminate chiral odorous compounds generated at
sub-ppm concentrations.

–3

–2

–1 0
–1

1

2

3

Grapefruit
Lime
Lemon
Bergamot
OrangeMandarin
Petit grain

–2
–3
PC1
(b)

Fig. 5. PCA scores obtained from measurements ofvarious odors with an array of QCRs coated with
PPFs. a) woody odors and b) citrus odors.

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Special Feature

have different characteristic odorous compounds.
The mandarin and orange, which have similar odors
to humans, were not discriminated well, but the otherodors were discriminated. The discrimination accuracy evaluated by the kNN method was 74%.

representing an ‘incorrect’ odor, the weight vector isdecreased and shifted from the input signal according
to the rule:

3.4 Odor judgment
The odor sensor system can judge a gas sample as
belonging to an odor category in the database. The
program is based on the learning vector quantization(LVQ) method [16] presented by Kohonen, which isa neural network system. The input signal to the neural network originates from the response patternobtained from the sensor array, and the output units(cells) are arranged in an array (map) and connectedby weights to the inputs. The weights are tuned withthe observed data beforehand. The input signal of theodor to be identified (unknown sample) is comparedto each output cell’s weight vector, and the output
vector with the weight nearest to the observed input
signal is selected as the ‘winner’.
The input signal is a standardized pattern vector (X)whose original data are sets of normalized a and τ
obtained from i kinds of PPF-coated QCR sensors.
The dimension of X is 2i. The sensor response parameter of a is normalized because the value of a (i.e.,the mass adsorbed by the PPF) is affected by odor
concentrations and normalization among data from ikinds of PPF-coated QCRs can eliminate the concentration effect. First, we create the feature map (e.g.,
Fig. 6) and the starting weight, which is generated
from one observed input signal based onwhere Wc is the nearest weight vector of an output
cell.
An example of odor judging based on LVQ isshown in Fig. 6 [16]. Each cell in the map represents
one of six odors. The numbers shown in the cells in
the field indicate the sum of ‘winning’ times andclearly tell us which odor the sensor system has
judged it to be. By making an odor map that covers
the range of target odors (including one or more molecules) and training the neural network through tests
using real samples, it will be possible to make an odor
system optimized for a given application [17].

Wj(T+1) = Wj(T) + .ε(T)[X–Wj(T)],(5)

where Wj is the jth weight vector and ε(T) = 0.01
(1–T/1000). T starts at 1 and increases to 1000. Next,
we tune the weight vector using the observed data. If
the nearest weight vector belongs to a cell representing the ‘correct’ odor, then the weight vector isincreased and shifted to the input signal. On the other
hand, if the nearest weight vector belongs to a cellWc(T+1) = Wc(T) ± ε(T)[X–Wc(T)],4. Conclusion
We have developed an odor-sensor system based on
plasma-processed organic adsorption film (PPF)coated quartz crystal resonators (QCRs) that has highsensitivity to various odors consisting of one or more
odorous molecules. Our sensor array can discriminate between various odors found in nature and fragrances and also between chiral odorous compounds.
A visualized odor discrimination system based on thelearning vector quantization method has been developed that is suitable for a particular application by
optimizing the PPF-coated QCRs in the sensor arrayand the learning process with the sensor response
database. The sensor array was assembled in a compact module, so it can be used in the factory, office,home, and field. We believe that further miniaturization of the whole odor sensor system will make odor
monitoring possible on a more personal level andextend the utilization of odor information.
References
[1]

[2]
15
3 10 6 51
1
1

AcetoneButanone
Methanol
Ethanol
TolueneBenzene[3][4]

Fig. 6. Odor discrimination based on LVQ.

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C. M. F. Kneepkens, G. Lepage, and C. C. Roy, “The potential of thehydrocarbon breath tests as a measure of lipid peroxidation,” Free
Radical Biology and Medicine, Vol. 17, pp. 127-160, 1994.
H. Akutsu, T. Kikusui, Y. Takeuchi, K. Sano, A. Hatanaka, and Y.
Mori, “Alleviating effects of plant-derived fragrances on stressinduced hyperthermia in rats,” Physiology & Behavior, Vol. 75, pp.
355-360, 2002.
K. Uegahara, “Recent administrative programs for offensive odor
(Syukitaisakugyousei-no-saikin-no-torikumi),” SANGYO TO
KANKYO, Vol. 368, pp. 71-73, 2003 (in Japanese).
W. Göpel, “Chemical imaging: I. Concepts and visions for electronic
and bioelectronic noses,” Sens. Actuators B, Vol. 52, pp. 125-142,
1998.

75

Special Feature[5]

B. Malnic, J. Hirono, and T. Sato, “Combinatorial receptor codes forodors,” Cell, Vol. 96, pp. 713-723, 1999.
I. Sugimoto, M. Seyama, and M. Nakamura, “Detection of petroleumhydrocarbons at low-ppb levels using quartz crystal resonator sensors
and instrumentation of a smart environmental monitoring system,”
Journal of Environmental Monitoring, Vol. 1, pp. 135-142, 1999.
[7] I. Sugimoto, M. Nakamura, S. Ogawa, M. Seyama, and T. Katoh,“Petroleum pollution sensing at ppb level using quartz crystal resonators sputtered with porous polyethylene under photo-excitation,”
Sens. Actuators B, Vol. 64, pp. 216-233, 2000.
[8] J. W. Grate, S. J. Patrash, and M. H. Abraham, “Method for estimating polymer-coated acoustic wave vapor sensor responses,” Anal.
Chem., Vol. 67, pp. 2162-2169, 1995.
[9] M. H. Abraham, J. Andonian-Haftvan, G. S. Whitig, A. Lep, and R.
S. Taft, “Hydrogen bonding. Part 34. The factors that influence thesolubility of gases and vapors in water at 298 K, and a new method forits determination,” J. Chem. Soc. Perkin Trans., Vol. 2, pp. 17771791, 1994.
[10] G. Z. Sauerbrey, “Verwendung von Schwingquarzen zur Wägung
dünner Schichten und zur Mikrowägung,” Z. Phys. Vol. 155, pp. 206222, 1959.
[11] S. Ogawa and I. Sugimoto, “Monitoring system for detecting odorous
materials in water using quartz crystal microbalance sensors,” Tech.
Dig. Sens. Symp., Vol. 18th, pp. 185-188, 2001.
[12] I. Sugimoto, M. Seyama, and M. Nakamura, “Chiral discriminative
amino acids films prepared by vacuum vaporization and/or plasma
processing,” Analyst, Vol. 125, pp. 169-174, 2000.
[13] M. Nakamura, I. Sugimoto, H. Kuwano, and R. Lemos, “Chemicalsensing by analyzing dynamics of plasma polymer film-coated sensors,” Sens. Actuators B, Vol. 20, pp. 231-237, 1994.
[14] M. Seyama, I. Sugimoto, and T. Miyagi, “Application of an array sensor based on plasma-deposited organic film coated quartz crystal resonators to monitoring indoor volatile gases,” IEEE Sensors Journal,
Vol. 2, pp. 422-427, 2002.
[15] M. Seyama, I. Sugimoto, and T. Miyagi, “ Detection and discrimination of odors generated from essential oils with an array of quartz
crystal resonators coated with plasma-deposited organic film,” Analytical Sciences, Vol. 17, pp. i257-260, 2001.
[16] M. Nakamura, I. Sugimoto, and H. Kuwano, “Pattern recognition of
dynamic chemical sensor responses by using LVQ algorithm,” Proc.
of the 1997 IEEE Int. Conf. System. Man. and Cybernetics (SMC’97), IEEE, Orlando, FL, U.S.A., Vol. 4, pp. 3036-3041, Oct. 1997.
[17] M. Nakamura, I. Sugimoto, and H. Kuwano, “Fire detection using
quartz crystal microbalance chemical sensors,” Trans. ElectricalEngineers of Japan, Vol. 118-E, pp. 42-47, 1998.
[6]

76

Michiko Seyama
Engineer, Ubiquitous Interface Research
Department, NTT Microsystem Integration Laboratories.
She received the B.S. and M.S. in appliedchemistry from Waseda University, Tokyo in
1995 and 1997. In 1997, she joined the Integrated Information and Energy Systems Laboratories, Tokyo, Japan. She joined the Lifestyle andEnvironmental Laboratories in 1999 and theMicrosystem Integration Laboratories in 2002.
She has researched optical chemical sensing andodor sensing systems. She is a member of theElectrochemical Society of Japan, the Japan
Society for Environmental Chemistry (JEC), andthe Society of Polymer Science, Japan. She
received a research promotion award and a paper
award in 1999 and 2001, respectively, from JEC.

Masayuki Nakamura
Senior Research Engineer, Environmental
Information Systems Project, NTT Energy andEnvironment Systems Laboratories.
He received the B.E., M.E., and Ph.D. degrees
in mathematical engineering and information
physics from the University of Tokyo, Tokyo in
1988, 1990, and 1998. He joined NTT Applied
Electronics Laboratories in 1990, NTT Interdisciplinary Research Laboratories in 1991, NTT
Integrated Information and Energy Systems Laboratories in 1996, NTT Lifestyle and Environmental Technology in 1999, and NTT Energy
and Environment Systems Laboratories in 2002.
He has studied information processing in sensing
systems. He is a member of the Society of Instrument and Control Engineers and the Institute ofElectrical Engineers of Japan (IEEJ). He
received a presentation award from IEEJ in 1998
and a research promotion award from JEC.

Akiyuki Tate
Senior Research Engineer, Supervisor, NTT
Microsystem Integration Laboratories.
He received the B.S. and M.S. degrees inapplied physics from Hokkaido University,
Hokkaido in 1976 and 1979, respectively. In
1979, he joined NTT Laboratories, where he has
been in charge of the development of various
types of optical functional materials, devices,and equipment in areas ranging from organics,
semiconductors, silica glass, and opto-magnetic
materials to ferroelectric materials for future
photonic communications and information processing. He is a member of the Japan Society ofApplied Physics, the Physical Society of Japan,
Optical Society of Japan, and the Institute ofElectronics, Information and Communication
Engineers of Japan.

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