We describe a method for generating a variety of chemically diverse, broadly responsive, low power vapor sensors. A key to our ability to fabricate chemically diverse sensing elements is the preparation of processable, air stable films of electrically conducting organic polymers. An array of such sensing elements produces a chemically reversible, diagnostic pattern of electrical resistance changes upon exposure to different odorants. Such conducting polymer elements are simply prepared and are readily modified chemically to respond to a broad range of analytes. In addition, these sensors yield a fairly rapid, low power, de electrical signal in response to the vapor of interest, and their signals are readily integrated with software or hardware-based neural networks for purposes of analyte identification. Principle component analysis has demonstrated that such sensors can identify and quantify different airborne organic solvents, and can yield information on the components of gas mixtures

Briglin, Shawn M.

Freund, Michael S.

Lewis, Nathan S.

Sisk, Brian C.

Caltech Authors - Main

51.2: Vapor Detection, Classification, and Quantification 
Performance Using Arrays of Conducting Polymer Composite 
Chemically Sensitive Resistors 
Shawn M. Briglin, Michael S. Freund, Brian C. Sisk, Nathan S. Lewis", 
'Division of Chemistry and Chemical Engineering, 
Noyes Laboratory, 127-72 
California Institute of Technology, Pasadena, CA 91125 
ABSTRACT 
We describe a method for generating a variety 
of chemically diverse, broadly responsive, low 
power vapor sensors. A key to our ability to 
fabricate chemically diverse sensing elements is 
the preparation of processable, air stable films 
of electrically conducting organic polym~rs. An 
array of such sensing elements produces a 
chemically reversible, diagnostic pattern of 
electrical resistance changes upon exposure to 
different odorants. Such conducting polymer 
elements are simply prepared and are readily 
modified chemically to respond to a broad range 
of analytes. In addition, these sensors yield a 
fairly rapid, low power, de electrical signal in 
response to the vapor of interest, and their 
signals are readily integrated with software or 
hardware-based neural networks for purposes of 
analyte identification. Principle component 
analysis has demonstrated that such sensors can 
identify and quantify different airborne organic 
solvents, and can yield information on the 
components of gas mixtures. 
Keywords: electronic nose, gas sensor, 
conducting polymer 
1. INTRODUCTION 
Carbon black-polymer composites can 
be used for array-based vapor sensing 
applications. 1 In such an array, no individual 
detector responds solely to a specific molecule, 
but the collective response of the entire array of 
0-7803-7454-1102/$17.00 ©2002 IEEE 727 
detectors yields a unique fingerprint for the 
vapor of interest. Such arrays are often referred 
to as "electronic noses" and are not designed in 
advance to perform a specific task, but are 
instead developed to identify and quantify 
vapors based on pattern recognition algorithms. 
2
·
7 This approach to vapor sensing takes 
advantage of the collective output of an array of 
broadly responsive detectors. In the polymer 
composite array configuration, the signal 
transduction is extremely simple: swelling of 
the polymeric phase of the composite, in the 
presence of a vapor, leads to an increase in the 
electrical resistance of the composite, which is 
monitored using simple electronics. The pattern 
of responses produced by an array of chemically 
different carbon black-polymer composites 
identifies the odorant, and the pattern height is 
correlated with the odorant concentration. The 
resistance change of a detector is reversible, is 
linear over at least an order of magnitude of 
odorant concentration, and is quite 
reproducible. 1 
In this work, a statistical metric was 
used to quantify the resolving power of detector 
arrays, and to evaluate how array resolving 
power improves as the number of detectors 
increases. It has been hypothesized that a fairly 
small number of detectors is sufficient to span 
odor space (a multi-dimensional space, 
containing all odorants, where every possible 
orthogonal chemical difference between any 
two odorants is represented by a separate 
dimension). 8 Small numbers of carefully 
chosen detectors are thought to be optimal 
because it is hypothesized that additional 
detectors add noise, but not classifying ability, 
to the data produced by a well-designed detector 
array. In contrast, others have hypothesized that 
1t 1s desirable to have as many detectors as 
possible in an array. 9•10 Current research 
suggests that in mammalian olfaction there are 
approximately 103 different receptor genes and 
approximately 107 total receptor cells. 11 Thus, 
it is not clear whether functional models of the 
mammalian olfactory system can be 
satisfactorily constructed with a small detector 
basis set or whether such models will require 
thousands, or even millions, of different 
detector compositions. A quantitative measure 
(i.e. a metric) of the resolving power of a 
detector array as a function of the number of 
detectors in the system can allow evaluation of 
some of these questions in a meaningful 
fashion. 
2. RESUI.TS AND DISCUSSION 
To quantify the ability of an array to 
discriminate between various odorants based on 
the different response patterns that each odorant 
produces, a series of odorants was presented to 
the detector array, and the responses were 
evaluated using conventional chemometric 
methods. In these experiments, although the 
resistance of each detector was sampled once 
every 3 seconds during each exposure, only the 
maximum relative differential resistance 
change, !'>.Rij, maxi Rb where !'>.Rij, miff is the 
maximum resistance change of the / detector 
during the ;th exposure and Rb is the baseline 
resistance of the detector prior to the exposure, 
was used in analysis of the data. 
To analyze the data in a consistent 
fashion for different detectors and vapors, the 
responses for each individual detector were 
autoscaled 12 so that all detectors, regardless of 
. their general response magnitudes, had the same 
operating range in the analysis of the data. The 
autoscaled response of the Jth detector to the jth 
exposure, Aij, was obtained from 
A;· \t'.R;j.max I 11;,)- j • (l) 
~ 13 . 
. 1 
The tenns Uj and f3J represent the mean and 
standard deviation, respectively, of the 
maximum relative differential resistance 
response of the lh detector to the entire group 
of analytes. 
The autoscaled patterns that resulted 
from exposure of fourteen carbon black-
728 
polymer composite chemiresistor detectors to 
acetonitrile, benzene or chloroform are depicted 
in Figure 1. Although these patterns are 
obviously different even to the untrained human 
eye, the goal of this work was to assess in a 
quantitative fashion the differences between 
such patterns. 
Neural networks were not used to 
analyze these differences because although the 
performance of neural networks in pattern 
classification can be superior to that of 
statistically based chemometric methods, the 
use of a neural network intimately couples the 
performance of a specific training/learning 
algorithm to the detector response data. A 
statistical measure of the differences between 
two clusters of patterns can be obtained by 
determining the distance between the centroids 
of the data arising from repeated exposures to a 
given vapor and dividing this distance by the 
sum of the standard deviations of the two 
different pattern clusters projected along the 
vector that counects these centroid points 
(Figure 2). It can be shown in a straightforward 
manner that this metric is independent of the 
coordinate system chosen to display the data 
and is instead an inherent, statistical property of 
the data set. 
From standard vector analysis, the mean 
response vector, xa, of an n-detector array to 
analyte a is given as the n-dimensional vector 
containing the Qll:an autoscaled response of 
each detector, Aa1, to the ath analyte as 
components such that, 
X a ~ (Aal, Aab ... , Aan) ~ (2) 
The average separation, I~ , between two 
analytes, a and b, in the Euclidean detector 
response space is then egual to the magnitude of 
the difference between Xa and xb . The noise of 
the detector responses is also important in 
quantifying the resolving power of the detector 
array. Thus the standard deviations, cr a J and 
cr b.d, obtained from all the individuaf jl!Tay 
responses to each of a and b along vector d, are 
used to describe the average separation and 
ultimately to define the pairwise resolution 
factor as 
I~ (3) rj~ fa 2 _ + 2 l a,d b,d 
Assuming a Gaussian distribution relative to the 
mean value of the data points that are obtained 
from the responses of the array to any given 
analyte, the probabilities of correctly identifying 
an analyte as a or b from a single presentation 
when a and b are separated with resolution 
factors of 1.0, 2.0 or 3.0 are approximately 
76%, 92% and 98% respectively. This 
resolution factor is equivalent to that proposed 
by Miill~13 and recently used by Gardner and 
Bartlett and is basically a multi-dimensional 
analogue to the separation factors used to 
quantify the resolving power of a column in gas 
chromatography. 
Table 1 presents the resolution factors 
obtained from an array of the fourteen carbon 
black-polymer composite detectors for all 171 
pairs of the nineteen vapors. In general, the full 
array of carbon black-polymer detectors can 
easily resolve the odorant pairs presented at a 
fixed concentration. 
ACKNOWLEDGMENTS 
This work was supported by the U.S. 
Army Research Office contracts DAAH04-96-
l-0048 Modification 1 and DAAG55-97-1-
0187, DARPA, and NASA. 
729 
REFERENCES 
(I) Lonergan, M. C.; Severin, E. J.; 
Doleman, B. J.; Beaber, S. A.; Grubbs, 
R. H.; Lewis, N. S. Chem. Mater. 1996, 
8, 2298. 
(2) Zaromb, S.; Stetter, J. R. Sens. Actuators 
1984, 6, 225. 
(3) Lundstrom, 1.; Erlandsson, R.; Frykman, 
U.; Hedborg, E.; Spetz, A.; Sundgren, 
H.; Welin, S.; Winquist, Nature 1991, 
352,47. 
(4) Shurrner, H. V.; Gardner, J. W. Sens. 
Actuators B 1992, 8, l. 
(5) Gardner, J. W.; Bartlett, P. N. Sens. 
Actuators B 1994, 18, 211. 
(6) Gardner, J. W.; Hines, E. L.; Tang, H. C. 
Sens. Actuators B 1992, 9, 9. 
(7) Nakamoto, T.; Fukuda, A.; Moriizumi, 
T. Sens. Actuators B 1993, 10, 85. 
(8) Grate, J. W.; Abraham, M. H. Sens. 
Actuators B 1991, 3, 85. 
(9) Di Natale, C.; D'Amico, A.; Davide, F. 
A.M. Sens. Actuators A 1993, 37-38, 612. 
(10) Dickinson, T. A.; Walt, D. R.; White, J.; 
Kauer, J. S. Anal. Chem. 1997, 69, 3413. 
(11) Axel, R. Sci. Am. 1995, 154. 
(12) Hecht, H. G. Mathematics in Chemistry: 
An Introduction to Modern Methods; 
Prentice Hall: Englewood Cliffs, NJ, 
1990. 
(13) Muller, R. Sens. Actuators B 1991, 4, 35. 
(14) Gardner, J. W.; Bartlett, P. N. In 
Eurosensors IX; 1995; pp 169. 
4 
D acetonitrile 
D benzene 
"' 
3 D chloroform ... 
"' 
= 
= c. 
"' 2 ... 
.. 
'C 
... 
.... 
1 ... 
"' 
= .... 
= 
"' 0 
-1 
1 2 3 4 5 6 7 8 9 10 11 12 13 14 
sensor 
Figure 1. Autoscaled responses, Aij, of the fourteen detectors in the carbon black-polymer composite 
array to approximately 6.7 ppth of benzene, chloroform and acetonitrile. Each bar represents the 
average autoscaled response over twelve exposures and the error bars represent the standard deviation in 
the responses. 
• 
. ·- . 
• X • 
• a 
·__/ 
aa,d 
• 
' . 
• 
.X • •• b • 
. . -./ 
x = mean response 
cr projected st. dev . 
I~ 
rf=--=::::::!::::== fa2 - + 2 1 a,d b,d 
Figure 2. Mathematical definition of the resolution factor, rf, in multi-dimensional space. 
730 
Table 1. Resolution factors quantifying the ability of the fourteen element carbon black-polymer 
composite detector array to resolve pairwise each of the nineteen vapors, at fixed concentration, from 
any other vapor in the test set. The average and worst pairwise resolution factors are 9.7 and 0.91, 
respectively. 
0. ~ 0 ;;· 
'0 5' . 5'. 0" 0 §= s. .§ :::0 " " ~ " 0 '< ~ "$. e- 0 
-$. "" " 0 "$. 3 & .% ~ ., s ~- 0" 0 0 s " ~ " c; "$. 0 ~ §. ~ "" " e- "' e- .g " ~-" E!. 0 " e- 0 g 8" c; 2: [ >< ., " 0 § § " 0 " ., ., " ~ "" " " 
" " " 
" " " 
0 g. g. e- ii 
" " " 1 2-dimethoxy ethane 14 11 16 19 6.5 17 24 16 13 11 21 18 22 23 17 16 22 22 
acetone 7.0 18 10 7.1 2.7 14 3.6 5.2 2.2 3.3 5.5 14 12 2.0 17 11 21 
acetonitrile 11 8.7 9.3 8.4 11 7.1 12 7.5 8.6 13 10 II 8.6 19 10 II 
anisole 9.0 5.6 II 14 16 7.6 II 15 9.3 13 15 19 7.6 12 5.2 
benzene 7.1 1.8 21 15 5.8 3.8 2.8 5.6 19 16 2.2 12 4.9 12 
butyl amine 6.8 9X 7.5 7.1 6.5 7.3 8.5 8.4 9.3 7.1 6.4 7.9 6.9 
chloroform 4.9 2.9 6.0 5.1 2.0 6.3 4.2 4.8 9.3 14 4.0 4.4 
cyclohexane 21 7.5 6.5 5.2 6.2 20 0.91 3.8 14 4.5 19 
dichloromethane 5.6 2.9 3.3 5.4 20 19 2.9 16 10 23 
ethanol 6.2 5.6 3.5 7.1 7.5 6.2 12 6.1 6.4 
ethyl acetate 4.2 7.1 5.6 6.0 4.4 25 6.1 7.2 
isopropyl alcohol 5.6 4.8 4.4 1.6 13 7.5 8.4 
methanol 6.1 6.1 6.4 13 5.5 6.6 
n-heptane II 3.5 12 3.8 13 
n-pentane 3.7 14 4.4 18 
thf 17 3.2 6.5 
toluene II 7.0 
triethyl amine 10 
731 


Vapor Detection, Classification, and Quantification Performance Using Arrays of Conducting Polymer Composite Chemically Sensitive Resistors

51.2: Vapor Detection, Classification, and Quantification 
Performance Using Arrays of Conducting Polymer Composite 
Chemically Sensitive Resistors 
Shawn M. Briglin, Michael S. Freund, Brian C. Sisk, Nathan S. Lewis", 
'Division of Chemistry and Chemical Engineering, 
Noyes Laboratory, 127-72 
California Institute of Technology, Pasadena, CA 91125 
ABSTRACT 
We describe a method for generating a variety 
of chemically diverse, broadly responsive, low 
power vapor sensors. A key to our ability to 
fabricate chemically diverse sensing elements is 
the preparation of processable, air stable films 
of electrically conducting organic polym~rs. An 
array of such sensing elements produces a 
chemically reversible, diagnostic pattern of 
electrical resistance changes upon exposure to 
different odorants. Such conducting polymer 
elements are simply prepared and are readily 
modified chemically to respond to a broad range 
of analytes. In addition, these sensors yield a 
fairly rapid, low power, de electrical signal in 
response to the vapor of interest, and their 
signals are readily integrated with software or 
hardware-based neural networks for purposes of 
analyte identification. Principle component 
analysis has demonstrated that such sensors can 
identify and quantify different airborne organic 
solvents, and can yield information on the 
components of gas mixtures. 
Keywords: electronic nose, gas sensor, 
conducting polymer 
1. INTRODUCTION 
Carbon black-polymer composites can 
be used for array-based vapor sensing 
applications. 1 In such an array, no individual 
detector responds solely to a specific molecule, 
but the collective response of the entire array of 
0-7803-7454-1102/$17.00 ©2002 IEEE 727 
detectors yields a unique fingerprint for the 
vapor of interest. Such arrays are often referred 
to as "electronic noses" and are not designed in 
advance to perform a specific task, but are 
instead developed to identify and quantify 
vapors based on pattern recognition algorithms. 
2·7 This approach to vapor sensing takes 
advantage of the collective output of an array of 
broadly responsive detectors. In the polymer 
composite array configuration, the signal 
transduction is extremely simple: swelling of 
the polymeric phase of the composite, in the 
presence of a vapor, leads to an increase in the 
electrical resistance of the composite, which is 
monitored using simple electronics. The pattern 
of responses produced by an array of chemically 
different carbon black-polymer composites 
identifies the odorant, and the pattern height is 
correlated with the odorant concentration. The 
resistance change of a detector is reversible, is 
linear over at least an order of magnitude of 
odorant concentration, and is quite 
reproducible. 1 
In this work, a statistical metric was 
used to quantify the resolving power of detector 
arrays, and to evaluate how array resolving 
power improves as the number of detectors 
increases. It has been hypothesized that a fairly 
small number of detectors is sufficient to span 
odor space (a multi-dimensional space, 
containing all odorants, where every possible 
orthogonal chemical difference between any 
two odorants is represented by a separate 
dimension). 8 Small numbers of carefully 
chosen detectors are thought to be optimal 
because it is hypothesized that additional 
detectors add noise, but not classifying ability, 
to the data produced by a well-designed detector 
array. In contrast, others have hypothesized that 
1t 1s desirable to have as many detectors as 
possible in an array. 9•10 Current research 
suggests that in mammalian olfaction there are 
approximately 103 different receptor genes and 
approximately 107 total receptor cells. 11 Thus, 
it is not clear whether functional models of the 
mammalian olfactory system can be 
satisfactorily constructed with a small detector 
basis set or whether such models will require 
thousands, or even millions, of different 
detector compositions. A quantitative measure 
(i.e. a metric) of the resolving power of a 
detector array as a function of the number of 
detectors in the system can allow evaluation of 
some of these questions in a meaningful 
fashion. 
2. RESUI.TS AND DISCUSSION 
To quantify the ability of an array to 
discriminate between various odorants based on 
the different response patterns that each odorant 
produces, a series of odorants was presented to 
the detector array, and the responses were 
evaluated using conventional chemometric 
methods. In these experiments, although the 
resistance of each detector was sampled once 
every 3 seconds during each exposure, only the 
maximum relative differential resistance 
change, !'>.Rij, maxi Rb where !'>.Rij, miff is the 
maximum resistance change of the / detector 
during the ;th exposure and Rb is the baseline 
resistance of the detector prior to the exposure, 
was used in analysis of the data. 
To analyze the data in a consistent 
fashion for different detectors and vapors, the 
responses for each individual detector were 
autoscaled 12 so that all detectors, regardless of 
. their general response magnitudes, had the same 
operating range in the analysis of the data. The 
autoscaled response of the Jth detector to the jth 
exposure, Aij, was obtained from 
A;· \t'.R;j.max I 11;,)- j • (l) 
~ 13 . 
. 1 
The tenns Uj and f3J represent the mean and 
standard deviation, respectively, of the 
maximum relative differential resistance 
response of the lh detector to the entire group 
of analytes. 
The autoscaled patterns that resulted 
from exposure of fourteen carbon black-
728 
polymer composite chemiresistor detectors to 
acetonitrile, benzene or chloroform are depicted 
in Figure 1. Although these patterns are 
obviously different even to the untrained human 
eye, the goal of this work was to assess in a 
quantitative fashion the differences between 
such patterns. 
Neural networks were not used to 
analyze these differences because although the 
performance of neural networks in pattern 
classification can be superior to that of 
statistically based chemometric methods, the 
use of a neural network intimately couples the 
performance of a specific training/learning 
algorithm to the detector response data. A 
statistical measure of the differences between 
two clusters of patterns can be obtained by 
determining the distance between the centroids 
of the data arising from repeated exposures to a 
given vapor and dividing this distance by the 
sum of the standard deviations of the two 
different pattern clusters projected along the 
vector that counects these centroid points 
(Figure 2). It can be shown in a straightforward 
manner that this metric is independent of the 
coordinate system chosen to display the data 
and is instead an inherent, statistical property of 
the data set. 
From standard vector analysis, the mean 
response vector, xa, of an n-detector array to 
analyte a is given as the n-dimensional vector 
containing the Qll:an autoscaled response of 
each detector, Aa1, to the ath analyte as 
components such that, 
X a ~ (Aal, Aab ... , Aan) ~ (2) 
The average separation, I~ , between two 
analytes, a and b, in the Euclidean detector 
response space is then egual to the magnitude of 
the difference between Xa and xb . The noise of 
the detector responses is also important in 
quantifying the resolving power of the detector 
array. Thus the standard deviations, cr a J and 
cr b.d, obtained from all the individuaf jl!Tay 
responses to each of a and b along vector d, are 
used to describe the average separation and 
ultimately to define the pairwise resolution 
factor as 
I~ (3) rj~ 
fa 2 _ + 2 
l a,d b,d 
Assuming a Gaussian distribution relative to the 
mean value of the data points that are obtained 
from the responses of the array to any given 
analyte, the probabilities of correctly identifying 
an analyte as a or b from a single presentation 
when a and b are separated with resolution 
factors of 1.0, 2.0 or 3.0 are approximately 
76%, 92% and 98% respectively. This 
resolution factor is equivalent to that proposed 
by Miill~13 and recently used by Gardner and 
Bartlett and is basically a multi-dimensional 
analogue to the separation factors used to 
quantify the resolving power of a column in gas 
chromatography. 
Table 1 presents the resolution factors 
obtained from an array of the fourteen carbon 
black-polymer composite detectors for all 171 
pairs of the nineteen vapors. In general, the full 
array of carbon black-polymer detectors can 
easily resolve the odorant pairs presented at a 
fixed concentration. 
ACKNOWLEDGMENTS 
This work was supported by the U.S. 
Army Research Office contracts DAAH04-96-
l-0048 Modification 1 and DAAG55-97-1-
0187, DARPA, and NASA. 
729 
REFERENCES 
(I) Lonergan, M. C.; Severin, E. J.; 
Doleman, B. J.; Beaber, S. A.; Grubbs, 
R. H.; Lewis, N. S. Chem. Mater. 1996, 
8, 2298. 
(2) Zaromb, S.; Stetter, J. R. Sens. Actuators 
1984, 6, 225. 
(3) Lundstrom, 1.; Erlandsson, R.; Frykman, 
U.; Hedborg, E.; Spetz, A.; Sundgren, 
H.; Welin, S.; Winquist, Nature 1991, 
352,47. 
(4) Shurrner, H. V.; Gardner, J. W. Sens. 
Actuators B 1992, 8, l. 
(5) Gardner, J. W.; Bartlett, P. N. Sens. 
Actuators B 1994, 18, 211. 
(6) Gardner, J. W.; Hines, E. L.; Tang, H. C. 
Sens. Actuators B 1992, 9, 9. 
(7) Nakamoto, T.; Fukuda, A.; Moriizumi, 
T. Sens. Actuators B 1993, 10, 85. 
(8) Grate, J. W.; Abraham, M. H. Sens. 
Actuators B 1991, 3, 85. 
(9) Di Natale, C.; D'Amico, A.; Davide, F. 
A.M. Sens. Actuators A 1993, 37-38, 612. 
(10) Dickinson, T. A.; Walt, D. R.; White, J.; 
Kauer, J. S. Anal. Chem. 1997, 69, 3413. 
(11) Axel, R. Sci. Am. 1995, 154. 
(12) Hecht, H. G. Mathematics in Chemistry: 
An Introduction to Modern Methods; 
Prentice Hall: Englewood Cliffs, NJ, 
1990. 
(13) Muller, R. Sens. Actuators B 1991, 4, 35. 
(14) Gardner, J. W.; Bartlett, P. N. In 
Eurosensors IX; 1995; pp 169. 
4 
D acetonitrile 
D benzene 
"' 3 D chloroform ... 
"' = = c. 
"' 2 ... .. 
'C ... .... 
1 ... 
"' = .... = "' 0 
-1 
1 2 3 4 5 6 7 8 9 10 11 12 13 14 
sensor 
Figure 1. Autoscaled responses, Aij, of the fourteen detectors in the carbon black-polymer composite 
array to approximately 6.7 ppth of benzene, chloroform and acetonitrile. Each bar represents the 
average autoscaled response over twelve exposures and the error bars represent the standard deviation in 
the responses. 
• . ·- . • X • • a 
·__/ 
aa,d 
• 
' . 
• .X • •• 
b • . . -./ 
x = mean response 
cr projected st. dev . 
I~ rf=--=::::::!::::== 
fa2 - + 2 1 a,d b,d 
Figure 2. Mathematical definition of the resolution factor, rf, in multi-dimensional space. 
730 
Table 1. Resolution factors quantifying the ability of the fourteen element carbon black-polymer 
composite detector array to resolve pairwise each of the nineteen vapors, at fixed concentration, from 
any other vapor in the test set. The average and worst pairwise resolution factors are 9.7 and 0.91, 
respectively. 
0. ~ 0 ;;· '0 5' . 5'. 
0" 0 §= s. .§ :::0 " " ~ " 0 '< ~ "$. e- 0 -$. "" " 0 "$. 3 & .% ~ ., s ~- 0" 0 
0 s " ~ " c; 
"$. 
0 ~ §. 
~ "" " e- "' e- .g " ~-" E!. 0 " e- 0 g 8" c; 2: [ >< ., " 0 § § " 0 " ., ., " ~ "" " " " " " " " " 0 g. g. e- ii " " " 1 2-dimethoxy ethane 14 11 16 19 6.5 17 24 16 13 11 21 18 22 23 17 16 22 22 
acetone 7.0 18 10 7.1 2.7 14 3.6 5.2 2.2 3.3 5.5 14 12 2.0 17 11 21 
acetonitrile 11 8.7 9.3 8.4 11 7.1 12 7.5 8.6 13 10 II 8.6 19 10 II 
anisole 9.0 5.6 II 14 16 7.6 II 15 9.3 13 15 19 7.6 12 5.2 
benzene 7.1 1.8 21 15 5.8 3.8 2.8 5.6 19 16 2.2 12 4.9 12 
butyl amine 6.8 9X 7.5 7.1 6.5 7.3 8.5 8.4 9.3 7.1 6.4 7.9 6.9 
chloroform 4.9 2.9 6.0 5.1 2.0 6.3 4.2 4.8 9.3 14 4.0 4.4 
cyclohexane 21 7.5 6.5 5.2 6.2 20 0.91 3.8 14 4.5 19 
dichloromethane 5.6 2.9 3.3 5.4 20 19 2.9 16 10 23 
ethanol 6.2 5.6 3.5 7.1 7.5 6.2 12 6.1 6.4 
ethyl acetate 4.2 7.1 5.6 6.0 4.4 25 6.1 7.2 
isopropyl alcohol 5.6 4.8 4.4 1.6 13 7.5 8.4 
methanol 6.1 6.1 6.4 13 5.5 6.6 
n-heptane II 3.5 12 3.8 13 
n-pentane 3.7 14 4.4 18 
thf 17 3.2 6.5 
toluene II 7.0 
triethyl amine 10 
731 


English

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Vapor Detection, Classification, and Quantification Performance Using Arrays of Conducting Polymer Composite Chemically Sensitive Resistors

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