Polydiacetylene paper-based colorimetric sensor array for vapor phase detection and identification of volatile organic compounds

Article abstract of DOI:10.1039/c2jm16273c

Detection and identification of VOCs in their vapor phase is essential for safety and quality assessment. In this work, a novel platform of a paper-based polydiacetylene (PDA) colorimetric sensor array is prepared from eight diacetylene monomers, six of which are amphiphilic and the other two are bolaamphiphilic. To fabricate the sensors, monomers are coated onto a filter paper surface using the drop-casting technique and converted to PDAs by UV irradiation. The PDA sensors show solvent induced irreversible color transition upon exposure to VOC vapors. When combined into a sensing array, the color change pattern as measured by RGB values and statistically analyzed by principal component analysis (PCA) is capable of distinguishing 18 distinct VOCs in the vapor phase. The PCA score and loading plots also allow the reduction of the sensing elements in the array from eight to three PDAs that are capable of classifying 18 VOCs. Utilizing an array containing only two PDAs, various types of automotive fuels including gasoline, gasohol and diesel are successfully classified. The Royal Society of Chemistry 2012.

Full text of DOI:10.1039/c2jm16273c

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Journal of
Materials Chemistry
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PAPER
Polydiacetylene paper-based colorimetric sensor array for vapor phase
detection and identification of volatile organic compounds†
a
b
c
Thichamporn Eaidkong, Radeemada Mungkarndee, Chaiwat Phollookin, Gamonwarn Tumcharern,
d
c
c
Mongkol Sukwattanasinitt and Sumrit Wacharasindhu*
Received 30th November 2011, Accepted 10th January 2012
DOI: 10.1039/c2jm16273c
Detection and identification of VOCs in their vapor phase is essential for safety and quality assessment.
In this work, a novel platform of a paper-based polydiacetylene (PDA) colorimetric sensor array is
prepared from eight diacetylene monomers, six of which are amphiphilic and the other two are
bolaamphiphilic. To fabricate the sensors, monomers are coated onto a filter paper surface using the
drop-casting technique and converted to PDAs by UV irradiation. The PDA sensors show solvent
induced irreversible color transition upon exposure to VOC vapors. When combined into a sensing
array, the color change pattern as measured by RGB values and statistically analyzed by principal
component analysis (PCA) is capable of distinguishing 18 distinct VOCs in the vapor phase. The PCA
score and loading plots also allow the reduction of the sensing elements in the array from eight to three
PDAs that are capable of classifying 18 VOCs. Utilizing an array containing only two PDAs, various
types of automotive fuels including gasoline, gasohol and diesel are successfully classified.
Introduction
unique chromism properties. PDAs are a class of conjugated
polymers which can undergo distinct color changes from blue to
red under various external stimuli such as heat (thermochrom-
During the last decade, monitoring of volatile organic
compounds (VOCs) has gained enormous interest due to envi-
ronmental and public safety concerns posed by exposure to these
substances. Therefore, many researchers have been focusing on
37–42
43,44
ism),
mechanical stress (mechanochromism),
ligand–
and solvents (sol-
that make them invaluable transducers for
45–51
receptor interactions (affinochromism)
33,52–54
vatochromism)
many optical sensing systems.
1
–4
the development of VOC sensors. Changes of electrochemical
10–15
55–59
The latter property has
5–9
16–20
properties,
fluorescence
and color
of those sensing
recently drawn attention for the development of PDAs in sensors
for VOCs. Those reports share the same strategy in the prepa-
ration of a PDA library embedded in a polymer matrix in various
forms such as polyethyleneoxide or polystyrene electrospun
materials after exposure to VOCs provide signals for the detec-
tion and identification of the analytes. Among output signals, the
colorimetric mode is considered as the most convenient sensing
platform for developing a simple naked-eye VOC detector
because it minimizes the need for extensive signal transduction
hardware. Such an advantage will lead to practical on-site
analysis that can be delivered to non-technicians or end-users.
Reported colorimetric sensing materials for VOC sensors include
fibers as well as polydimethylsiloxane or poly(4-vinylpyridine)
31–36
spin-coating films.
Moreover, PDAs have also been incor-
porated into arrays generating color change patterns to each
VOC analyte leading to the detection and qualitative recognition
of VOCs. The sensing systems reported so far have been tested
successfully only with the liquid form of VOCs but not their
vapor phase. Some of these fabrication techniques are rather
time-consuming and require complicated instrument set up.
Furthermore, all the VOC discrimination was based only on the
visual observation of the color change without any statistical
analysis. Therefore, there remains a great challenge to improve
the fabrication and analysis method of these PDA sensing
systems. More diacetylene monomers should also be investigated
for an optimal array, which can efficiently detect and identify the
vapor of VOCs.
21–24
25–30
small organic compounds,
metal complexes
and conju-
31–36
gated polymers.
Among these sensing materials, poly-
diacetylenes (PDAs) are one of the most promising due to their
a
Graduate Program for Petrochemical and Polymer Science, Faculty of
Science, Chulalongkorn University, Bangkok, 10330, Thailand
b
Program in Biotechnology, Faculty of Science, Chulalongkorn University,
Bangkok, 10330, Thailand
c
Center for Petroleum Petrochemicals, Advanced Materials, Department of
Chemistry, Chulalongkorn University, Bangkok, 10330, Thailand. E-mail:
sumrit.w@chula.ac.th; Fax: +66-2-2187634; Tel: +662-218-7598
d
National Nanotechnology Center, National Science and Technology
Development Agency, Pathumthani, 12120, Thailand
60–62
As a continuation of our previous works on PDA sensors,
we would like to report herein the preparation of a VOC sensor
array from eight PDAs coated on filter paper. Filter paper is
selected as a sensing platform due to its many advantageous
†
characterization data of compounds 3–8; PCA loading plot; PCA score
Electronic
Supplementary
Information
(ESI)
available:
plot; color change profile. See DOI: 10.1039/c2jm16273c
5
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features i.e. (i) it is widely available, easy to use, store and
transport; (ii) it is thin and lightweight; (iii) it is biodegradable
and biocompatible; (iv) its white surface provides strong contrast
with colored substrates, making it a good medium for colori-
metric tests. The fabrication technique is fast and scalable giving
a stable and sensitive paper based sensor array, which can be
used for the detection and identification of 18 different VOC
vapors using a statistical pattern recognition method. Numerical
data of colorimetric responses to VOCs were conveniently
obtained from a flatbed scanner and an image processing
program. Principal component analysis (PCA) was used for the
multivariate statistical analysis. This paper based PDA sensor
array and the analysis method can also be applied successfully to
distinguish vapors of various automotive fuels such as gasoline,
gasohol and diesel.
fabrication was started by dropping a solution of the DA
monomer in an appropriate solvent on filter paper (Fig. 2a) and
allowing it to dry in the dark at room temperature. The size of the
DA dot can be easily controlled by the solution volume using an
auto pipette and the desired numbers of DA dots are created
simply by repeating the drop casting process at different loca-
tions on the filter paper. The resulting filter paper coated with
multiple dots of the DA monomer was irradiated with UV light
ꢀ2
(254 nm, 500 mW cm ) for 1 min to produce the replicated PDA
sensing dots (Fig. 2a). All DA monomers except 7 gave the
typical blue color of ene-yne p-conjugated PDAs indicating good
packing of the DA molecules. The color of PDA derived from 7
appeared as red, which is perhaps due to its unusual packing in
66
the molecular self-assembly. Pleasingly, the PDA prepared by
this method was very stable in that their color did not change
upon storage in a refrigerator over several months. It is thus
important to emphasize that this preparation technique is very
convenient and economical to construct a robust solid-state PDA
sensor array as shown in Fig. 2b. The microscopic images
obtained from an optical microscope also revealed that most of
the PDA pigments are deposited on the surface of the cellulose
fibers with little or no fiber penetration (Fig. 2c). This surface
coating behavior is desirable for colorimetric detection because it
requires less sensing material and provides better sensitivity.
Results and discussion
Synthesis of diacetylene monomers 1–8
In this work, eight diacetylene monomers (DA) such as amphi-
philic (1–6) and bolaamphiphilic (7–8) compounds were utilized
as precursors for preparation of PDA sensing elements in the
paper based array sensor for VOCs (Fig. 1). The monomers 1
(
PCDA) and 2 (TCDA) are well known and commercially
available while the others were prepared according to reported
procedures. Briefly, diacetylene lipid 3 was synthesized through
the Cadiot–Chodkiewicz coupling reaction between the corre-
sponding alkynoic acid with terminal alkyne in the presence of
Colorimetric detection of VOC vapors
Although the solvent dependent color transition of PDAs has
31–36
been well-investigated in many literature works,
the sensing
38
copper iodide. Diacetylene monomers 4 and 5 were prepared
application of PDAs for VOCs in the vapor phase have never
been reported. Therefore, the PDA coated filter papers were
tested for this application by attaching them on the inner surface
of a lid of a chamber saturated with a wide range of VOC vapors
i.e. pentane, hexane, cyclohexane, toluene, o-xylene, benzene,
from the condensation reaction between PCDA (1) with appro-
63
priate diamines. The addition of HBr to secondary amine 5
63
gave ammonium salt 6 in reasonable yield. Finally, bolaam-
phiphilic monomers 7 and 8 were synthesized from a copper
catalyzed Glasser homocoupling reaction between the corre-
diethyl
ether,
dichloromethane
(DCM),
2-propanol,
64,65
sponding terminal alkynoic acids.
Detail synthetic proce-
dures and characterization data of compounds 3–8 are available
in the ESI.†
Preparation of PDA coated paper
With the panel of diacetylene monomers (DA) in hand, we next
focused on their fabrication onto filter paper sheets. The
Fig. 2 (a) Preparation of paper-based PDA sensor and (b) scanned
images of sensor array constructed from 1–8 exposed with UV light
ꢀ
2
1 min, 500 mW cm ). (c) Microscopic images of a paper based PDA
(
Fig. 1 Structures of diacetylene monomers (1–8).
sensor from 8 before (left) and after exposure to THF vapor (right).
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tetrahydrofuran (THF), chloroform (CHCl ), ethanol (EtOH),
3
a conventional image-processing program. A three-dimensional
vector (DR, DG, DB) of each PDA sensor was determined from
the RGB values before and after exposure to the VOC vapors.
The histogram plot of these vectors against the VOC vapors
generated a color change profile of the array (Fig. 4), which
illustrated distinguishable color change patterns of the VOC
vapors. To highlight the discrimination efficiency of our PDA
array, the responses of VOCs with similar structures and polarity
are mentioned here. For example, hydrocarbon VOCs (pentane,
hexane, cyclohexane, toluene, o-xylene and benzene) induced
different response patterns on PDAs 2 and 3. PDA 2 is also
effective for discriminating between two chlorinated VOCs such
as chloroform and dichloromethane. Due to their significant
difference in solvating power nature, the ethereal solvents such as
ether and THF are readily distinguished by several PDAs
including 1, 5, 6 and 8. For solvents containing carbonyl groups
such as ethyl acetate, acetone and DMF, PDA 8 seemed to be the
most efficient sensing element for differentiating these VOCs.
For vapors of alcohols such as 2-propanol, ethanol and meth-
anol, PDAs 2 and 6 gave diverse responses. Notably, the quan-
titative color change profile (Fig. 4) is in good agreement with the
photographic images (Fig. 3). Importantly, the standard devia-
tion as depicted by the error bars in Fig. 4, calculated from 12
measurements (4 dots of PDA ꢁ 3 scans), was very small which
demonstrates the high precision of this method of measurement.
We attribute the high reproducibility of this method to the fact
that the PDA sensing spots are not in direct contact with the
liquid solvents, which can cause PDA detachment from the paper
surface.
ethyl acetate (EtOAc), acetone, methanol (MeOH), acetonitrile
(
(
MeCN), dimethylformamide (DMF) and dimethylsulfoxide
DMSO). To monitor the color changes of the PDA sensor array,
fa latbed scanner was chosen as the optical recorder as the images
67,68
are always in focus and not affected by lighting conditions.
Upon exposure to vapors for 1 h, the PDA dots displayed
various degrees of color transition as shown in Fig. 3. Due to its
high solvent sensitivity, poly(PCDA) was used for mixing with
less sensitive PDAs to generate solvent sensor arrays with a wide
69
range of solvent responses. However, in this work, poly(PCDA)
coated paper was colorimetrically insensitive toward most VOC
vapors except THF (Fig. 3, row 1). The results strongly suggested
that PDAs with higher sensitivity are required for construction of
an effective vapor-sensing array. Among the diacetylene mono-
mers tested in this work, 3 gave blue PDA coated paper with the
highest sensitivity, as it turned red upon the exposure to vapors
of most VOCs (Fig. 3, row 3). We believe that the high sensitivity
of this PDA is derived from weaker hydrophobic interaction
38,54,60
between its shorter side chains.
The PDAs obtained from
other monomers gave lower but variable sensitivities toward
various VOC vapors that provided an array of different color
patterns. These variable sensitivities of PDAs are governed by
the interactions among PDA side chains. The combination of
different alkyl chain lengths with various functional groups i.e.
carboxylic, dicarboxylic, amido, amino and ammonium groups
was proven to be effective for tuning the VOC sensitivity.
Notably, the color transition of this PDA array is irreversible so
that the color record by a flatbed scanner can be conveniently
conducted after the removal of the VOCs.
Since the colorimetric response patterns were based on 24
dimensions (8 polymers ꢁ 3 values of RGB), effective pattern
recognition and comparison require a statistical multivariate
analysis. In this work, we utilize principal component analysis
(PCA), a non-supervised method, to generate coordinates (PC
scores) recognizable within the lowest dimension represented by
the principal component (PCs) from the set of 5184 colorimetric
Evaluation of colorimetric response patterns
To evaluate 144 colorimetric responses of 8 PDA sensors against
1
8 VOC vapors, we converted the scanned images (TIF format)
into the RGB values (R ¼ red, G ¼ green and B ¼ blue) using
Fig. 3 Scanned images of the paper-based PDA sensor array prepared from 1–8 exposed to various saturated vapors of volatile organic solvents.
972 | J. Mater. Chem., 2012, 22, 5970–5977 This journal is ª The Royal Society of Chemistry 2012
5

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Fig. 4 RGB color change profile of a paper-based PDA sensor array prepared from 1–8 after exposure to saturated vapors of VOCs.
data set (18 VOCs ꢁ 12 repeats ꢁ 8 polymers ꢁ 3 values of
RGB). The PC score plot showed that the first and second
components (PC1 and PC2) accounted for 47.33% and 14.62% of
the data variance, respectively (Fig. 5). Within this 2D plot, 18
discrete clusters corresponding to all VOCs tested were clearly
observed. The results indicated high discriminating ability of this
paper based PDA sensor array. To evaluate the classification
accuracy, factorial discriminant analysis (FDA), a supervised
method, was applied on the PC scores to cross validate the
higher influence in the PC scores than sensors 1, 4, 5, 6 and 7 of
which the loading factors appear close to the origin point.
Therefore, three PC score plots of the data from each pair of
sensing elements selected from sensors 2, 3 and 8 were generated.
Again, FDA was used to cross validate the classification accu-
racy of the PCA score plots. Interestingly, both 2/8 and 3/8
sensor pairs provided the classification accuracy of 100% while
the 2/3 sensor pair gave a lower accuracy of 88% (see ESI†
Fig. S20–22). These outcomes imply that the combination of the
sensors from amphiphilic (2 or 3) and bolaamphiphilic mono-
mers (8) provides greater sensitivity variance toward the VOCs
compared with the sensor pair from only amphiphilic monomers
(2/3 pair). The high sensitivity variance in turn benefits the
discriminating efficiency of the sensor array.
70
discriminating ability using a leave-one-out technique. The
FDA cross validation gave 100% classification accuracy con-
firming the high efficiency of this sensor array.
For a sensor array, if the numbers of sensing elements can be
reduced without deterioration of the discriminating perfor-
mance, its application will be more user friendly. In order to
reduce the redundant and negative sensing elements in the
studied array, the plots of loading factors associated with PC1
and PC2 for each individual PDA sensor were evaluated
Application in classification of automotive fuels
Due to the current price hike of petroleum fuels, several bio-fuels
such as ethanol and fatty acid esters have been added to create
(
see ESI† Fig. S19). The plots showed that sensors 2, 3 and 8 had
Fig. 5 PCA score plot of RGB color changes obtained from the paper-based PDA sensor array upon exposure to 18 VOC vapors (8 PDAs ꢁ 4
replicates ꢁ 18 VOCs ꢁ 3 measurements).
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a variety of alternative fuels. In Thailand, for example, many
types of automotive fuel have been commercialized such as
gasoline octane 91 (G91), 10% ethanol blended gasoline widely
known as gasohol octane 91 and 95 (H91 and 95), 20% ethanol
blended gasoline (E20), diesel (D) and biodiesel (B5). Authenti-
cation and identification of these fuels are of commercial and
legal importance. Presently, the authentication and identification
of different types of gasoline are performed by direct observation
of the color generated by different additives (see the their
appearance in ESI† Fig. S23). However, this easy and simple
method does not prevent misuse and intentionally fake gasoline
because the color of the additive does not reflect the chemical
component within the fuel. Therefore, there remains a need for
an inexpensive on-site method for automotive fuel testing. Along
this line, we extended the scope of our paper based PDA sensor
array to identify these commercial automotive fuels.
From Fig. 6(top), the classification of fuels into gasohol
(including E20), gasoline and diesel (including biodiesel) is
possible by the color pattern of sensors 2 and 3. To ensure the
robustness of this sensor array, the reproducibility and discrim-
inating ability were evaluated using the PCA technique. The
RGB color-change values (see ESI† Fig. S24) were again con-
verted into a PCA score plot (Fig. 6(bottom)). From the plot,
a total variance of 97.54% can be obtained from the first two PCs
in which PC1 and PC2 contributed 84.52% and 13.02%, respec-
tively. The plot also showed well-separated clusters of diesel (D),
biodiesel (B5), E20, gasohol (H), gasoline (G) confirming the
high fuel discrimination ability of sensors 2 and 3.
It is also important to note that all the data points in each
cluster are very tight indicating a high reproducibility of this
sensing platform. In fact, the data points were so reproducible
that even the same type of fuels obtained from different
companies could be differentiated. These dissimilarities may be
attributed to different additives used by the oil companies.
Having identified that PDAs 2, 3 and 8 are the most infor-
mative sensing elements for the identification of VOC vapors, ten
automotive fuel samples (G91, H91, H95, E20, D and B5) from
two leading oil companies in Thailand (A and B) were tested with
selected PDA sensors by the same procedure as those performed
with the solvent vapors described above. Clearly, the PDA from
Conclusions
Paper-based PDA sensors were prepared by drop casting of
diacetylene monomers followed by UV photopolymerization,
a very simple and economical method. An array constructed from
2
showed blue to red color transition upon the exposure to
vapors of fuels containing ethanol (H95(A), H95(B), H91(A),
H91(B) and E20) and the PDA from 3 gave the response to both
gasohol and gasoline while the PDA from 8 was insensitive to all
type of fuels (Fig. 6(top)). None of these PDAs showed any
colorimetric response to diesel (D) and biodiesel (B5). The fuel
dependent color transitions described above are the results of the
difference in solvating power and vapor pressure of the fuels.
8
different diacetylene monomers was capable of distinguishing 18
VOCs in the vapor phase when utilized in combination with
principle component analysis. In its optimal form, an array con-
taining 3 chosen PDAs was capable of classifying 18 VOCs with
high reproducibility and discriminating ability. In addition,
a sensor array containing two PDAs was effective at dis-
tinguishing various types of automotive fuels. Thus, this is the first
platform truly proven to be effective for detection and identifi-
cation of a large numbers of VOCs in their vapor phase.
Experimental section
General information
1
0,12-Pentacosadiynoic acid (PCDA) and 10,12-tricosadiynoic
acid (TCDA) were purchased from GFS Chemical and other
reagents were obtained from Sigma-Aldrich and Fluka. All
organic solvents for monomer synthesis and purification were
purchased from TSL chemicals (Thailand) and were used
without further purification. For VOCs tested, all solvents are
1
13
high purity grade (HPLC grade). H and C spectra were
collected on a 400 MHz NMR spectrometer (Mercury 400,
Varian). Photographic images were recorded using a Can-
oScanner LiDE 200.
Preparation of diacetylene monomers (3–8)
6
,8-Nonadecadiynoic acid (3). To a stirred solution of 6-hep-
tynoic acid (3.0 mmol) and 1-iodo-1-dodecyne (3.6 mmol) in
pyrrolidine (15.0 mmol) was added copper(I) iodide (0.05 mmol).
Then the reaction mixture was stirred at room temperature for 3 h.
The reaction mixture was then added to an saturated aqueous
solution of ammonium chloride (50 mL) and extracted with
diethyl ether (3 ꢁ 50 mL). The combined organic layers were dried
Fig. 6 (Top) Scanned images of the paper-based PDA sensor array
prepared from 2, 3 and 8 before (blank) and after exposure to 10 auto-
motive fuel vapors. (Bottom) PCA score plot of RGB color changes
obtained from the paper-based PDA sensor array derived from 2 and 3
upon exposure to automotive fuels (2 PDAs ꢁ 3 replicates ꢁ 10 auto-
motive fuels ꢁ 3 measurements of each RGB).
2 4
over anhydrous Na SO and filtered, and the solvent was removed
by a rotary evaporator. The crude product was purified by column
5
974 | J. Mater. Chem., 2012, 22, 5970–5977
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1
3
chromatography with hexane and ethyl acetate (90:10) as an
CH ), 4.75 (brs, 1H, NH), 6.11, (brs, 1H; NH); C NMR (400
2
eluent to give 6,8-nonadecadiynoic acid (3) (650.00 mg, 75% yield)
MHz,CDCl ): d (ppm) ¼ 173.5, 77.6, 77.5, 65.3, 65.2, 48.4, 43.7,
3
ꢂ
1
as a white powder: mp 57–59 C; H NMR (400 MHz, CDCl ):
38.7, 31.9, 29.6, 29.6, 29.6, 29.5, 29.5, 29.3, 29.3, 29.1, 29.2, 29.1,
3
d (ppm) ¼ 2.31 (t, J ¼ 7.4 Hz, 2H), 2.22 (t, J ¼ 6.9 Hz, 2H), 2.17
28.9, 28.9, 28.8, 28.4, 28.3, 25.7, 22.7, 19.2. ESIMS m/z ¼ 444.58
+
[M] and 445.48 [M + H] .
+
(
t, J ¼ 7.1), 1.68 (m, 2H), 1.52 (m, 2H), 1.43 (m, 2H), 1.30–1.28 (m,
ꢀ
1
4H), 0.81 (t, J ¼ 6.8 Hz, 3H); ESIMS m/z ¼ 289.26 [M ꢀ H]
(calcd for C19
calcd for C19
H
30
O
2
, 290.44). Anal. Found: C, 78.71; H, 10.53
: C, 78.57; H, 10.41).
N-Ethyl-2-pentacosa-10,12-diynamidoethanaminium bromide
(6). N-(2-(Ethylamino)ethyl)pentacosa-10,12-diynamide (400.00
mg, 0.90 mmol) was dissolved in chloroform and hydrobromic
acid (HBr) (97.00 mL, 1.80 mmol). The mixture was stirred at
room temperature for 1 h. Water (20 mL) was added and the
mixture was extracted with chloroform. The organic phase was
dried with sodium sulfate and rotary evaporated to yield the
crude product as a white powder. The crude product was
recrystallized by chloroform to give N-ethyl-2-pentacosa-10,12-
diynamidoethanaminium bromide (6, 307.00 mg, 65% yield) as
(
H
30
O
2
N-(2-Aminoethyl)pentacosa-10,12-diynamide (4). A solution of
0
1
-ethyl-3-(3 -dimethylamino) carbodiimide HCl salt (EDC)
(
246.14 mg, 1.28 mmol) in methylene chloride (2 mL) was added
dropwise into a solution of 10,12-pentacosadiynoic acid (PCDA)
400.00 mg, 1.07 mmol) in methylene chloride (5 mL). The
(
mixture was stirred for 1 h at room temperature and was then
added dropwise into a solution of N-hydroxysuccinimide (NHS)
ꢂ
1
(
147.77 mg, 1.28 mmol) in methylene chloride (2 mL) at room
a white powder: mp 115–118 C; H NMR (400 MHz,CDCl
d (ppm) ¼ 0.88 (t, J ¼ 6.8 Hz, 3H; CH ), 1.40, (m, 32H; CH
), 2.25 (m, H3; CH
), 7.65 (brs, 1H; NHC]O);
3
):
temperature. After the reaction mixture was stirred at room
temperature overnight, water (20 mL) was added and extracted
with methylene chloride (25 ꢁ 3 mL). The organic phase was
dried with sodium sulfate and rotary evaporated to yield the
crude product as a white powder. Then the crude product was
dissolved in methylene chloride (10 mL) and added dropwise into
a solution of ethylenediamine in methylene chloride (2 mL). The
mixture was then kept stirring for 4 h at room temperature. The
mixture was extracted with methylene chloride (25 ꢁ 3 mL) and
the organic phase was dried with sodium sulfate and rotary
evaporated to yield the crude product as a white powder. The
crude product was purified by column chromatography on silica
gel eluted with a mixture of ethyl acetate and methanol (70:30) to
give N-(2-aminoethyl)pentacosa-10,12-diynamide (4, 325.00 mg,
3
2
),
1.62 (t, J ¼ 7.2 Hz, 3H; CH
3
2
), 3.12 (m, 4H;
CH
2
), 3.68 (q, J ¼ 4.4 Hz, 2H; CH
2
13
C NMR (400 MHz,CDCl
3
): d (ppm) ¼ 175.5, 77.7, 77.4, 65.3,
65.2, 47.9, 43.5, 36.5, 36.3, 31.9, 29.7, 29.6, 29.7, 29.5, 29.4, 29.2,
29.2, 29.1, 28.9, 28.9, 28.9, 28.3, 28.4, 25.6, 22.7, 19.2, 19.2, 14.1,
+
+
11.3. ESIMS m/z ¼ 444.62 [M] and 445.50 [M + H] .
5,7-Dodecadiynedioic acid (7). A solution of 5-hexynoic acid
(400 mL, 3.60 mmol) in DI water (15 mL) in a three-neck round
bottom flask was bubbled with oxygen gas at room temperature.
Then copper(I) chloride (1790 mg, 18.10 mmol) and ammonium
chloride (1930 mg, 36.00 mmol) were successfully added. After
the reaction mixture was vigorously stirred for overnight, 10 mL
of 1 M hydrochloric acid cold solution was poured into the
mixture and the precipitate was collected and dried under
vacuum oven at room temperature to give 5,7-dodecadiynedioic
ꢂ
1
9
2% yield) as a white powder: mp 111–114 C. H NMR (400
MHz, CDCl ): d (ppm) ¼ 0.87 (t, J ¼ 6.8 Hz, 3H), 1.42 (m, 32H),
3
2
.18 (t, J ¼ 7.6 Hz, 2H), 2.23 (t, J ¼ 6.8 Hz, 4H), 2.84 (t, J ¼ 5.7
13
ꢂ
Hz, 2H), 3.31 (q, J ¼ 5.8 Hz, 2H), 5.94 (brs, 1H); C NMR (400
acid (7, 393.00 mg, 98% yield) as a white powder: mp 137–139 C;
1
MHz, CDCl ): d (ppm) ¼ 173.6, 77.6, 77.5, 65.3, 65.3, 41.6, 41.6,
H NMR (400 MHz, CDCl ): d (ppm) ¼ 1.79 (m, 4H; CH ), 2.32
3
3
2
1
3
4
2
4
1.3, 36.8, 31.9, 29.6, 29.6, 29.5, 29.3, 29.2, 29.2, 29.1, 28.9, 28.9,
+
(t, J ¼ 2.33 Hz, 4H; CH
2
), 2.43 (t, J ¼ 2.43 Hz, 4H; CH
2
); C
8.7, 28.3, 25.7, 22.7, 19.2, 19.2, 14.1; ESIMS m/z ¼ 416.50 [M] ,
NMR (400 MHz, CDCl
3
): d (ppm) ¼ 19.4, 25.0, 33.8, 51.7, 51.9,
+
+
ꢀ
38.47 [M ꢀ H + Na] and 439.34 [M + Na] .
67.1, 77.4, 175.5. ESIMS m/z ¼ 220.46 [M] .
0
N-(2-(Ethylamino)ethyl)pentacosa-10,12-diynamide (5). N,N -
Dicyclohexylcarbodiimide (DCC) (264.18 mg, 1.28 mmol) in
methylene chloride (4 mL) was added dropwise into a solution of
10,12-Docosadiynedioic acid (8). A solution of 10-undecynoic
acid (200 mg, 1.1 mmol) in tetrahydrofuran (15 mL) in a three-neck
round bottom flask was bubbled with oxygen gas at room temper-
ature. Then copper(I) chloride (545 mg, 5.50 mmol) and ammonium
chloride (590 mg, 11.00 mmol) were successfully added. After the
reaction mixture was vigorously stirred overnight, 10 mL of 1 M
hydrochloric acid cold solution was poured into the mixture and the
precipitate was collected and dried under vacuum oven at room
temperature to give 10,12-docosadiynedioic acid (8, 179.00 mg, 90%
1
0,12-pentacosadiynoic acid (PCDA) (400.00 mg, 1.07 mmol) in
ꢂ
methylene chloride (7 mL). The mixture was stirred at 0 C for 1
h. N-Ethylethylenediamine (146 mL, 1.38 mmol) was added
dropwise into the reaction mixture at room temperature. The
reaction mixture was stirred at room temperature overnight. The
mixture was extracted with methylene chloride (25 ꢁ 3 mL). The
organic phase was dried with sodium sulfate and evaporated to
yield the crude product as a white powder. The crude product
was purified by column chromatography on silica gel eluted with
a mixture of ethyl acetate and methanol (70:30) to give N-(2-
ꢂ
1
yield) as a white powder: mp 100–103 C. H NMR (400 MHz,
CDCl ), 2.42 (t, J ¼ 2.24 Hz, 4H;
): d (ppm) ¼ 1.51 (m, 12H; CH
CH ). C NMR (400 MHz,
), 2.28 (m, J ¼ 2.28 Hz; 4H; CH
CDCl
3
2
13
2
2
3
): d (ppm) ¼ 19.5, 25.8, 29.3, 29.6, 29.8, 29.9, 30.0, 34.6, 66.2,
ꢀ
(
ethylamino)ethyl)pentacosa-10,12-diynamide (5, (417.00 mg,
77.7, 175.9. ESIMS m/z ¼ 360.51 [M] .
ꢂ
1
8% yield) as a white powder: mp 64–66 C; H NMR (400 MHz,
8
CDCl ): d (ppm) ¼ 0.86 (t, J ¼ 6.8 Hz, 3H; CH ), 1.09 (t, J ¼ 7.1
3
3
Fabrication of PDA coated paper
Hz, 3H; CH ), 1.33 (m, 16H, 32H; CH ), 2.15 (t, J ¼ 7.6 Hz, 2H:
3
2
CH ), 2.21 (t, J ¼ 6.9 Hz, 4H; CH ), 2.64 (q, J ¼ 7.2 Hz, 2H;
A diacetylene solution (2 mL) in 2-propanol or THF (1% w/v) was
dropped on
2
2
CH
2
), 2.74 (t, J ¼ 7.6 Hz, 2H; CH
2
), 3.33 (q, J ¼ 5.7 Hz, 2H;
a
piece of filter paper (Whatman No.1
This journal is ª The Royal Society of Chemistry 2012
J. Mater. Chem., 2012, 22, 5970–5977 | 5975

View Article Online
chromatography paper). The paper was allowed to dry in the air
at room temperature for 60 min. A hand-held UV lamp has
8 E. Llobet, J. Brezmes, X. Vilanova, J. E. Sueiras and X. Correig, Sens.
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T. Kida, N. Morinaga, S. Kishi, K. M. An, K. W. Sim, B. Y. Chae,
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56, 7484–7490.
ꢀ
2
5
00 mW cm at 254 nm, hung 10 cm above the dotted white filter
paper, and was used for UV-polymerization for 1 min to provide
PDA coated papers.
10 N. H. Yusoff, M. M. Salleh and M. Yahaya, Adv. Mater. Res., 2008,
5–57, 269–272.
5
1
1 F. Samain, S. Ghosh, Y. N. Teo and E. T. Kool, Angew. Chem., Int.
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Detection of VOCs and colorimetric measurements
1
The organic solvents (10 mL) were poured into chambers and
kept for 60 minutes before placing the dotted blue filter paper
within the chamber. The chambers were then placed in a fume
hood. Filter papers containing the blue PDA dots were attached
on the inside surface of a cover of a chamber containing the VOC
being tested. The cover was then used to close the chamber
tightly for 60 min at room temperature. The cover was removed
from the chamber and the photographic images of the PDA dots
were recorded using a scanner. Each image of PDA dots was
13 A. B. Descalzo, M. Dolores Marcos, C. Monte, R. Mart ꢀı nez-M ꢀa n~ ez
and K. Rurack, J. Mater. Chem., 2007, 17, 4716–4723.
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1
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1
1549.
7 K. S. Suslick, N. A. Rakow and A. Sen, Tetrahedron, 2004, 60, 11133–
1138.
1
18 M. L. Muro, C. A. Daws and F. N. Castellano, Chem. Commun.,
2008, 6134–6136.
2
saved in TIF format and cropped into 0.5 ꢁ 0.5 cm areas. The
1
9 K. Kawamura, K. Kerman, M. Fujihara, N. Nagatani, T. Hashiba
and E. Tamiya, Sens. Actuators, B, 2005, 105, 495–501.
RGB values of the cropped images were obtained from Adobe
Photoshopꢀ. The DR, DG and DB values were determined from
the RGB values before and after exposure to VOC saturated
vapors and were used for the histogram plot.
20 M. C. Janzen, J. B. Ponder, D. P. Bailey, C. K. Ingison and
K. S. Suslick, Anal. Chem., 2006, 78, 3591–3600.
2
2
1 X. Zhang and Y. Chen, Anal. Chim. Acta, 2009, 650, 254–257.
2 K. Tian, D. Hu, R. Hu, S. Wang, S. Li, Y. Li and G. Yang, Chem.
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2
2
2
3 S. Kang, S. Kim, Y. K. Yang, S. Bae and J. Tae, Tetrahedron Lett.,
2
Data analysis for vapors of VOC discrimination
009, 50, 2010–2012.
4 J. H. Hines, E. Wanigasekara, D. M. Rudkevich and R. D. Rogers, J.
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The set data of 5184 RGB numerical data (3 RGB values ꢁ 18
solvents ꢁ 8 PDAs ꢁ 4 pieces of paper ꢁ 3 scans) were then
tabulated and analyzed by principal component analysis (PCA;
Unscrambler 9.7 software version) to generate clusters of data in
the PCA score plot.
5 K. J. Wallace, S. R. Cordero, C. P. Tan, V. M. Lynch and
E. V. Anslyn, Sens. Actuators, B, 2007, 120, 362–367.
26 N. A. Rakow, M. S. Wendland, J. E. Trend, R. J. Poirier,
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2
7 A. C. Paske, L. D. Earl and J. L. O’Donnell, Sens. Actuators, B, 2011,
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Acknowledgements
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2
8 H. Lin and K. S. Suslick, J. Am. Chem. Soc., 2010, 132, 15519–15521.
9 N. Gerasimchuk, A. N. Esaulenko, K. N. Dalley and C. Moore,
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This study is financially supported by the Thailand Research
Fund (TRF-RSA54) and National Nanotechnology Center
3
(
NANOTEC), NSTDA (NN-B-22-FN9-10-52-06), Center for
Petroleum, Petrochemicals and Advanced Materials, Chula-
longkorn University. This work is part of the Project for
Establishment of Comprehensive Center for Innovative Food,
Health Products and Agriculture supported by the Thai
Government Stimulus Package 2 (TKK2555, SP2) and also the
National Research University Project of CHE (AM 1006A). The
31 J. Yoon and J. M. Kim, Macromol. Chem. Phys., 2008, 209, 2194–
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2 J. Yoon, Y. S. Jung and J. M. Kim, Adv. Funct. Mater., 2009, 19, 209–
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5 J. Lee, S. Balakrishnan, J. Cho, S. H. Jeon and J. M. Kim, J. Mater.
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8 C. Phollookin, S. Wacharasindhu, A. Ajavakom, G. Tumcharern,
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4
th
student scholarship was received from 90 Anniversary of
Chulalongkorn University fund (Ratchadaphiseksomphot
Endowment Fund). We would like to thank Jade-tapong Klahan
for the graphical abstract.
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