chemosensors
Article
vQRS Based on Hybrids of CNT with PMMA-POSS
and PS-POSS Copolymers to Reach the Sub-PPM
Detection of Ammonia and Formaldehyde at Room
Temperature Despite Moisture
Abhishek Sachan 1,2
1
2
*
, Mickael Castro 1 , Veena Choudhary 2 and Jean-François Feller 1, *
Smart Plastics Group, Bretagne Loire University (UBL), IRDL CNRS 3744-UBS, Lorient 56321, France;
abhishek.sachan@univ-ubs.fr (A.S.); mickael.castro@univ-ubs.fr (M.C.)
Indian Institute of Technology (IIT), Centre for Polymer Science & Engineering, Delhi 110016, India;
veenac@polymers.iitd.ac.in
Correspondence: jean-francois.feller@univ-ubs.fr
Received: 11 June 2017; Accepted: 10 July 2017; Published: 12 July 2017
Abstract: Nanocomposite-based quantum resistive vapour sensors (vQRS) have been developed from
the assembly of hybrid copolymers of polyhedral oligomeric silsesquioxane (POSS) and poly(methyl
methacrylate) (PMMA) or poly(styrene) (PS) with carbon nanotubes (CNT). The originality of the
resulting conducting architecture is expected to be responsible for the ability of the transducer to detect
sub-ppm concentrations of ammonia and formaldehyde at room temperature despite the presence
of humidity. In particular, the boosting effect of POSS is evidenced in CNT-based nanocomposite
vQRS. The additive fabrication by spraying layer-by-layer provides (sLbL) is an effective method
to control the reproducibility of the transducers’ chemo-resistive responses. In dry atmosphere, the
two types of sensors showed a high sensitivity towards both hazardous gases, as they were able to
detect 300 ppb of formaldehyde and 500 ppb of ammonia with a sufficiently good signal to noise
ratio (SNR > 10). They also exhibited a quick response times less than 5 s for both vapours and, even
in the presence of 100 ppm of water, they were able to detect small amounts of gases (1.5 ppm of NH3
and 9 ppm of CH2 O). The results suggest promising applications of POSS-based vQRS for air quality
or volatolome monitoring.
Keywords: quantum resistive vapour sensor; toxic gases sub ppm detection; ammonia; formaldehyde;
room-temperature; nanocomposite; humidity; spraying layer-by-layer
1. Introduction
There are now much evidence that environmental issues are related to human activity growth, in
particular global warming, and water and air pollution. The latter results in serious troubles to human
health, as frequently pointed out by the World Health Organization (WHO) [1]. The WHO reported
that 8.2 million deaths caused by the environment come from non-communicable diseases, such as
stroke, heart disease, cancers, and chronic respiratory disease, which now amounts to nearly two-thirds
of the total deaths caused by unhealthy environments [2]. Figure 1 shows the death attributable to
joint effects of both household and ambient air pollution. Actually, one-third of cancer deaths could
be prevented by anticipated diagnosis and this perspective holds significant potential to solve this
issue [3,4].
Chemosensors 2017, 5, 22; doi:10.3390/chemosensors5030022
www.mdpi.com/journal/chemosensors
Chemosensors 2017, 5, 22
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’
Figure 1. According to the world health organization (WHO), air pollution is now one of the world’s
largest health risks when excluding communicable diseases; deaths attributable to joint effects of both
household and ambient air pollution [5]. * Low and middle income countries.
Therefore, different strategies can be envisaged to counterbalance this trend and enhance people’s
quality’ of life. Trying to reduce the impacts of human activity on the environment seems a difficult
and long-term task, although necessary to lead. However, developing anticipation tools for the
early diagnosis of cancer or monitoring air quality, and, in particular toxic vapours, must be more
effective in the short-term. In this context the detection of toxic gases, such as ammonia (NH3 ),
is of great interest especially for environmental gas monitoring [6], exhaust gas determination
in automobiles [7], leakage detection in chemical plants [8], and product quality assurance in
food companies [9]. The limit of concentration of short term exposure to ammonia is 50 ppm
for 30 min [10], while for longer times it is only 20 ppm. Classical ammonia sensors are mainly
based on metal oxides (MOx) [11], intrinsically-conducting polymers (ICP) [12], catalytic sensors [13],
and spectrophotometric detection [14]. Nevertheless, the detection of ammonia at the sub-ppm
level remains challenging for most of these sensors, which require high temperatures to activate,
react with ammonia molecules, or eliminate moisture [15,16]. Additionally, ICP-based sensors can
’
lack reproducibility due to the dependency of their responses’ amplitude on several parameters,
such as the active layer thickness and morphology, its porosity, the nature of the dopant, and the
presence of other components in the material [12]. Several works also mention the use of carbon
–
nanotube-based sensors for the detection of ammonia [17–19], but CNTs without functionalization
have poor chemical selectivity [20] and the resulting conducting architecture tends to have a
poor structural stability when assembled without any binder or matrix [21]. This is why several
researchers have experimented the functionalization of CNT by insulating polymers, like PMMA [22]
–
chitosan [23], poly(lactic acid) PLA [24], or intrinsically-conducting polymers (ICP), like poly(pyrrole)
(PPy) [25–27], poly(thiophene) (PEDOT) [27,28], or poly(aniline) (PANI) [27,28], which can lead to
a positive synergy. However, it seems that reaching the ppb range of detection makes the use of
nanostructured transducers compulsory, as only the few works using this strategy report such a high
sensitivity. Wojkiewicz et al. [12] synthesized three kinds of nanostructured thin transducing films,
–
from the assembly of poly(butadiene) (PBuA–PANI), poly(vinyl fluoride) (PVDF-PANI) core/shell
nanoparticles, and PANI (CSA) nanofibers filled poly(urethane) films, demonstrating their ability
to detect, respectively, 250, 100, and 20 ppb of NH3 . Moreover, the chemo-resistive signals of these
systems are found to be reproducible and with low noise, but their recovery time is long (more than
20 min), compared to the exposure time of analytes of 5 min.
Chemosensors 2017, 5, 22
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Table 1 summarises the performances of some chemo-resistive nanocomposite transducers for
ammonia sensing found in the literature compared to the transducer developed in the present study.
Table 1. Ammonia sensing performances of some nanocomposite sensors, Op.: Operating.
Transducer
LOD
Mechanism
Op. Temp ◦ C
Reference
PMMA-POSS/CNT
V2 O5 and V7 O16 film
SnO2 -Nb-Pt nanocrystaline
Nanoporous NiO film
PPy NP
TiO2 NP deco GR/PPy
0.5 ppm
0.2 ppm
10 ppm
20 ppm
5 ppm
1 ppm
Chemo-resistive
Chemo-resistive
Chemo-resistive
Chemo-resistive
Chemo-resistive
Chemo-resistive
25
350
355
250
25
25
This work
[29]
[30]
[31]
[32]
[33]
Formaldehyde (CH2 O) is another common toxic gas that can be found inside buildings [34].
Mostly household, wooden, and plastic products or carpeting emit formaldehyde [35–37]. The limit
concentration of short-term exposure to formaldehyde is 0.08 ppm for 30 min [38] and 0.016 ppm for
long-term exposure [39]. Different types of sensors have been developed in the literature to detect
formaldehyde, such as spectro-colorimetric sensors by Suzuki et al., who made a device using organic
molecules that change colour when reacting with CH2 O [40], or amperometric sensors which take
benefit from an enzymatic reaction with formaldehyde molecules, which changes the electrical current
in the transducer [41]. Other sensors use metal oxides or have very complex testing setups and
generally require high working temperatures, for instance, SiO2 -NiO-based formaldehyde sensors
that operate at 300 ◦ C [42]. Li et al. [43] have tested Au-In2 O3 and In2 O3 nanoparticle-based sensors
for the detection of ammonia and formaldehyde at the concentration of 100 ppm. Nevertheless,
metal oxide-based gas sensors still require high operating temperatures (ca. 300–400 ◦ C) to allow
signal recovery after analyte exposure and to eliminate undesirable resistance variations, which is
energy consuming, and does not seem to completely fix baselines drifting upon oxidation by the
tested gases [44]. Nevertheless, some MOx hybrids were found to operate at room temperature with
ppm sensitivity [45,46]. Additionally, some strategies have been developed to increase their limit of
detection at the ppb level by decreasing the size of active particles (resulting in an increase of porosity).
For instance, hierarchically porous indium oxide In2 O3 nanolamellae with two levels of nanopores
were found able to detect 80 ppb of CH2 O although with a rather low signal-to-noise ratio [47], and
CuO2 nanocubes could reach the very low limit of detection of 6 ppb of CH2 O [48]. To take benefit
from nanostructuring and overcome the low selectivity of metal oxide transducers, Güntner et al. [49]
used flame spray pyrolysis to develop aggregates of SnO2 nanoparticles doped with Pt, Si, Ti, and
Pd to selectively detect analytes, such as CH2 O (at 30 ppb) and NH3 (at 250 ppb), in the presence of
90% H2 O. The fact that SnO2 sensors were able to operate in such difficult conditions is encouraging
for breath analysis, but they maintain a strong temperature dependency requiring reaching 400 ◦ C to
stabilize the resistance and allow the full signal recovery, consuming about 500 mW per sensor.
From this non-exhaustive overview on the different strategies used to sense CH2 O and NH3
at the very low concentration at which they are toxic, i.e., the sub-ppm level, the best results were
obtained with MOx- [49] or ICP-based [12] nanostructured resistive sensors. However, it can also be
concluded that there is still a challenge to develop vapour sensors: highly-sensitive and -selective to
CH2 O and NH3 , operating at room temperature to minimize consumption, working in high moisture
environments, and easily up-scalable into e-nose applications. In this context, we have used our
experience in the design of quantum resistive vapour sensors (vQRS) that already proved their
sub-ppm sensitivity to VOCs [50] and robustness towards 50% moisture [51] to investigate their
performances in this new frame and, accordingly, develop new sensors eventually integrable in an
e-nose. Carbon nanotubes were chosen to build the sensors’ conducting architecture because of
their exceptional aspect ratio, electrical conductivity, and ability to form light and easily-switchable
interconnected networks [52]. Polyhedral oligomeric silsesquioxane (POSS) was selected in this study
Chemosensors 2017, 5, 22
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because it recently proved to be an effective nanobrick to provide a nano-assembled conducting
architecture with chemical and geometrical functionalities [51]. POSS is a molecule of generic
formula Sin O1.5n composed of eight silicon atoms forming a cage of 0.53 nm diameter [53]. The
functionality and size of the groups attached to Si atoms can be varied according to the required
applications (copolymerization and blending) [54,55]. In particular, various types of organic chains
can be attached to the POSS cage, which can modify its solubility in different polymers, providing
a better dispersion in the matrix [56]. The groups attached to the Si atoms of POSS can be used
to enhance the compatibility with the host polymer taking benefit of the properties of inorganic
nanoparticles [57]. POSS, when grafted with reactive functional groups, can be copolymerized with
more conventional monomers. The resulting organic-inorganic hybrid is expected to have enhanced
properties compared to the organic homopolymer, such as an increased specific surface, and a larger
flexibility [58,59]. POSS-related materials meet applications in polymer based electrolytes [60,61],
biomaterials [62,63], hybrid nanocomposites [64], polymer-based optoelectronic devices [65], and
sensitivity enhancers in vapour sensing application [51]. In order to maximize the effect of POSS
it was decided to incorporate it directly in the polymer chains from the matrix. Poly(styrene) (PS)
and poly(methyl methacrylate) (PMMA) were selected because of their amorphous nature, which is
known to increase their permeability to vapours and, thus, decrease the response time of the derived
transducers [21]. Of course, the second criteria was their ability to be copolymerized with reactive
POSS. Moreover Wu et al. showed that PS-co-POSS did not crystallize, even generating additional free
volume, leading to a higher level of plasticization of the matrix [63,66–68]. A similar behaviour was
observed with PMMA-co-POSS that exhibited a very low crystallinity [69] and presented a decreased Tg
compared to the homopolymer [70]. Therefore, the introduction of POSS in CNT-based CPC is expected
to boost their sensitivity [51] by modifying the molecular mobility of polymers without causing any
segregation thanks to their copolymerization in the same chain. The cage structure of POSS molecules
and their tailorable organic functionalities should also provide additional means of discrimination
of vapours, complementary to those resulting from molecular interaction of analytes with polymer
chains and carbon nanotubes. To validate these assumptions, solutions of CNT co-dispersed with
poly(POSS-co-styrene), and poly(POSS-co-methyl methacrylate) have been synthesized, then sprayed
layer-by-layer (sLbL) on microelectrodes to characterized their chemo-resistive behaviour when
exposed to ammonia and formaldehyde vapours.
2. Experimental
2.1. Materials
Multi-walled carbon nanotubes (CNT) were obtained from Nanocyl SA, Sambreville, Belgium.
MWCNT were prepared by catalytic carbon vapour deposition (CCVD) method. They have 90% purity,
average diameters of 9.5 nm, and average lengths of 1.5 microns. MWCNT were used without any
purification. Poly(methyl methacrylate) (PMMA) was VQ 101S from Rhöm (Munich, Germany). Flakes
of atactic poly(styrene) (aPS) were purchased from Polyscience (Paris, France) with an average molar
mass of Mn = 50,000 g·mol−1 . Poly[(propyl-methacryl-hepta isobutyl-PPS)-co-styrene], or PS-co-POSS,
and poly[(propyl-methacryl-hepta isobutyl-PPS)-co-methyl methacylate], or PMMA-co-POSS, were
procured from Sigma-Aldrich (St. Louis, MO, USA). Both copolymers contain 45 wt% of POSS
(about 7 mol%), randomly distributed in the polymer chain. All solvents and chloroform used in the
experiments were also obtained from Sigma-Aldrich (St. Louis, MO, USA).
2.2. Sensor’s Fabrication and Chemo-Resistive Characterization
Quantum resistive vapour sensors (vQRS) were prepared by spraying layer-by-layer (sLbL)
method using dispersion of copolymer and CNT in chloroform. Two dispersions were prepared by
sonication (Branson 3800) of each copolymer with 2 wt% CNT in chloroform to reach the concentration
of 10 g·dm−3 . Four layers of these two dispersions were sprayed over interdigitated electrodes.
Chemosensors 2017, 5, 22
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The resulting initial resistances were R0 = 17.9 ± 3 kΩ and R0 = 21.6 ± 5 kΩ for PS-co-POSS/CNT and
PMMA-co-POSS/CNT, respectively.
The chemo-resistive properties were analysed by recording the change in the electrical resistance
for both sensors. vQRS were exposed to five minutes’ alternative cycles of nitrogen and vapours
(formaldehyde or ammonia). These vapours were generated by an OVG4 (Owlstone Ltd., Cambridge,
UK) oven using calibrated permeation tubes of ammonia (NH3 ) and formaldehyde (CH2 O) at
100 cm3 ·min−1 , then sent to the sensor chamber for testing. The concentration of vapours in the
outlet of the oven was varied using a split flow controller from 100 ppm (parts per million) to 500 ppb
(parts per billion). Two different copolymers of POSS were used to investigate the effects of the polymer
chemical nature and physico-chemical structure on vQRS chemo-resistive characteristics. The change
in electrical resistance of sensors was measured by Keithley 6517 and recorded by a program made
with the Labview software (National Instruments, Nanterre, France). The response of the vQRS was
expressed as relative amplitude (AR ) given in Equation (1):
R − R0
(1)
R0
𝑹−𝑹𝟎
𝑨𝑹 =
𝟎
where R0 is the initial resistance of sensors in a dry 𝑹nitrogen
stream and R is the resistance in the
presence of pure or solvent-water vapour mix.
In order to assess the efficiency of the sensors in the ppm to ppb concentration range, the
signal-to-noise ratio (SNR) during exposure of VOC is calculated with Equation (2) according to [71]:
AR =
SNR = ∆Rmax /σbaseline
Δ
σ
(2)
where ∆R
Δ max defines the maximum resistance change upon exposing the sensor to analyte, σbaseline
σ
represents the standard deviation in baseline resistance before analyte delivery, calculated using at
least 10 data points.
The nanoscale characterizations were done by atomic force microscopy (AFM) in ambient
conditions using the light tapping mode (TM-AFM) on a calibre multimode scanning probe microscope
from Bruker-Veeco, Paris, France. The morphology of POSS-CNT sensors was observed under the
Zeiss EVO 50 scanning electronic microscope (SEM). A scheme summarizing the vQRS processing and
chemo-resistive sensing characterization is illustrated in Figure 2.
Figure 2. Scheme of the development, assembly, fabrication, and chemo-resistive characterization of
quantum resistive vapour sensors (vQRS).
Chemosensors 2017, 5, 22
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3. Results and Discussion
3.1. vQRS Morphological Characterization
The fabrication of POSS copolymer-functionalized CNT vQRS starts by the LbL spraying of
nanocomposite suspensions on interdigitated electrodes to structure, step-by-step, the transducer
from the nano to the microscale. In Figure 3a a SEM profile allows evaluating the average thickness
of the transducing films made of four layers was about 4 µm and also shows the porous nature of
transducers which is also visible from the top image in the Figure 3b. In Figure 3c,d, the AFM images
of, respectively, PMMA-co-POSS copolymer and its nanocomposite with CNT show the homogeneity
of the copolymer film and the good coating and dispersion of one CNT in the matrix. A second level of
porosity (80 nm) can also be observed from these two AFM pictures revealing another characteristic of
sLbL-structured transducers expected to favour analyte molecule diffusion to the CNT nanojonctions.
POSS molecules are not visible in these pictures, suggesting that they are not aggregated, as it was
shown that they tend to assemble in catenary nanoclusters [72].
’
Figure 3. (a) SEM image of the nanocomposite transducing film’s cross section; (b) SEM image
’ surface;
of transducers’ surface; (c) AFM image of PMMA-co-POSS copolymer; and (d) AFM image of
nanocomposite showing CNT in the copolymer matrix.
’
Porosity is promoting the analytes’ diffusion within the CPC transducers leading to shorter
response times as pointed out earlier for PS-CNP-based vQRS [73]. The different nature of the
two copolymers of POSS were used to investigate the effects of the chemical interactions and
physico-chemical structure on vQRS chemo-resistive characteristics. It is expected that the junctions
of the random network of CNT are coated with the synthesized nanocomposite in order to provide
effective chemo-sensitive nanoswitches acting on tunnelling conduction in the presence of analytes.
The molecular mobility of chains at room temperature (20 ◦ C) is expected to facilitate the disconnection
of junctions due to the local swelling induced by the CH2 O and NH3 vapour molecules. The amorphous
nature of both PS-co-POSS and PMMA-co-POSS matrices having glass transition temperatures (Tg ),
respectively, 20 ◦ C below and slightly above 100 ◦ C, as can be seen from Table 2, is the guarantee of
low density and absence of a gas barrier, such as crystalline lamellae. Nevertheless, comparing the
data obtained in the present work to others found in the literature does not allow concluding on any
−
−
Chemosensors 2017, 5, 22
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strong effect of either CNT of POSS on the Tg of PMMA or PS. At least the effect of these nanofillers is
in the same order of magnitude as that of the molar mass.
Table 2. The effect of nanofillers on nanocomposite glass temperature transitions Tg .
Tg (◦ C)
Material
PS (Mn = 50,000 g·mol−1 )
PMMA (Mn = 3.9 × 104 g·mol−1 )
PS-1% w/w CNT
PMMA-5% w/w CNT
PS-co-POSS/2% w/w CNT
PMMA-co-POSS/2% w/w CNT
100 [73]
100.2 [74]
105 [75]
102.5 [74]
80.7 [this work]
112.7 [this work]
3.2. vQRS Chemo-Resistive Characterization
3.2.1. Effect of POSS on the Chemo-Resistive Response?
If no strong effect of POSS on the polymer matrices’’ morphologies can be noticed, it is not the
case that their chemo-resistive responses are associated to CNT, as is clearly visible in Figure 4. POSS
molecules bring a clear boosting effect to the chemo-resistive responses of all CPC transducers. as
already found in a previous work on CNT-based vQRS [51].
(a)
(b)
Figure 4. The effect of the presence of POSS co-monomer on the chemo-resistive responses of PS-CNT
and PMMA-CNT sensors to (a) 2 ppm of CH2 O vapour and (b) 3 ppm of NH3 vapour.
When exposed to 2 ppm of formaldehyde vapours PS-CNT and PMMA-CNT vQRS see their
responses increasing by 88% and 64%, respectively, by the introduction of POSS, through the
substitution of undoped polymer nanocomposites by PS-co-POSS-CNT and PMMA-co-POSS-CNT. The
same conclusion can be made when the vQRS are exposed to 3 ppm of ammonia vapour. Compared
to undoped polymers, the addition of POSS boosts the responses of 114% and 100% for PS-CNT
and PMMA-CNT, respectively. These results confirm that POSS can effectively enhance the CNT
CNT network‘s
disconnection
to a spacing
effect
that canthefavour
the of
diffusion
analyte
network‘s
disconnection
thanksthanks
to a spacing
effect that
can favour
diffusion
analyte of
molecules
to the junctions.
3.2.2. Principle of CH2 O and NH3 Detection with vQRS
The sensing mechanism of vQRS is rather different from that of metal oxide or
semiconductor-based sensors; it is based on the disconnection of the conductive architecture
(at nanojunctions) resulting from the adsorption of vapour molecules both on carbon nanofillers and
Δ
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inside the amorphous phase of the polymer matrix (there is a partial swelling of the macromolecules
coating the nanojunctions) [76,77]. By increasing the average gap ∆Z between CNT, the vapour
molecules generate tunnelling conduction that yield exponential variations of the global resistance of
transducers [23,78], according to Equation (3), this is why these sensors are called quantum resistive
vapour sensors vQRS [21,51]:
Ar = aeb∆Z
(3)
where Ar is the relative variation of resistivity, and a and b are positive constants and ∆Z the gap
variation between two vicinal CNT (typically 1 < ∆Z < 10 nm).
The selectivity of such vQRS results from van der Waals interactions between the organic
component of the nanocomposite present at junctions and the analytes, which are well modelled
by the χ12 intermolecular interaction parameter from Flory-Huggins that can be calculated with
Equation (4):
Vm
χ12 =
(4)
− δTana )2
(δ
RT Tlink
where Vm is the molar volume of the solvent (cm3 ·mol−1 ), T is the temperature (K), the ideal gas
constant (R = 8.314 J·mol−1 ), and the analyte and linker global solubility parameters δTana and δTlink
(J1/2 ·cm−3/2 ), respectively.
The closest to zero χ12 is, the highest the interactions between polymer chains and organic
molecules and consequently the largest the swelling of junctions and the disconnection ability. Finally
n , as described in a previous work [77] and
the response amplitude of vQRS can be well related to 1/χ12
expressed in Equation (5):
b
n
Ar = a·e χ12
(5)
where χ12 is the Flory-Huggins intermolecular interaction parameter which can be determined using
Equation (4) and a and b are constants
This model is meaningful to link the chemo-resistive properties to the chemical structure of the
vQRS constituents.
For example, by using the Hansen’s solubility parameters from Table 3 yields the values of χ12
collected in Table 4. These results predict well that the lower the χ12 the larger the chemo-resistive
response Ar . It seems however more difficult to predict the effect of POSS molecules in the selectivity
of transducers, as they may act distinctly to enhance the disconnectability by space effect.
Table 3. Hansen’s solubility parameters of the organic compounds possibly interacting in the vQRS
using δt = (δD 2 + δP 2 + δH 2 )1/2 .
Compound
δD (MPa1/2 )
δP (MPa1/2 )
δH (MPa1/2 )
δt (MPa1/2 )
Vm (cm3 ·mol−1 )
NH3
C2 HO
PS
PMMA
13.7
12.8
5.9
10.5
16.7
14.4
18.7
18.8
18.8
15.4
3.5
5.7
28.63
24.66
19.92
22.27
25
36.9
-
Table 4. Flory-Huggins intermolecular interactions parameters.
χ12 /Ar
PS
PMMA
NH3
CH2 O
0.765/0.018
0.334/0.026
0.407/0.028
0.085/0.036
As the boosting effect of POSS on the responses of both PS-CNT and PMMA-CNT to CH2 O and
NH3 was demonstrated at the ppm level, only the performances of these two sensors will be tested
under more severe conditions in the following sections.
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3.2.3. Formaldehyde Sensing with PS-co-POSS/CNT and PMMA-co-POSS/CNT
’
’
As mentioned
’ earlier, CH2 O is a dangerous vapour even at low concentrations. Therefore, CH2 O
vapours were generated by an OVG4 oven at concentrations ranging from 0.3 ppm to 27 ppm, and
carried by a 100 cm3 ·min−1 nitrogen flow to sensors’ chamber. The two different selected vQRS,
PS-co-POSS/CNT and PMMA-co-POSS/CNT, were exposed to CH2 O nitrogen/analyte successive
cycles of 5 min in order to interpret the responses collected in Figure 5a. The shape of the two curves
appears very typical of the LHC model developed to fit the evolution of the chemo-resistive response
with the concentration
of the analyte and is expressed in Equation (6) [23,79,80]. The LHC model
Henry’s
also allows deducing whether penetrating solvent vapour molecules will only slightly adsorb on
specific sites (Langmuir), diffuse and form of several layers of vapour molecules on CPC (Henry), or
generate plasticization and swelling of the matrix through the clustering of solvent vapour molecules
(Clustering). Each of these three terms is expressed in Equation (6):
’
′
b ( f ′′ − f ) f
Ar = L
+ kH f + f − f ′ f n
1 + bL
(6)
where bL is the Langmuir affinity
’ constant, f is the solvent fraction, f ′ is the solvent fraction over
which clustering starts, f ” is the vapour fraction over which Langmuir’s diffusion replaced by Henry’s
diffusion, kH is Henry’s solubility coefficient,
and n′ is the
number of vapour molecules associated
−
−
in clusters.
Figure 5. (a) PS-co-POSS/CNT and PMMA-co-POSS/CNT sensors response towards CH2 O; and
(b) sensitivity of both sensors towards CH2 O, and CH2 O sensing in presence of 100 ppm of water by
(c) PS-co-POSS/CNT and (d) PMMA-co-POSS/CNT sensors respectively.
According to this model, and the shape of the two curves plotted in Figure 5a, it is possible to
state that between 0.3 ppm and 5 ppm the chemo-resistive behaviours resulting from the interactions
of molecules with the CNT network fits a Langmuir adsorption, whereas between 5 ppm and 27 ppm
Chemosensors 2017, 5, 22
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it is more coherent with Henry’s diffusion. These data demonstrate the capability of both vQRS to
detect sub ppm amounts of CH2 O vapours (300 ppb) and this good signal to noise ratio, i.e., SNR = 17
and 16 for PMMA-co-POSS/CNT and PS-co-POSS/CNT, respectively. Additionally, the PMMA-based
vQRS appears more sensitive at concentrations, i.e., under 15 ppm, than its PS homologue, but this
tendency seems to be reversed over 15 ppm due to a larger Henry’s diffusion coefficient of the latter
(in the second term of Equation (6) the slope is proportional to kH ). Under 5 ppm according to the
first term of the same equation, the slope of the curves presented in Figure 5b is proportional to bL ,
the Langmuir’s affinity constant. Actually, bL is also expressing the sensitivity at low concentration,
which was evaluated by a linear fit for PS-co-POSS/CNT and PMMA-co-POSS/CNT to, respectively,
0.011 ppm−1 and 0.014 ppm−1 with R2 = 0.996 and 0.992.
The effect of water molecules on the detection of analytes makes sense since they are present
almost everywhere in the atmosphere in rather large amounts and can even combine with analytes.
To study this fact, CH2 O detection was carried out in the presence of water to test the capability of
both vQRS to detect toxic vapours in harsh conditions. Thus, CH2 O concentration was varied from
5 ppm to 27 ppm in the presence of 100 ppm of water, and the results are presented in Figure 5c,d.
It can be seen that both sensors were able to detect very low contents of target analytes in a humidified
environment, despite a global decrease in the amplitude of their responses. This can be explained by
the fact that CH2 O can be partly soluble in water, and that the two molecules are competing for the
adsorption on the sensor’s nanojunctions responsible for the chemo-resistive response. However, at
ambient temperature (Tamb = 20 ◦ C), and in the presence of 100 ppm of water, it is remarkable that
both PS-co-POSS/CNT and PMMA-co-POSS/CNT sensors were able to detect 14 ppm and 9 ppm of
CH2 O, respectively proving their robustness against moisture.
3.2.4. Ammonia Sensing with PS-co-POSS/CNT and PMMA-co-POSS/CNT
PS-co-POSS/CNT and PMMA-co-POSS/CNT vQRS were also exposed to ammonia, the second
important toxic vapour of our study, under similar conditions as for CH2 O. NH3 vapours were
generated by the OVG4 oven through a membrane previously calibrated at concentrations ranging
from 500 ppb to 3 ppm in a nitrogen stream of 100 cm3 ·min−1 . This narrower range of concentrations
was due to operating limits of the device in particular the permeation tube and the oven temperature.
Nevertheless, is was possible to record the chemo-resistive signals of both sensors that were found
to respond quickly to 3 ppm pulses of NH3 , i.e., within 5 s with an increase of up to 4–6%, as seen
in Figure 6a,b. As expected AR was gradually decreasing with the concentration of NH3 , but was
large enough to allow the measurement with the satisfying SNR values of respectively 10 and 13 for
PMMA-co-POSS/CNT and PS-co-POSS/CNT. The sensitivity of sensors determined by a linear fit
in Figure 6c was also found to decease accordingly, i.e., 6.2 × 10−3 ppm−1 for PS-co-POSS/CNT and
7.8 × 10−3 ppm−1 for PMMA-co-POSS/CNT with R2 = 0.996 and 0.992 respectively. However, this
sensitivity is comparable to that obtained by Joulazadeh et al. [81] for sensors based on poly(pyrrole)
3.3 × 10−3 ppm−1 and for poly(pyrrole)-SnO2 7.9 × 10−3 ppm−1 .
Figure 7a,b summarizes the influence of the presence of 100 ppm of water on the detection of
NH3 molecules by the two sensors. It can be seen that, despite the decreasing values of AR due to
the presence of H2 O, PMMA-co-POSS/CNT and PS-co-POSS/CNT were still able to detect 1.5 ppm
and 3 ppm of NH3 . However, the fact that the two curves are merging at about 500 ppb of NH3 in
Figure 7a suggests that-PS based sensors will not be able to properly discriminate NH3 under its pure
form and when strongly hydrated.
−
−
−
−
Chemosensors 2017, 5, 22
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−
11 of 16
−
−
Figure 6. The effect of the addition of 100 ppm on (a) PS-co-POSS/CNT and (b) PMMA-co-POSS/CNT
’
sensors’ responses when exposed to varying concentrations of NH3 , and (c) their sensitivity
determination towards NH3 .
Figure 7. Summary of the effect of the presence of 100 ppm of water on the chemo-resistive responses
of (a) PS-co-POSS/CNT and (b) PMMA-co-POSS/CNT when exposed to NH3 .
’
Chemosensors 2017, 5, 22
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4. Conclusions
Quantum resistive vapour sensors made of a CNT conducting architecture functionalized by
POSS-hybridized PMMA and PS matrices, were nanostructured by spraying layer-by-layer to detect
vapours that can be toxic at only some tens of ppb. SEM and AFM characterized the interest
of this processing technique to generate micro- and nanopores facilitating the analytes’ diffusion
to the nanojunctions and, thus, the quickness of the sensors’ responses. Both types of vQRS
were exposed, firstly, to ppm amounts of NH3 and CH2 O to confirm the boosting effect of the
introduction of polyhedral oligomeric silsesquioxane on the chemo-resistive response of the sensors.
Then PMMA-co-POSS/CNT and PS-co-POSS/CNT were exposed to pulses of very small amounts of
ammoniac and formaldehyde vapours ranging from 300 ppb to 100 ppm to demonstrate their limit of
detection. At the minimum fraction of molecules allowed by our device, i.e., 0.3 ppm, both vQRS had
a SNR of about 15, which was still enough to allow for good measurements. In agreement with the
Langmuir-Henry-clustering model developed to fit electro-sorption experiments, it was possible to
identify that both vQRS had a chemo-resistive behaviour driven by Langmuir adsorption between
0.3 ppm and 15 ppm and by Henry’s diffusion between 15 ppm and 100 ppm. In degraded conditions
the two sensors exhibited decreasing values of their AR due to the presence of 100 ppm additional
moisture, but PMMA-co-POSS/CNT and PS-co-POSS/CNT were still able to detect 1.5 ppm and
3 ppm of NH3 . All POSS-based vQRS have demonstrated their fast response, ability to detect ppb
concentrations of NH3 and CH2 O toxic gases, and to work at room temperature in a humid atmosphere.
The POSS molecules were assumed to increase the sensing ability of the sensors, without much
changing their selectivity due to their action as nanospacers within the conducting network that gave
more mobility to macromolecules and more space for analytes to diffuse. The PMMA-co-POSS/CNT
sensor was slightly more sensitive than the PS-co-POSS/CNT sensors, but both of them could be good
candidates for integration into an e-nose designed for air quality and volatolomics monitoring.
Acknowledgments: We are grateful to Hervé Béllégou and Isabelle Pillin for their contribution to this work.
This research was funded by the University of South Brittany (UBS) in Lorient.
Author Contributions: A.S., M.C., V.C. and J.F.F. conceived and designed the experiments; A.S. performed
the experiments; A.S., M.C., V.C. and J.F.F. analyzed the data; A.S., M.C. and V.C. contributed
reagents/materials/analysis tools; A.S., M.C. and J.F.F. wrote the paper.
Conflicts of Interest: The authors declare no conflict of interest.
References
1.
2.
3.
4.
5.
6.
7.
8.
Lindmeier, C.; Osseiran, N.; Chriscaden, K. An Estimated 12.6 Million Deaths Each Year are Attributable to
Unhealthy Environments; World Health Organization: Geneva, Switzerland, 2016.
Cancer Today: Population Fact Sheets, World Estimated Incidence and Mortality of Cancer, Fact Sheet. 2012.
Available online: http://globocan.iarc.fr/Pages/fact_sheets_population.aspx (accessed on 11 July 2017).
Broza, Y.Y.; Kremer, R.; Tisch, U.; Gevorkyan, A.; Shiban, A.; Best, L.A. A nanomaterial-based breath test for
short-term follow-up after lung tumor resection. Nanomed. Nanotechnol. Biol. Med. 2013, 9, 15–21. [CrossRef]
[PubMed]
Broza, Y.Y.; Zuri, L.; Haick, H. Combined volatolomics for monitoring of human body chemistry. Sci. Rep.
2014, 4, 1–6. [CrossRef] [PubMed]
Hashim, D.; Boffetta, P. Occupational and environmental exposures and cancers in developing countries.
Ann. Glob. Health 2014, 80, 393–411. [CrossRef] [PubMed]
Mount, G.H.; Rumburg, B.; Havig, J.; Lamb, B.; Westberg, H.; Yonge, D. Measurement of atmospheric
ammonia at a dairy using differential optical absorption spectroscopy in the mid-ultraviolet. Atmos. Environ.
2002, 36, 1799–1810. [CrossRef]
Pijolat, C.; Pupier, C.; Sauvan, M.; Tournier, G.; Lalauze, R. Gas detection for automotive pollution control.
Sens. Actuators B Chem. 1999, 59, 195–202. [CrossRef]
Kohl, D. Function and applications of gas sensors. J. Phys. D Appl. Phys. 2001, 34, R125. [CrossRef]
Chemosensors 2017, 5, 22
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
13 of 16
Ampuero, S.; Bosset, J.O. The electronic nose applied to dairy products: A review. Sens. Actuators B Chem.
2003, 94, 1–12. [CrossRef]
Greenstein, G.R. The Merck Index: An Encyclopedia of Chemicals, Drugs, and Biologicals (14th edition).
Ref. Rev. 2006, 21, 40.
Zakrzewska, K. Mixed oxides as gas sensors. Thin Solid Films 2001, 391, 229–238. [CrossRef]
Wojkiewicz, J.L.; Bliznyuk, V.N.; Carquigny, S.; Elkamchi, N.; Redon, N.; Lasri, T. Nanostructured
polyaniline-based composites for ppb range ammonia sensing. Sens. Actuators B Chem. 2011, 160, 1394–1403.
[CrossRef]
Peeters, R.; Berden, G.; Apituley, A.; Meijer, G. Open-path trace gas detection of ammonia based on
cavity-enhanced absorption spectroscopy. Appl. Phys. B 2000, 71, 231–236. [CrossRef]
Tiggelaar, R.M.; Veenstra, T.T.; Sanders, R.G.P.; Berenschot, E.; Gardeniers, H.; Elwenspoek, M. Analysis
systems for the detection of ammonia based on micromachined components modular hybrid versus
monolithic integrated approach. Sens. Actuators B Chem. 2003, 92, 25–36. [CrossRef]
Winquist, F.; Spetz, A.; Lundström, I.; Danielsson, B. Determination of ammonia in air and aqueous samples
with a gas-sensitive semiconductor capacitor. Anal. Chim. Acta 1984, 164, 127–138. [CrossRef]
Xu, C.N.; Miura, N.; Ishida, Y.; Matsuda, K.; Yamazoe, N. Selective detection of NH3 over NO in combustion
exhausts by using Au and MoO3 doubly promoted WO3 element. Sens. Actuators B Chem. 2000, 65, 163–165.
[CrossRef]
Cantalini, C.; Valentini, L.; Armentano, I.; Lozzi, L.; Kenny, J.M.; Santucci, S. Sensitivity to NO2 and
cross-sensitivity analysis to NH3 , ethanol and humidity of carbon nanotubes thin film prepared by PECVD.
Sens. Actuators B Chem. 2003, 95, 195–202. [CrossRef]
Arab, M.; Berger, F.; Picaud, F.; Ramseyer, C.; Glory, J.; Mayne-L’Hermite, M. Direct growth of the
multi-walled carbon nanotubes as a tool to detect ammonia at room temperature. Chem. Phys. Lett.
2006, 433, 175–181. [CrossRef]
Battie, Y.; Ducloux, O.; Thobois, P.; Dorval, N.; Lauret, J.S.; Attal-Trétout, B. Gas sensors based on thick films
of semi-conducting single walled carbon nanotubes. Carbon N. Y. 2011, 49, 3544–3552. [CrossRef]
Feller, J.F.; Gatt, N.; Kumar, B.; Castro, M. Selectivity of chemoresistive sensors made of chemically
functionalized carbon nanotube random networks for volatile organic compounds (VOC). ChemoSensors
2014, 2, 26–40. [CrossRef]
Kumar, B.; Castro, M.; Feller, J.F. Quantum resistive vapour sensors made of polymer coated carbon
nanotubes random networks for biomarkers detection. Chem. Sens. 2013, 3, 1–7.
Li, Y.; Wang, H.; Yang, M. n-Type gas sensing characteristics of chemically modified multi-walled carbon
nanotubes and PMMA composite. Sens. Actuators B Chem. 2007, 121, 496–500. [CrossRef]
Kumar, B.; Feller, J.F.; Castro, M.; Lu, J. Conductive bio-Polymer nano-Composites (CPC): Chitosan-carbon
nanotube transducers assembled via spray layer-by-layer for volatile organic compound sensing. Talanta
2010, 81, 908–915. [CrossRef] [PubMed]
Kumar, B.; Castro, M.; Feller, J.F. Poly(lactic acid)–multiwall carbon nanotube conductive biopolymer
nanocomposite vapour sensors. Sens. Actuators B Chem. 2012, 161, 621–628. [CrossRef]
Huyen, D.N.; Tung, N.T.; Vinh, T.D.; Thien, N.D. Synergistic effects in the gas sensitivity of
polypyrrole/single wall carbon nanotube composites. Sensors 2012, 12, 7965–7974. [CrossRef] [PubMed]
Van Hieu, N.; Dung, N.Q.; Tam, P.D.; Trung, T.; Chien, N.D. Thin film polypyrrole/SWCNTs
nanocomposites-based NH3 sensor operated at room temperature. Sens. Actuators B Chem. 2009, 140,
500–507. [CrossRef]
Setka, M.; Drbohlavova, J.; Hubalek, J. Nanostructured polypyrrole-based ammonia and volatile organic
compound sensors. Sensors 2017, 17, 562. [CrossRef] [PubMed]
Sharma, S.; Hussain, S.; Singh, S.; Islam, S.S. MWCNT-conducting polymer composite based ammonia
gas sensors: A new approach for complete recovery process. Sens. Actuators B Chem. 2014, 194, 213–219.
[CrossRef]
Huotari, J.; Lappalainen, J.; Eriksson, J.; Bjorklund, R.; Heinonen, E.; Miinalainen, I. Synthesis of
nanostructured solid-state phases of V7 O16 and V2 O5 compounds for ppb-level detection of ammonia.
J. Alloy. Compd. 2016, 675, 433–440. [CrossRef]
Chemosensors 2017, 5, 22
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
14 of 16
Krivetskiy, V.; Malkov, I.; Garshev, A.; Mordvinova, N.; Lebedev, O.I.; Dolenko, S. Chemically modified
nanocrystalline SnO2 -based materials for nitrogen-containing gases detection using gas sensor array.
J. Alloy. Compd. 2017, 691, 514–523. [CrossRef]
Dalavi, D.S.; Harale, N.S.; Mulla, I.S.; Rao, V.K.; Patil, V.B.; Kim, I.Y. Nanoporous network of nickel oxide for
ammonia gas detection. Mater. Lett. 2015, 146, 103–107. [CrossRef]
Kwon, O.S.; Hong, J.Y.; Park, S.J.; Jang, Y.; Jang, J. Resistive gas sensors based on precisely size-controlled
polypyrrole nanoparticles: Effects of particle size and deposition method. J. Phys. Chem. C 2010, 114,
18874–18879. [CrossRef]
Xiang, C.; Jiang, D.; Zou, Y.; Chu, H.; Qiu, S.; Zhang, H. Ammonia sensor based on polypyrrole-graphene
nanocomposite decorated with titania nanoparticles. Ceram. Int. 2015, 41, 6432–6438. [CrossRef]
Korpan, Y.I.; Gonchar, M.V.; Sibirny, A.A.; Martelet, C.; El’skaya, A.V.; Gibson, T.D. Development of highly
selective and stable potentiometric sensors for formaldehyde determination. Biosens. Bioelectron. 2000, 15,
77–83. [CrossRef]
Kawamura, K.; Kerman, K.; Fujihara, M.; Nagatani, N.; Hashiba, T.; Tamiya, E. Development of a novel
hand-held formaldehyde gas sensor for the rapid detection of sick building syndrome. Sens. Actuators
B Chem. 2005, 105, 495–501. [CrossRef]
Que, Z.; Furuno, T.; Katoh, S.; Nishino, Y. Evaluation of three test methods in determination of formaldehyde
emission from particleboard bonded with different mole ratio in the urea-formaldehyde resin. Build. Environ.
2007, 42, 1242–1249. [CrossRef]
An, J.Y.; Kim, S.; Kim, H.J.; Seo, J. Emission behavior of formaldehyde and TVOC from engineered flooring
in under heating and air circulation systems. Build. Environ. 2010, 45, 1826–1833. [CrossRef]
World Health Organization (WHO). Air Quality Guidelines for Europe. Available online: http://www.euro.
who.int/en/publications/abstracts/air-quality-guidelines-for-europe (accessed on 11 July 2017).
U.S. Dept Health & Human Services. Occupational Safety and Health Guideline for Formaldehyde Potential Human
Carcinogen; NIOSH: Washington, DC, USA, 1988.
Suzuki, Y.; Nakano, N.; Suzuki, K. Portable sick house syndrome gas monitoring system based on novel
colorimetric reagents for the highly selective and sensitive detection of formaldehyde. Environ. Sci. Technol.
2003, 37, 5695–5700. [CrossRef] [PubMed]
Achmann, S.; Hermann, M.; Hilbrig, F.; Jérôme, V.; Hämmerle, M.; Freitag, R. Direct detection of
formaldehyde in air by a novel NAD+- and glutathione-independent formaldehyde dehydrogenase-based
biosensor. Talanta 2008, 75, 786–791. [CrossRef] [PubMed]
Lv, P.; Tang, Z.A.; Yu, J.; Zhang, F.T.; Wei, G.F.; Huang, Z.X. Study on a micro-gas sensor with SnO2 -NiO
sensitive film for indoor formaldehyde detection. Sens. Actuators B Chem. 2008, 132, 74–80. [CrossRef]
Li, X.; Liu, J.; Guo, H.; Zhou, X.; Wang, C.; Sun, P. Au@In2 O3 core-shell composites: A metal-semiconductor
heterostructure for gas sensing applications. RSC Adv. 2015, 5, 545–551. [CrossRef]
Barsan, N.; Koziej, D.; Weimar, U. Metal oxide-based gas sensor research: How to? Sens. Actuators B Chem.
2007, 121, 18–35. [CrossRef]
Zhang, D.; Liu, J.; Jiang, C.; Liu, A.; Xia, B. Quantitative detection of formaldehyde and ammonia gas via
metal oxide-modified graphene-based sensor array combining with neural network model. Sens. Actuators
B Chem. 2017, 240, 55–65. [CrossRef]
Zhang, D.; Jiang, C.; Li, P.; Sun, Y. Layer-by-layer self-assembly of Co3 O4 nanorod-decorated MoS2
nanosheet-based nanocomposite toward high-performance ammonia detection. ACS Appl. Mater. Interfaces
2017, 9, 6462–6471. [CrossRef] [PubMed]
Fang, F.; Bai, L.; Sun, H.; Kuang, Y.; Sun, X.; Shi, T. Hierarchically porous indium oxide nanolamellas with
ten-parts-per-billion-level formaldehyde-sensing performance. Sens. Actuators B Chem. 2015, 206, 714–720.
[CrossRef]
Park, H.J.; Choi, N.J.; Kang, H.; Jung, M.Y.; Park, J.W.; Park, K.H. A ppb-level formaldehyde gas sensor based
on CuO nanocubes prepared using a polyol process. Sens. Actuators B Chem. 2014, 203, 282–288. [CrossRef]
Güntner, A.T.; Koren, V.; Chikkadi, K.; Righettoni, M.; Pratsinis, S.E. E-Nose sensing of low-ppb
formaldehyde in gas mixtures at high relative humidity for breath screening of lung cancer? ACS Sens. 2016,
1, 528–535. [CrossRef]
Chemosensors 2017, 5, 22
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
61.
62.
63.
64.
65.
66.
67.
68.
69.
70.
71.
72.
15 of 16
Nag, S.; Castro, M.; Choudhary, V.; Feller, J.F. Sulfonated poly(ether ether ketone) [SPEEK] nanocomposites
based on hybrid nanocarbons for the detection and discrimination of some lung cancer VOC biomarkers.
J. Mater. Chem. B Biol. Med. 2017, 5, 348–359. [CrossRef]
Nag, S.; Sachan, A.; Castro, M.; Choudhary, V.; Feller, J.F. Spray layer-by-layer assembly of POSS
functionalized CNT quantum chemo-resistive sensors with tuneable selectivity and ppm resolution to
VOC biomarkers. Sens. Actuators B Chem. 2016, 222, 362–373. [CrossRef]
Feller, J.F. Des Composites Polymères Conducteurs Aux éco-Composites Polymères, 2nd ed.; Universitaires
Européennes (EUE): Sarrebruck, Germany, 2003.
Zhang, W.; Müller, A.H.E. Architecture, self-assembly and properties of well-defined hybrid polymers based
on polyhedral oligomeric silsequioxane (POSS). Prog. Polym. Sci. 2013, 38, 1121–1162. [CrossRef]
Pielichowski, K.; Njuguna, J.; Janowski, B.; Pielichowski, J. Polyhedral oligomeric silsesquioxanes
(POSS)-containing nanohybrid polymers. In Supramolecular Polymers Polymeric Betains Oligomers; Springer:
Berlin/Heidelberg, Germany, 2006; pp. 225–296.
Gnanasekaran, D.; Madhavpan, K.; Reddy, R.S.R. Developments of polyhedral oligomeric silsesquioxanes
(POSS), POSS nanocomposites and their applications: A review. J. Sci. Ind. Res. 2009, 68, 437–464. (In India)
Franchini, E.; Galy, J.; Gérard, J.-F.; Tabuani, D.; Medici, A. Influence of POSS structure on the fire retardant
properties of epoxy hybrid networks. Polym. Degrad. Stab. 2009, 94, 1728–1736. [CrossRef]
Raftopoulos, K.N.; Pielichowski, K. Segmental dynamics in hybrid polymer/POSS nanomaterials.
Prog. Polym. Sci. 2016, 52, 136–187. [CrossRef]
Tanaka, K.; Adachi, S.; Chujo, Y. Structure-property relationship of octa-substituted POSS in thermal and
mechanical reinforcements of conventional polymers. J. Polym. Sci. Part A Polym. Chem. 2009, 47, 5690–5697.
[CrossRef]
Li, S.; Simon, G.P.; Matisons, J.G. Morphology of blends containing high concentrations of POSS nanoparticles
in different polymer matrices. Polym. Eng. Sci. 2010, 50, 991–999. [CrossRef]
Thakur, V.K.; Ding, G.; Ma, J.; Lee, P.S.; Lu, X. Hybrid Materials and Polymer Electrolytes for Electrochromic
Device Applications. Adv. Mater. 2012, 24, 4071–4096. [CrossRef] [PubMed]
Maitra, P.; Wunder, S.L. POSS based electrolytes for rechargeable lithium batteries. Electrochem. Solide-State
Lett. 2004, 7, A88. [CrossRef]
Ghanbari, H.; Cousins, B.G.; Seifalian, A.M. A Nanocage for Nanomedicine: Polyhedral Oligomeric
Silsesquioxane (POSS). Macromol. Rapid Commun. 2011, 32, 1032–1046. [CrossRef] [PubMed]
Wu, J.; Mather, P.T. POSS Polymers: Physical Properties and Biomaterials Applications. Polym. Rev. 2009, 49,
25–63. [CrossRef]
Fina, A.; Monticelli, O.; Camino, G. POSS-based hybrids by melt/reactive blending. J. Mater. Chem. 2010, 20,
9297–9305. [CrossRef]
Nguyen, T.-P. Polymer-based nanocomposites for organic optoelectronic devices. A review. Surf. Coat. Technol.
2011, 206, 742–752. [CrossRef]
Wu, J.; Haddad, T.S.; Mather, P.T. Vertex Group Effects in Entangled Polystyrene—Polyhedral
Oligosilsesquioxane (POSS) Copolymers. Macromolecules 2009, 42, 1142–1152. [CrossRef]
Wu, J.; Haddad, T.S.; Kim, G.-M.; Mather, P.T. Rheological Behavior of Entangled Polystyrene—Polyhedral
Oligosilsesquioxane (POSS) Copolymers. Macromolecules 2007, 40, 544–554. [CrossRef]
Gordon, M.; Taylor, J.S. Ideal copolymers and the second-order transitions of synthetic rubbers. i.
non-crystalline copolymers. J. Appl. Chem. 1952, 2, 493–500. [CrossRef]
Ma, X.-M.; Wang, B.; Zhang, M.-X.; Min, F.-F.; He, J. Synthesis and Thermal Characterizations of Pmma
Nanocomposite Functionalized by Polyhedral Oligomeric Silsesquioxane, Phosphorus. Sulfur. Silicon
Relat. Elem. 2013, 188, 1819–1826. [CrossRef]
Kotal, A.; Si, S.; Paira, T.K.; Mandal, T.K. Synthesis of semitelechelic POSS-polymethacrylate hybrids by
thiol-mediated controlled radical polymerization with unusual thermal behaviors. J. Polym. Sci. Part A
Polym. Chem. 2008, 46, 1111–1123. [CrossRef]
Gao, T.; Woodka, M.D.; Brunschwig, B.S.; Lewis, N.S. Chemiresistors for array-based vapor sensing using
composites of carbon black with low volatility organic molecules. Chem. Mater. 2006, 18, 5193–5202.
[CrossRef]
Deng, H.; Skipa, T.; Zhang, R.; Lellinger, D.; Bilotti, E.; Alig, I. Effect of melting and crystallization on the
conductive network in conductive polymer composites. Polymer 2009, 50, 3747–3754. [CrossRef]
Chemosensors 2017, 5, 22
73.
74.
75.
76.
77.
78.
79.
80.
81.
16 of 16
Feller, J.F.; Grohens, Y. Electrical response of Poly(styrene)/carbon black conductive polymer composites
(CPC) to methanol, toluene, chloroform and styrene vapors as a function of filler nature and matrix tacticity.
Synth. Met. 2005, 154, 193–196. [CrossRef]
Tripathi, S.N.; Singh, S.; Malik, R.S.; Choudhary, V. Effect of multiwalled carbon nanotubes on the properties
of poly(methyl methacrylate) in PMMA/CNT nanocomposites. Macromol. Symp. 2014, 341, 75–89. [CrossRef]
Amr, I.T.; Al-Amer, A.; Al-Harthi, S.T.P.M.; Girei, S.A.; Sougrat, R. Effect of acid treated carbon nanotubes on
mechanical, rheological and thermal properties of polystyrene nanocomposites. Compos. B Eng. 2011, 42,
1554–1561. [CrossRef]
Kumar, B.; Castro, M.; Feller, J.F. Tailoring the chemo-resistive response of self-assembled
polysaccharide-CNT sensors by chain conformation at tunnel junctions. Carbon N. Y. 2012, 50, 3627–3634.
[CrossRef]
Kumar, B.; Castro, M.; Feller, J.F. Controlled conductive junction gap for chitosan-carbon nanotube quantum
resistive vapour sensors. J. Mater. Chem. 2012, 22, 10656–10664. [CrossRef]
Feller, J.F.; Castro, M.; Kumar, B. Polymer-carbon nanotube conductive nanocomposites for sensing. In
Polymer Carbon Nanotube Composites: Preparation, Properties and Applications, 1st ed.; McNally, T., Pötschke, P.,
Eds.; Woodhead Publishing Limited: Cambridge, UK, 2011; pp. 760–803.
Bouvrée, A.; Feller, J.F.; Castro, M.; Grohens, Y.; Rinaudo, M. Conductive Polymer nano-bioComposites
(CPC): Chitosan-carbon nanoparticle a good candidate to design polar vapour sensors. Sens. Actuators
B Chem. 2009, 138, 138–147. [CrossRef]
Chatterjee, S.; Castro, M.; Feller, J.F. An e-nose made of carbon nanotube based quantum resistive sensors for
the detection of eighteen polar/nonpolar VOC biomarkers of lung cancer. J. Mater. Chem. B 2013, 1, 4563.
[CrossRef]
Joulazadeh, M.; Navarchian, A.H. Ammonia detection of one-dimensional nano-structured
polypyrrole/metal oxide nanocomposites sensors. Synth. Met. 2015, 210 Part B, 404–411. [CrossRef]
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