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Nanocomposites with Biodegradable PolymersSynthesis, Properties, and Future Perspectives$

Vikas Mittal

Print publication date: 2011

Print ISBN-13: 9780199581924

Published to Oxford Scholarship Online: September 2011

DOI: 10.1093/acprof:oso/9780199581924.001.0001

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Conductive biopolymer nanocomposites for sensors

Conductive biopolymer nanocomposites for sensors

(p.368) 15 Conductive biopolymer nanocomposites for sensors
Nanocomposites with Biodegradable Polymers

Jean-François Feller

Bijandra Kumar

Mickaë Castro

Oxford University Press

Abstract and Keywords

This chapter describes and updates the development, design, and potential of resistive sensors from smart nanocomposites. Conductive bioPolymer nanoComposites (CPC) are obtained by structuring a network of conductive nanofillers (carbon or metallic) into an insulating biopolymer matrice. Additionally from being helpful to reduce sensors carbon footprint, the use of biopolymers, also offers new functionalities like a higher affinity for polar target molecules, biocompatibility, biodegradability etc. Special attention is paid to the construction of 3D conducting architectures, to their characterisation with the different tools available to image their morphologies at the different scales of observation, and to the innovative electrical properties responsible for their smart behaviours.

Keywords:   conductive biopolymers nanocomposite, transducers, vapour sensing, liquid sensing, temperature sensing, strain sensing

15.1 Introduction

Since the pioneering work of Persaud & Dodd (1980) has opened up new prospects to instrumentally reproduce human olfaction system by means of an array of partially selective transducers and pattern recognition techniques, many researchers (Persaud et al. 1996, Philip et al. 2003) have carried on this powerful approach consisting in mimicking living beings described by Buck (2005), able to recognise many thousands of different odours and tastes by means of a much smaller number of receptors.

In fact, the so-called electronic noses (Gardner and Barlett 1992) and electronic tongues (Pioggia et al. 2007a, b) meet the request for low cost, high throughput, portable, highly sensitive and versatile device capable to replace expensive instruments or inefficient human olfaction in assessing products quality, pollutants toxicity or presence of dangerous compounds like explosives or warfare chemicals. Most often, these receptors are made of polymer nanocomposites able to transduce the quantity and nature of molecules existing in their surrounding, into interpretable electrical signal. Nevertheless most of these transducers have a poor selectivity towards polar vapours due to the hydrophobicity of their polymer matrix. Therefore biopolymers have gained interest and credibility over the past decade to substitute petroleum feedstock like traditional plastics, by biobased compounds such as poly(ester), poly(amide), or poly(saccharide). Moreover their hydrophilic nature makes them compatible with many biomolecules such as glucose. Associating nanofiller with such (p.369) biomatrix will produce a new category of material (biopolymer nanocomposite) with original functionalities for new applications. Particularly the dispersion of conductive nanofillers into biopolymers matrices will lead to smart materials for sensors (able to convert external solicitations like strain, temperature, chemicals, into interpretable electrical signal) and actuators (able to convert current into mechanical work). The formulation, processing and use of such conductive biopolymer nanocomposites for sensing will be reviewed in the following pages.

15.2 Conductive biopolymer nanocomposite (CPC) transducer development

15.2.1 Choice and association of materials for conductive biopolymer nanocomposite development

For smart applications, the choice of the different components of the CPC formulation is crucial. This key step consists in selecting and assembling the different pieces of what will become a sensitive material. Although there is a wide range of materials potentially available, hundreds of different matrices and tens of conducting fillers, there will only be at the end only some suitable combinations. The association of conductive nanofillers with the suitable biopolymer matrix for the different sensing targets will be introduced, together with main characteristics of these materials. Moreover special features obtained via biomacromolecules functionalized on nanoparticles surface will be depicted. Finally the optimal level of conductivity for sensing with CPC will be discussed.  Conductive nanofillers

One-dimensional nanostructures have demonstrated great interest for ultrasensitive, miniaturized molecule sensors in many applications (Xia et al. 2003). On the basis of their atomic composition these nanoparticles can be divided into three categories. Inorganic conductive nanofillers

The detailed features of inorganic nanoparticles can not be summarized in a short paragraph as there are large extent classes of inorganic nanoparticles such as metal, metal oxides, etc. Inorganic nanoparticles provide three important functions for electroanalysis. These include roughening of conductive sensing interface, catalytic properties of nanoparticles permitting the amplified electrochemical detection of molecules and conductive properties of nanoparticles at nanoscale. Besides that, they can also be easily modified using a wide range of biomolecules and chemical ligands (Darder et al. 2007). Many kinds of nanofillers such as Au (Xu et al. 2006), Ag (Ren et al. 2005) or Pt, Si (Li et al. 2004) and their compounds have been used to construct sensors via either immobilization within the bio matrix or surface functionalization of nanoparticles. Following the improvement of nanoscience and nanotechnology, the challenge in application of nanomaterials is to control not only the particle sizes but also the particle shapes and morphologies. Shape control is an alternative tool for adjusting the optical, electronic or catalytic properties of nanomaterials (Perez-Juste et al. 2005). The shape of inorganic nanomaterial can be tailored by tuning synthesis conditions (Hahm and Lieber 2004, Park et al. 2006, Feng et al. 2009).

(p.370) Organic conductive nanofillers

Carbon is the foremost component of organic nanofillers and often termed as carbon nanomaterials, pertaining various structures such as nanoparticles, nanotubes, nanoplatelets and nanorods. Carbon nanoparticles also called carbon blacks (CNP) are a semi-cristalline form of carbon that have a high level of structuring and cover a wide range of applications. However, concerning conductive bio-nanocomposite, the high loading necessary to achieve electrical percolation threshold value, technical hitches for surface functionalization, make it limited for applications in advanced nanotechnology. Carbon nanotubes (CNT), firstly discovered by Radushkevich and Lukyanovich (1952), represent the most significant group of nanoscale materials in present time (Figure 15.1). They possess one of the simplest chemical compositions and atomic bonding configurations and among all nanomaterials referred to, they show the most extreme diversity and richness of structures and associated properties. Particularly richness of surface chemistry makes CNT an admirable element among existing nanoparticles for sensing (Abraham et al. 2004, Kauffman and Star 2008). Moreover, the impressive role of CNT in modern electroanalytical chemistry is supported by relevant characteristics from an analytical point of view such as electrocatalytic ability, large surface area, high electrical conductivity, anti-fouling capability and easy decoration by nanoparticles as well as by polymers (Ajayan 1999, Agüi et al. 2008, Capek 2009). Other kinds of carbon nanofillers can be synthesized like carbon nanofibres (CNF), which are cylindrical nanostructures composed of graphene layers arranged as stacked cones, cups, or plates, currently produced industrially by the continuous floating catalyst method. Their mechanical strength and electric properties are similar to those of carbon nanotubes while their size, shape and graphite ordering can be well controlled (Weisenberger et al. 2009). Fullerene or buckminsterfullerene, discovered by Kroto et al. (1985), has a ball shape having π electron-rich surface. Depending on carbon atom number fullerene can be C60, C70, etc. C60 is the most substantial among fullerenes. The average diameter of C60 is approximately less than 1 nm. Recent methods of synthesizing via plasma, supercritical CO2 or laser ablation technique (Chattopadhyay and Gupta 2000, Wang et al. 2001, Dubrovsky et al. 2004) with controlled extent at large scale have facilitated the utilization of this smart organic nanomaterial (Capaccio et al. 2005).

Conductive biopolymer nanocomposites for sensors

Fig. 15.1 AFM picture of CNT dispersed into a chitosan matrix.

Reproduced from Kumar (2010).

(p.371)  Insulating biopolymer matrix

A biopolymer can be either biosourced and/or biodegradable depending on applications but in most cases it is assumed to be hydrophilic, thus soluble in water and compatible with biomolecules. It is also this functionality that makes insulating biopolymers interesting to fabricate sensors or nanodevices for sensing. Mainly poly(saccharide) like cellulose, starch or chitosan, poly(ester) such as poly(caprolactone) (PCL), poly(lactide acid) (PLA) or poly(hydroxyalkanoate) (PHA), poly(amide) PA11, PA12 and biomolecules like proteins, deoxyribonucleic acids (DNA) or enzymes are commercially available. Chitosan, one widely used polymer is obtained after partial deacetylation of chitin, poly(β-(1®4)-N-acetyl-D-glucosamine), synthesized in a number of living organisms (crustacean shells, cuticles of insects and in the cell walls of fungi). Chitosan is a random copolymer of β-(1®4)-N-acetyl-D-glucosamine and (1®4)-D-glucosamine characterized by a degree of acetylation (DA). Recently, much attention has been paid to chitosan due to its excellent properties such as biocompatibility, biodegradability, low toxicity, good film forming character. Cellulose comprises long chains of β-(1®4)-linked glucosyl residues, which in higher plants are assembled into long microfibrils with diameters typically in the range 2 to 10 nm (Brown et al. 1996). PLA is widely used in biotechnology applications due to its biocompatibility and biodegradability. It is synthesized from the fermentation of poly(saccharides) by polycondensation of lactic acids and ring-opening polymerization of lactides. End hydroxyl group facilitate PLA utilization for nanoparticles grafting by covalent bonding (Södergard and Stolt 2003). Cyclodextrins (CD) are crystalline, cyclic oligosaccharides derived from starch. CD possesses a very specific bowl (without bottom)-like architectural shape. The ring-like structure of the CD creates a hydrophobic interior and hydrophilic exterior and has been employed extensively in host–guest chemical systems. Their internal cavity is able to accommodate one or two guest molecules depending on size and chemical structure CD used as matrix in conductive polymer composites would provide specific information depending on guest molecule/interactions. These characteristics make cyclodextrin an ultimate candidate for smart material (Chambers et al. 2003, Wenz 1994, Stott et al. 2005). Additionally DNA, glucose oxidase, and enzymes are broadly studied in biosensors design and can be an interesting source of inspiration. Both complex structure and functionality of DNA make it a fascinating molecule. It is constituted of two macromolecules entwined in a double helix. The backbone of each chain is made up of alternate sugar and phosphate groups joined together in regular 3'-5' phosphate diester linkages. The DNA double helix is stabilized by hydrogen bonds between the bases attached to the two strands. Two kinds of complementary bases can interact, purines (adenine and guanine) and pyrimidines (thymine and cytosine). Adenine is linked to thymine by two hydrogen bonds whereas guanine is associated to cytosine by three hydrogen bonds. In this structure, nucleotides are inside while phosphate and sugar groups are outside the helical chains (Watson and Crick 1953). Glucose oxidase (GOx) is one of the most used biopolymer due to its catalytic characteristics in biosensing technology. GOx is a dimeric protein, containing one tightly bound flavin adenine dinucleotide (FAD) per monomer as cofactor. Highly purified GOx can be commercially produced from fungi (Swoboda and Massey 1965, Witt et al. 2000).

15.2.2  Conductive biopolymer nanocomposite architecture design

The first attempts to develop sensors for biomolecules in the 1950s are due to Clark (1956), and later on some of the successive steps of development have been described by Magner (1998). But the new generation of materials called nanocomposites were only discovered in the 1980s, (p.372) whereas their conductive homologues appear in the 1990s (although rubber tyre could be considered as conductive nanocomposites to some extend). The actual formulation of CPC starts after 2000 with Liu et al. (2005a) and it has only been a couple of years since they were first used as resistive transducers (Kobashi et al. 2008) and even less that researchers have been interested in the control of their conductive network structure (Bouvrée et al. 2009). This section will present some recent strategies for controlling conductive architectures and follow the evolution of their multiscale organization as a function of processes.  Covalent functionalization of the filler

In order to meet the specifications of particular applications (e.i. biocompatibility of nanotube-based biosensors or implantable devices), the chemical modification of CNT is essential. Covalent coupling of biomaterials to CNT is highly important. The application of site-selective reactions to nonaligned and aligned CNT has opened a rich field of CNT chemistry (Guo and wang 1998, Bahr and Tour 2002). Carboxylic functional groups have been introduced at the ends of CNT and have been partially generated as defect sites on sidewalls of CNT by their oxidation. These acid-functionalized SWNT were converted into acid chlorides by derivatization with SOCl2 and then used to covalently bind biomaterials such as sugar moieties, oligonucleotides, peptide nucleic acids and proteins (Pompeo and Resasco 2002, Pantarotto 2003). For example the bovine serum albumin (BSA) protein was covalently attached to CNT via diimide-activated amidation under ambient conditions. Interestingly the overwhelming majority of the protein species in the nanotube-BSA conjugates remain bioactive (Huang et al. 2002, Baker et al. 2003, Jiang et al. 2004). The edges of CNT are more reactive than their sidewalls, thus allowing the attachment of functional primary amine group of DNA to nanotube end carboxylic group (Dwyer et al. 2002). In situ polymerization of polymers such as PLA and PCL is also a fascinating technique for functionalized CNT enclosing of covalent interactions (Castro 2009). Biopolymers not only could be functionalized on a CNT surface but also these macromolecules can be covalently affixed to other nanomaterial (Capaccio et al. 2005). For example, fullerenes were covalently functionalized by protein via chemical linking reaction (Nednoor et al. 2004).  Non-covalent functionalization of the filler

There are numerous reports on non-covalent grafting of proteins, DNA and enzyme on conductive nanofiller surface, which open the wide creative area of bio-nanocomposite for sensing (Shim et al. 2002). Biomacromolecules such as chitosan, proteins and DNA have specific molecular structures that facilitate the adsorption on nanoparticles surface. Non-covalent modifications of nanoparticles not only improve the solubility of nanomaterials in green media but they also constitute a non-destructive process. The main advantage of non-destructive process is to preserve the native structure of nanofillers and so on primary electrical and mechanical properties (Lopez-Bezanilla et al. 2009). A non-destructive surface decoration of CNT with chitosan biopolymer via a controlled surface deposition and cross-linking process was achieved utilizing the emulsifying capacity of chitosan, the completely different water-solubility of chitosan in acidic and basic solutions and the cross-linking reaction among chitosan polymers (Guo et al. 1998, Liu et al. 2005a). Poly(saccharides) such as starch (Star et al. 2002) and peptide (Zorbas 2004) were found to wrap themselves in helical fashion around CNT. Nucleic acids such as DNA and RNA can also be attached on CNT. Non-specific interactions of DNA with CNT are (p.373)

Conductive biopolymer nanocomposites for sensors

Fig. 15.2 Schematic drawing of the pyrenecyclodextrin-decorated SWNT hybrids and how they interact with guest molecules when they are being sensed in an FET device.

Reproduced from Zhao (2008).

due to stacking of nucleic acid bases onto nanotube surfaces. GOx has been immobilized on a CNT surface via non-covalent functionalization by Besteman et al. (2003). On the other hand, Zhao et al. (2008) designed and fabricated a field effect transistor (FET) device by decorating SWNT with pyrene-modified β-cyclodextrin, which behaved as chemical sensors in aqueous solution, detecting organic molecules as a consequence of their molecular recognition by the pyrenecyclodextrin derivative (Figure 15.2). Star et al. (2001) achieved good dispersion of CNT via polymer wrapping on their surface. Certain spacing between the functional groups upon immobilization on surfaces of CNT has been achieved using dendrons unique anisotropic shape and an orthogonal functional group at their apex by Woo et al. (2007). Finally biomacromolecules were encapsulated in CNT to improve their solubility in aqueous media, which could be an attractive architecture for sensing applications (Kim, et al. 2003).  Physical deposition

The simplest procedure to fabricate sensor is deposition of biomacromolecules on or within the supported film. This is an easy handling and scientifically straightforward approach to fabricate sensor. Physical deposition of biomolecules can be done by electro-deposition (Loh et al. 2004. Lim et al. 2005). Dip coating or casting are also used for physical depositions (Ruslin 1998). The poor interaction between host and guest molecules is one strong drawback of this method. Anyhow it is a widely accepted and used technique (Tang et al. 2004, Salimi et al. 2004, Liu et al. 2005b).  Dip layer-by-layer assembly (dLbL)

The formal layer-by-layer (LbL) assembly process has been developed by (Decher and Hong 1991, Decher 1997) who used hydrogen bonding and other types of van der Waals attractions to build (p.374) assemblies by alternately dipping a substrate into aqueous solutions containing charged ingredients either positive like (poly(ethyleneimine), PEI) or negative such as (poly(acrylic acid), PAA). Each positive and negative pair (bilayer) deposited is typically 1–100 nm thick and their association allows building films highly tailorable by altering pH, ionic strength or chemistry. Further on LbL has been found a powerful protocol to develop progressively aggregated nanocomposite structures: with gold nanoparticles to obtain self-assembled hollow spheres (Schneider and Decher 2004), clay nanoplatelets mimicking natural biocomposites like nacre to reach high mechanical properties (Tang et al. 2003), (Podsiadlo et al. 2007) or O2 barrier capability (Jang et al. 2008) and carbon nanotubes leading to films with tensile strength close to hard ceramics (Mamedov et al. 2002, Olek et al. 2004). Moreover this technique has been adopted to build films with antibacterial activity due to progressive release of cetyltrimethylammonium bromide DTAB (Dvoracek et al. 2009) and to architect specific sensors for nanobiotechnology. In this later case the LbL film will provide not only a suitable microenvironment to retain biomolecular activity but also a chemically and mechanically robust system for sensing. (Zhou et al. 2002) found that electroactivity of haemoglobin (Hb) when associated to clay in LbL films was similar to Hb in solution and that it could reduce trichloroacetic acid, oxygen, and hydrogen peroxide. Liu and Lin (2006) developed a flow injection amperometric glucose biosensor based on electrostatic self-assembly of GOx on a CNT-modified glassy carbon transducer with poly(diallyldimethylammonium) chloride (PDDA). They also found that the unique sandwich-like layer structure (PDDA/GOx/PDDA/CNT) formed by self-assembling provided a favourable microenvironment to keep the bioactivity of GOx and to prevent enzyme molecule leakage. Zhao et al. (2006) also showed that assembling electrodeposited LbL chitosan/Fe3O4 nanoparticles into thin films preserved quite well both Fe3O4 superparamagnetic properties and high catalytic activity of adsorbed Hb. Nevertheless, Bouvrée (2007) demonstrated that the use of polycations such as poly(allylamine hydrochloride) (PAH) to self-assemble conducting fillers like gold nanoparticles (AuNP) stabilized by citrates anions, led on the one hand to well-assembled nanostructures visible on Figure 15.3a by atomic force microscopy (AFM), but which were on the other hand non conductive due to insulation of nanoparticles coating. This drawback was overcome by spraying layer by layer (SLbL) a suspension of citrate functionalized AuNP and atactic poly(styrene) aPS that made it possible to structure in 3D microporous films for volatile organic vapour (VOC) sensing. Moreover, the highly porous microstructure resulting from SLbL (Figure 15.3b) leads to high specific surface that increases interactions with target molecules and thus the sensors sensitivity.  Spray layer by layer assembly

Spray layer by layer assembly is an especially suitable method for 3D chemo-resistive transducers preparation from CPC solutions. After dispersion of nanofillers within the polymer solution by sonication under stirring, SLbL can be done in two main steps. In the first, CPC micro-droplets are projected directly on the electrodes thanks to an air stream under pressure, by successive there and back translations at controlled speed. Then in a second step the micro-beads progressively solidify and weld together due to solvent evaporation. This process finally leads to a double percolated conductive network hierarchically structured as evidenced by Lu et al. (2009b). Since its first development to structure CNP networks within synthetic poly(styrene) matrices of different crystallinities (Feller and Grohens 2005), SLbL has been used to fabricate bio-nanocomposite (p.375)

Conductive biopolymer nanocomposites for sensorsConductive biopolymer nanocomposites for sensors

Fig. 15.3 (a) AFM image of non conductive spin coated LbL film of 20 bilayers of PAH-AuNP/Cit-AuNP and (b) SEM image of conductive spayed LbL films of aPS/Cit-AuNP.

Reproduced from Bouvrée (2007).

transducers for polar vapour detection with both CNP (Bouvrée et al. 2009) and CNT conducting architecture (Kumar et al. 2010) within a chitosan matrix. Conveniently the measurement of electrical resistance during sample fabrication allows tailoring sensor characteristics like sensitivity. Some variants of the technique have been proposed by Merrill and Sun (2009), who introduced the rotation of the substrate during spraying to decrease surface roughness and the use of a masks to plot conductive tracks from carbon nanofibres with poly(diallyldimethylammonium chloride) assembly. This technique opens up the potential to expand existing LbL research from components development to 3D nanostructured devices like sensors of photovoltaic cells.

(p.376) 15.2.3  Conductive biopolymer nanocomposite transducer characterization

The recent developments in nanoscale science and technology have required instrumentation for observation and metrology, i.e. the architect must see and measure what he builds. Whatever the fabrication technique used, an important part of sensors performances will depend on the different morphologies and architectures at the different scales of observation from nano to macro. Nanoscale objects can be visualized by transmission electronic microscopy (TEM), high-resolution scanning electronic microscopy (SEM) and AFM. Each technique has its own merits and demerits.  Transmission and scanning electronic microscopy (TEM and SEM)

TEM is an enough precise technique to image the thin enclosed layers of CNT and D-spacing of grapheme sheets. Nevertheless TEM suffers from the complexity of samples preparation. Theoretically the thickness of specimen should be comparable to the mean free path of few nanometres of electrons travelling through the samples. But recent advanced modifications have made TEM a more effective technique for wide applications for nanoparticles and nanocomposites characterization. For example Utsunimiya and Ewing (2003) used new technologies like high-angle annular dark field scanning TEM, SEM-energy dispersive X-ray spectrometry and energy-filtered TEM to characterize nanoparticles present at very low amounts in the environment. Stankovich (2006) have been able to precisely image the organisation of graphene nanoplatelets in a poly(styrene) matrix by TEM and high-resolution SEM and also to determine if graphene-based sheets were individually dispersed by selected area electron diffraction (SAED). Du et al. (2007) have observed the biosynthesis of gold nanoparticles assisted by Escherichia coli DH5: to realize direct electrochemistry of haemoglobin. Nevertheless if TEM is very useful to investigate the morphology of nano objects and their eventual exfoliation it would require extensive imaging and statistical treatment to ensure a representative view of the material at a larger scale. Moreover the preparation techniques are often heavy and may damage the sample.

Although SEM is not able to reach the same order of magnification as TEM even in its high-resolution version, it can be useful to image rough (Figure 15.3b) and bulk samples (Figure 15.12) or samples requiring an observation at several scales (Dikin et al. 2006, Stankovich et al. 2006). Zhang et al. (2006) and Chakoli et al. (2009) also used TEM and SEM to visualize CNT coating by PLA macromolecules.  Atomic force microscopy

Besides providing useful information, the electron beam used in a TEM or SEM can cause temporary or permanent change in the surface or bulk structure of a specimen due to high energy electron assault, heating, etc. (Egerton et al. 2004). Atomic force microscopy measurements by design mostly rely on interactions between atoms of the tip and the surface to analyse. Thus, no important damage of the specimen is expected during analysis which is one of the main advantages over electron beam based microscopy together with easy sample preparation and three-dimensional imaging (Figure 15.3a). More importantly, AFM can be run in different modes, i.e., conductive, tapping, contact modes that make it an essential tool for conductive nanobiocomposite investigation. The Normal mode provides information on dispersion of nanoparticles while the conductive mode offers the understanding of the structure of the real 3D conductive network (p.377) within conductive polymer composites as shown in Kumar et al. (2008). Guiseppi-Elie et al. (2002) showed by AFM that flavin adenine dinucleotide (FAD), the redox active prosthetic group of flavoenzymes that catalyses important biological redox reactions and the flavoenzyme GOx, could spontaneously adsorb onto CNT bundles.  Optical microscopy (OM)

Optical microscopy cannot provide the network architecture at the nano scale but it can visualise the dispersion state of micro-aggregates within bulk specimen. The main advantage of this technique is that it is very informative about the real structure of layers that outward appears during sample formation process. For example, micro-bead-like structure of polycarbonate CNT composite has been revealed under optical microscopy (Lu et al. 2009) that could not be visualized with AFM or TEM spectroscopy due to their experimental limitations. The extended OM is called near-field OM. The main advantage of near-field OM, besides the improved lateral optical resolution, is the simultaneously acquired topography. Near-field scanning OM (NSOM) has proved its interest for determining the quality of membrane preparations, especially from photosynthetic organisms, due to its ability to determine the surface topography of the sample with resolution better than optical techniques and to work with wet samples and even under water (Shinkarev et al. 1999). Schreiber et al. (2004) report the first observation of the Raman spectrum of isolated single-walled CNT (SWCNT) by near-field optical Raman spectroscopy. Raman spectra have been obtained by using an aperture-based NSOM coupled to a T64000 Jobin Yvon spectrometer in collection mode. Liu and Grunlan (2007) used bright-field OM to image the co-dispersion of SWNT and clay in epoxy matrices. Bellayer et al. (2007) have been able to analyse surface roughness of PAN-MA/SWCNT, PAN-MA/CB and SWCNT films with laser scanning confocal microscope (LSCM) and Lu et al. (2009a) were able to evidence the influence of processing conditions on CNT-filled phase orientation in co-continuous CPC.  Rheological analysis

Rotating shearing rheological measurements, although of different nature than the previous techniques based on direct observation, can reveal some interesting characteristics of polymer composites related to particle–particle interactions, network structures, states of dispersion of particles and degrees of interaction between particles and polymer matrix through yield stress, solid-like plateau behaviour and strain hardening. Furthermore, rheology has been used to investigate both morphology dynamics of fillers and macromolecules in the liquid state before the composite fabrication. Depending on the composition, the temperature and the processing conditions, the addition of a small amount of nanofiller is found to increase significantly composites viscosity. Conductive filler network structuring is usually characterized by a plateau in the storage modulus (G') and a sharp increase in complex viscosity at low frequency, revealing a solid-like behaviour as seen in Figures 15.4a and 15.4b, respectively. Lee et al. (2009) have compared such behaviour for poly(carbonate) filled with different kind of carbon fillers, CNP, CNF and CNT. The latter was found to be much more effective than the two others to create networks for 2 wt% of fillers. Mitchell et al. (2002) used this technique to evidence the influence of CNT functionalization; a plateau was formed at lower amount of CNT when they were functionalized. Kim et al. (2006) classified the dispersion of different CNT in epoxy resin as a function of their surface treatment, amine, acid or plasma. Seo and Park (2004) saw less interactions of CNT with poly(propylene) matrix. (p.378)

Conductive biopolymer nanocomposites for sensorsConductive biopolymer nanocomposites for sensors

Fig. 15.4 Rheological curves of poly(ε-caprolactone) filled with 0 to 5% w/w of CNT at 90°C: (a) G' = f (frequency) and (b) η* = f (frequency).

15.2.4  Instrumentation and tests

Different ways and experimental devices used to determine sensing properties of CPC will be summarized in the following sections. Resistimetry

Electrical properties measurement is of course a fundamental step in the characterization of CPC. In fact, it is not as simple as it seems to determine the resistance of a CPC sample that depends on many parameters. Obviously conduction in these heterogeneous materials is closely related to filler concentration (Feller and Linossier 2003, Feller and Petitjean 2003), but also fillers dispersion and confinement (Feller 2004), intrinsic electrical properties and aspect ratio of fillers (Feller et al. 2002a). A tiny change in any of these parameters will definitely (p.379) induce large modifications of the conductive network structure and morphology resulting in important variations of resistance and sensing behaviour of CPC. Moreover measurements of samples resistance are influenced by pressure, temperature and atmosphere, as CPC are sensitive to all of them. Another important point is the connection of the transducer to measurement electrodes. Independently from adhesion problems between the two different materials that can be solved by improving physical and/or chemical interactions between them, electrons circulation from a good conductor (the electrode) to a semi-conductor (the CPC transducer) may not be favoured. Thus, a third material with intermediate conducting properties like silver paste may be used to decrease the conductivity gradient. Then technically the current input and output will depend on transducers' shape, like extruded film, spun fibre, compressed tape and sprayed layer. Ideally a four-probe device should be used to get rid of wire resistance, but in practice when a sample's resistivity is larger than a few kΩ.cm, two points measurement gives good results particularly if interdigitated electrodes are used. Once all the undesirable perturbations are under control, smart resistive CPC transducers allow for an easy monitoring of any isolated environmental variation. For example, when conductive polymer composites are exposed to organic vapours, the change in resistance caused by the diffusion of these molecules through the CPC will cause tunnelling current due to swelling of polymer chains or adsorption at filler/filler junctions, providing specific information (Bouvrée et al. 2009). The accuracy of resistivity measurements has been widely adopted for the design of vapour, liquid, strain and temperature sensors.  Voltammetry

For transducers immersed in a liquid medium, the key parameter for resistance variation will be the variation of ions number, charge or velocity due to chemical reactions. In this case the term ‘voltammetry’ is more suitable as it covers a range of techniques involving the application of a linearly varying potential between a working electrode and a reference electrode in an electrochemical cell. The theory of voltammetry has been extended to include electron transfer reactions. The method provides an extremely rapid and simple way for evaluating electrode kinetics. In linear sweep voltammetry (LSV), the potential range is scanned starting at the Initial potential and ending at the final potential. Cyclic voltammetry (CV) is an extension of LSV in that the direction of the potential scan is reversed at the end of the first scan (the first switching potential), and the potential range is scanned again in the reverse direction. The traced graph is called voltammogram. Differential pulse voltammetry (DPV) can be considered as a derivative of LSV with a series of regular voltage pulses superimposed on the potential linear sweep or stair steps (Romani 2000). More advanced stripping voltammetry techniques can measure accurately low concentrations of metal ions at the ppb levels with rapid analysis times and low-cost instrumentation (Dai et al. 2004). These techniques have been widely developed for biosensing such as glucose and DNA.

15.2.5  Principle of conductive biopolymer nanocomposite resistive sensors

Since pioneer works have early evidenced the potential of CPC for temperature (Meyer 1973, 1974), strain (Kost et al. 1983, Kost et al. 1984) and chemical sensing (Hatfield et al. 1994, Freund et al. 1995, Lonergan et al. 1996), CPC transducers have found an increasing number of (p.380) applications. Although the sensing principle is almost the same whatever the variable to measure, i.e., an increase in interfiller gap, there are important differences to consider depending on the nature of the solicitation that the CPC must transduce.  Positive/negative temperature coefficient effect

Temperature sensing with CPC results from their original thermo-electrical behaviour upon heating/cooling. Typically, the important interfiller gap increase resulting from polymer phase volume expansion during melting, will result in a resistivity rise of several decades. As shown in Figure 15.5, the switching temperature (T s) can be determined at the beginning of the resistivity jump, known as the positive temperature coefficient (PTC) effect. T s coincides with the beginning of polymer crystalline phase melting (Feller et al. 2002b) or amorphous phase softening (Pillin et al. 2002), although in the later case lower PTC amplitude is obtained. The versatility of CPC temperature sensors comes from the easy tailoring of T s and sensitivity range by simply changing the polymer melting temperature and crystallinity. Nevertheless, at the single polymer phase CPC, an undesirable phenomenon can take place upon heating, called the negative temperature coefficient (NTC). Just after the end of crystals melting a reaggregation of fillers in the melt can take place, resulting in a sudden decrease in resistivity and most of the time leading to sample destruction. To overcome this drawback either slight reticulation of the matrix or structuring of co-continuous polymer phases is used. In the latter case only the conductive phase will be liquid, whereas the structuring phase will be solid. However, this dramatic NTC effect upon heating must not be mismatched with the reversible NTC effect observed upon cooling that corresponds to normal and reversible crystallization of the polymer as it will be seen later in the section dedicated to thermo-resistive sensors.

Conductive biopolymer nanocomposites for sensors

Fig. 15.5 PTC effect in different PCL-CNT based CPC.

Reproduced from Lu et al. (2009a).

(p.381)  Positive/negative strain coefficient effect

Strain sensing is based on the ability of CPC to transduce any elongation at the macroscale modifying interfiller gap at the nanoscale and resulting in the generation of tunnelling conduction. This contribution, being exponentially dependent on inter-CNT gap Z according to Equation 15.1, makes CPC very sensitive to elongation:

ρ c = a e bZ ,

ρ is the resistivity, a and b are positive constants and Z is the gap between two vicinal CNT (Gau et al. 2009).

Possible drawbacks could be nonlinearity of signals due to the fact that the polymer matrix has been submitted to out of linear range solicitations, which can lead to permanent drift of the sensor. Thus depending on the amplitude of the deformation to follow, it is necessary to adapt polymer matrix characteristics so that it will be always more elastic than the sample to monitor.  Positive/negative vapour coefficient effect

By analogy with PTC effect the sharp resistivity jump observed upon exposition of CPC to organic vapour molecules (cf. Figure 15.10) is called the positive vapour coefficient (PVC) and its counterpart the negative vapour coefficient (NVC). Conveniently unlike for temperature sensing both PVC and NVC can be used for vapour sensing (Lu et al. 2010). Much information can be derived from the analysis of such signals as their amplitude depends on both the amount and chemical nature of the analytes to detect. CPC sensors can be selective and quantitative after proper calibration of the device. Interestingly their selectivity can be tailored by changing the chemical nature of the polymer matrix, i.e. specific interactions between polymer chains and organic molecules. Additionally, dielectric permittivity and even the size of the solvent molecules can be used to change sensor selectivity (Lu et al. 2009b). However, due to the hybrid and hierarchical nature of CPC, it is still complex to fully understand sensing phenomena

Conductive biopolymer nanocomposites for sensors

Fig. 15.6 Correlation between electrical response and molecular interactions.

(p.382) responsible for their transducing. Generally it is assumed that during their diffusion through the composite, vapour molecules can specifically disconnect nanofiller–nanofiller junctions by increasing the gap between nanofillers directly adsorbing on carbon or indirectly relaxing macromolecules in the vicinity of the junction. Consequently quantum-tunnelling conduction, very sensitive to interparticle gap as expressed in Eq. (15.1), will develop to the detriment of ohmic conduction, resulting in large resistance increase even for a small amount of solvent molecules. The amplitude of this phenomenon is classically evaluated by following the evolution of Ar the relative resistance defined by Equation 15.2
A r = R v - R i n i t R i n i t ,

where R init is the initial resistance in pure nitrogen and R v the resistance in the presence of vapour

CPC can be used to design sensors for chemicals in either gas or liquid state but obviously design specifications will differ depending on the target. Typically the dynamics of CPC sensors is studied by submitting them to periodic flows of solvent vapours and nitrogen gas as shown in Figure 15.10. This pattern will also evidence sorption, desorption and eventually accumulation of organic molecules inside the material if the base line does not come back to its initial position.

15.2.6  Properties of conductive biopolymer nanocomposite resistive transducers

Different kinds of properties can be expected from CPC as a result of their ability to transduce any external solicitation into electrical signal. Their original chemoelectrical properties lead straight to the design of vapour transducers, the combination of which will produce an electronic nose (e-nose) by analogy with the mammalian sense of olfaction from which it was inspired. Thanks to Persaud et al. (1996) and Lewis (2004) who mimicked the human nose by analysing the responses of many different sensors in parallel with pattern recognition algorithms, it has been possible to identify organic vapours and even odours with e-noses. Nevertheless they did not use much at that time the hydrophilic character of most biopolymers which provides high sensitivity to polar molecules like water and even methanol (Bouvrée et al. 2009, Kumar et al. 2010) nor did they formulate transducers with new architectures of conducting nanofillers that are now available. In fact both sensitivity and selectivity can be tailored by changing the conductive network structure. This can be done by adjusting content (vicinity to percolation threshold) and shape factor of nanofillers in CPC, polarity, tacticity and crystallinity of macromolecules from the matrix (Feller et al. 2005), as all these parameters will contribute to structuring the percolated network and consequently the chemoelectrical behaviour of the sensor. Additionally the selectivity of the nanofillers network itself (especially when it constitutes the major phase) must be considered and controlled. For example, depending on surface physicochemical and electronic characteristics of the nanofiller, p- or n-type doped CNT will lead to different sensing patterns although the CPC composition will be the same. Similar facts must be taken into account to fabricate liquid sensors as the amount of solvent molecules is much higher in this case and possible structural changes are expected to take place. In contrast, other requirements are needed for strain sensors related to reversibility among elongation to maintain the transducer in a linear range. However, there are a few examples of CPC used as strain sensors (Liu et al. 2007), although this field of research might be promising as there is a real need for measuring strain induced in (p.383) biomechanical structures. Finally temperature sensors made of biopolymer nanocomposite are almost non-existent in the open literature as only one example was found (Lu et al. 2009a). In the following we will investigate successively resistive sensors by subcategories termed as vapour sensor, liquid sensor, temperature sensor and strain sensor.  Chemo-resistive biopolymer nanocomposite vapour transducers

As noted previously there are only a few studies reporting on smart applications of CPC for vapour sensing (Stai et al. 2005, Bouvrée et al. 2009, Kumar et al. 2010). This highlights the fact that biopolymers are still not really implemented in all scientific domains although they represent a wide potential of applications and also will help to reduce the carbon imprint of many devices. Recently, our group has investigated the vapour sensing characteristics of biopolymer and CNP-based conductive polymer bio-nanocomposite. These sensors have shown very selective response towards different vapours.  Chemical selectivity of responses

There exists unfortunately no universal rule or theory that can guide to fabricate specific selective and highly sensitive sensors as ideally expected. Selectivity and sensitivity of vapour sensors is correlated to many factors, among which are solubility parameters, physical aspects of the matrix like its molecular weight, crystallinity, transition temperatures and moduli, chemical nature, specific geometry of the nanostructure, nanofiller loading and shape factor, vapour pressure of solvent and molecular size of vapour molecule. The key parameters are discussed below.

Selectivity due to nonspecific solvent–matrix interactions

Physicochemical interactions between solvent molecules and matrix macromolecules are often dominating selectivity and sensitivity of the CPC vapour sensor. The Flory–Huggins intermolecular interaction parameter well predicts the sensitivity of CPC transducers to volatile organic compounds (Kumar et al. 2010) and organic liquids (Kobashi et al. 2009):

χ 12 = V R T · ( δ Tpol - δ Tsol ) 2 ,
  • V (cm3 · mol–1): the molar volume of the solvent

  • R = 8.314 J.mol–1 · K–1: the perfect gases constant

  • δT: the total energy from bonds between molecules derived from δ T 2 = δ d 2 + δ P 2 + δ H 2 · (J1/2 · cm–3/2)

  • δd: solubility parameter from dispersion bonds between molecules (J1/2 · cm–3/2)

  • •δp: solubility parameter from polar bonds between molecules (J1/2 · cm–3/2)

  • •δH: solubility parameter from hydrogen bonds between molecules (J1/2 · cm–3/2)

There is even a simple law relating the electrical response of CPC A r and the inverse of their interaction parameter with the solvent vapour 1/χ12 according to Equation 15.4. This relation has been validated for PCL, PC and chitosan in a range of values of χ12 between 0.15 and 15 J1/2 · cm–3/2 (Figure 15.6). Nevertheless some solvents, having a too high affinity for the polymer, may lead to (p.384) important swelling and even degradation of the polymer matrix, which is the limit of use of the following equation:

A r = a e b χ 12

where a and b are constants.

In most cases it is possible to make satisfying predictions of sensors response to organic solvent vapours. Such considerations are illustrated in Figure 15.7 where the chitosan-based transducer shows the highest response towards water vapour, the PCL-based transducer the highest response towards toluene vapours and the PLA matrix will be better for methanol vapour sensing.

Concerning nanofiller nature and structure, compared to more classical CNP or metal nanoparticles (MNP), CNT due to their exceptional shape factor allow reaching the same level of electrical and mechanical properties, but for ten times less amount. Additionally A r will also vary with transducer's thickness, ambient temperature and initial resistance, parameters that will not be studied here but which must be controlled.

Selectivity due to molecular recognition

The use of a polymer matrix with chemical functions able to have specific interactions with solvent molecules like DNA is an efficient tool to tailor CPC transducer selectivity. As shown recently by Stai et al. (2005; Figure 15.8) DNA-decorated CNT lead to a versatile class of nanoscale chemical sensors. In this case a single-stranded DNA (ss-DNA) is used as chemical recognition site and SWCNT field effect transistors (swNT-FET) as electronic read-out component. Functionalization of a single CNT by DNA having different sequences and consequently different chemical properties (sequence 1: 5'GAG TCT GTG GAG GAG GTA GTC 3' and sequence 2: 5' CTT CTG TCT TGA TGT TTG TCA AAC 3') tuned the electrical response of CNT from p type to n type. The tuned networks have different response towards studied solvent vapours: methanol, propionic acid, trimethylamine (TMA), dinitrotoluene (DNT), and dimethyl methylphosphonate (DMMP, a simulant for the nerve agent sarin), with rapid response and recovery times on the scale of seconds. This remarkable set of attributes makes sensors based on ss-DNA decorated nanotubes very promising for ‘electronic nose’ and ‘electronic tongue’.

Conductive biopolymer nanocomposites for sensors

Fig. 15.7 Selectivity caused of polymer nature [PLA-1w%CNT, PCL-1w%CNT, Chit-2v%CNT] towards different vapour nature.

Conductive biopolymer nanocomposites for sensorsConductive biopolymer nanocomposites for sensors

Fig. 15.8 (a) Scheme of SwCN-FET coated with ss-DNA. (b) Response of sensor upon exposure to propionic acid (up) and methanol (down).

Reproduced from Staii et al. (2005).

Selectivity due to polymer structure

Selectivity can also result from specific molecular architecture like that of cyclodextrins that can host guest molecules depending on their size. Zhao et al. (2008) grafted cyclodextrins by pyrene functions to non-covalently stack them onto a CNT surface for selective molecules detection according to Figure 15.2. This device has been used as a tuneable photosensor of fluorescent adamantyl-modified Ru complex (ADA-Ru). When the light is on the transfer curve of the pyrenecyclodextrin-SWNT/FET device shifts toward a negative gate voltage by about 1.6 V and its sheet resistance increases quickly, indicating a charge-transfer process from the pyrenecyclodextrins to the SWNT. More indirectly Szymanska et al. (2001) prepared a smell sensor from the modification of a lipid bilayer by saturation with fullerene C60, which possesses electron mediator properties and facilitates a redox reaction occurring at the border of the lipid membrane and metal surface. Proportionality between vapour fraction and responses

The vapour-sensing behaviour of transducer is highly influenced by number of vapour molecule in its surrounding. Higher vapour molecule number enhances the magnitude of relative (p.386) amplitude corresponding to their nature. The Langmuir-Henry-Clustering (LHC) model could explain such phenomena. This model, derived from classical sorption models, describes to a certain extent which diffusion regime takes place in the CPC transducer: simple adsorption, diffusion or clustering, corresponding, respectively, to the three terms of

A r = b L · ( f ' ' - f ) · f ( 1 + b L · f ) + k H · f + ( f - f ' ) · f n ' ,

where b L is the Langmuir affinity constant, f: is the vapour fraction over which Langmuir's diffusion is replaced by Henry's diffusion, f is the solvent fraction, k H is Henry's solubility coefficient and n: the number of vapour molecules associated in clusters.

On the one hand this model makes it possible to identify which kind of diffusion mode is more suitable for sensing, whereas on the other, it makes it possible to determine organic molecule concentration in atmosphere, from relative amplitude measurement. A good example is given in Figure 15.9. where Chit-CNT curves seem to be homothetic for the different vapours and never cross, i.e., their proportionality is the same for all vapour content. Only the curve of CNP-filled chitosan (Chit-CNP) has a different shape: no Langmuir contribution is observed at low vapour concentrations (it can be fitted with only a HC model) and its amplitude is always lower than that of Chit-CNT for the same vapour (water). This result is consistent with the fact that weaker density of junctions is expected in a CNT network than in a CNP network. Enhancement of responses

Enhancing amplitude responses remains an important issue for detecting analytes having weak interactions with the polymer matrix or for detecting lower amounts of molecules (under ppm level). We recently reported results on chemoelectrical properties of poly(ε-caprolactone)-grafted carbon nanotubes prepared by in situ polymerization and further on SLbL (Castro 2009). This study reveals that partial coating of nanotubes by PCL chains grafted at the surface can improve CNT dispersion, resulting in enhanced CPC responses to water, methanol, toluene, tetrahydrofuran and chloroform vapours (Figure 15.10). Interestingly this improvement is

Conductive biopolymer nanocomposites for sensors

Fig. 15.9 Influence of vapour concentration and LHC model fit curve.

Reproduced from Kumar et al. (2010).

(p.387) sensitive not only when modified CNT are sprayed just after the synthesis but also once they have been dispersed by melt processing in a commercial PCL matrix. Additionally such sensors exhibited typical PVC effect, followed by complete desorption for all studied vapours, thus highlighting the interest of grafting nanotubes to boost sensitivity of vapour sensors.

This concept has been validated with chitosan-CNT conductive nanocomposites using the random network formulation technique developed by Kumar (2010). It appears that wrapping CNT random network with chitosan chains by non-covalent bonding instead of dispersing CNT in bulk chitosan makes it possible to significantly enhance sensor response to organic vapours like water methanol and toluene. In the case of water vapour, for example, Figure 15.11 shows that Chit-CNT transducer response can be more than doubled, evidencing that the key point for sensitivity is the polymer nano-domain surrounding each carbon–carbon junction.

Conductive biopolymer nanocomposites for sensors

Fig. 15.10 Influence of CNT grafting by PCL on response to toluene vapour.

Reproduced from Castro et al. (2009).

Conductive biopolymer nanocomposites for sensors

Fig. 15.11 Influence of CNT modification towards different studied vapours. Kumar (2010).

(p.388)  Chemo-resistive biopolymer nanocomposite liquid transducers

Conductive biopolymer nanocomposites can also be used to sense liquids, as recently evidenced by Pioggia et al. (2007a, b), who developed e-tongues, including some biosourced transducers like PLA-CNP in combination with PVA-CNT. The e-tongue was tested with five compounds of very different chemical characteristics (glucose, sodium dehydrocholate, sodium chloride, citric acid and glutamic acid) able to elicit different kinds of gustative perceptions representing respectively the five classic tastes (bitter sweet, umami, salty, acid). Further on, Kobashi et al. (2008, 2009) have investigated liquid-sensing properties of melt-processed PLA-CNT CPC under the form of films or fibres plunged into a series of organic solvents: methanol, ethanol and chloroform dichloromethane, THF and toluene. CNT loading was found to significantly affect resistance changes, loadings closer to percolation threshold resulting in larger resistance changes but also with excessive signal noise. The solubility parameter δ of solvents and polymer matrices appeared to be a good indicator to predict the sensing properties of CPC to poor and good solvents.  Thermo-resistive biopolymer nanocomposite temperature transducers

Less attention has been paid towards thermo-resistive sensors based on conductive bio nanocomposites, as rare works are found on the subject (Lu et al. 2009a). Temperature sensing depends on different factors such as filler content, conductive filler size, morphology of blend, crystallinity of polymer and processing conditions (Feller and Linossier 2003, Feller and Petitjean 2003, Feller 2004). Weakly filled CPC (i.e. with a filler content close to the percolation threshold) will show a higher room temperature resistivity but a higher PTC intensity (I PTC), while strongly filled composites will behave the opposite according to the percolation theory. For equivalent chemical nature nanofillers with high aspect ratio will involve fewer filler/filler contacts and thus will give the same conductivity and switching effect for much fewer amounts. Conveniently as seen previously, the switching temperature Ts can be simply tailored by adjusting the melting temperature of the polymer matrix from conductive phase. In Lu 's et al. (2009) study (reported in Figure 15.12), T s has been chosen around 50°C, which corresponds to PCL melting temperature. Interestingly for applications, this temperature also corresponds to the admitted human pain threshold, thus making it possible to design devices able to warn persons (for example, firemen) before they are injured. Nevertheless, it is also clearly evident that over 60°C, in the absence of another polymer of a melting temperature higher than that of PCL, a NTC effect takes place that might damage the sensor. To prevent this drawback Lu et al. (2009a) used CPC with co-continuous morphologies obtained by blending PCL-CNT with two different external matrices, poly(amide 12) and poly(propylene), in order to secure transducers integrity (Figure 15.12). In addition this type of diphasic CPC has other benefits over monophasic ones such as a lower percolation threshold and higher mechanical properties. Usually such samples are prepared in two steps: in the first, conducting nanofillers are dispersed in the polymer phase, the melting temperature of which will determine T s, then in a second step this premix is blended once more with the second polymer in charge of the CPC structure. Recently the dispersion of thermally conducting microfillers (BN, AlN, Talc, Al2O3) in this second polymer phase made it possible to decrease the thermal gradient and consequently the inertia of the CPC towards temperature variations (Droval et al. 2008). This new hybrid system with dual network architecture is very original as it makes it possible to adjust almost independently thermal and electrical conductivity in the CPC. The demonstration of phase co-continuity (p.389) results obviously but radically from the observation of current circulation through the sample. It can be complemented by SEM observation of a cross section after extracting one phase (Figure 15.12) and weight loss monitoring during solvent extraction. However, some finer morphologies, which can be obtained when the conducting nanofiller is confined at the interphase between the two immiscible polymer phases, are more difficult to evidence directly. However, as shown by Feller (2004) one indirect proof has been successfully derived from the combination of enhancement in conductivity and decrease in I PTC. This is in fact what happens with PA12/PCL-CNT transducers in Figure 15.12. This phenomenon is less visible with PP/PCL-CNT, which must be the best compromise between safety (weak NTC effect) and sharpness of temperature detection (satisfying I PTC) although not completely biobased. Besides the ability to sense temperature variations, CPC can also be used to dissipate energy by Joule effect if AC supply is used and input voltage is raised to some tens of volts (Zribi et al. 2006).  Piezo-resistive biopolymer nanocomposite strain transducers

The development of a novel CPC strain sensor using poly(L-lactide) (PLLA) as a host polymer matrix and CNT as conducting filler has been reported by Liu et al. (2007). This material exhibits a gauge factor 30 times higher than classical metallic foils which is explained by the fact that PLLA matrix improves load transfer across the nanotubes by means of better interfacial bonding between polymer and carbon nanotubes filler, thus endowing the nanocomposites material with excellent piezo-resistive property. Experimental results using a fabricated CPC strain sensor demonstrate its linear response and high gauge factor. Actually as many biomedical applications require high sensitivity for measuring strain induced in biomechanical structures and due to biocompatibility and biodegradability of PLLA, such sensor appears really attractive for many biomedical and wearable applications. The next step would be to design an implantable sensor that can be used for remote strain measurement inside the human body. Zhang et al. (2007) have studied the origin of resistivity decrease upon stretching of a CNT/thermoplastic elastomer (TPE) CPC fabricated by solution process with low percolation threshold (p c = 0.35 wt%). Resistivity was found to vary exponentially with strain (deformation) and independently of

Conductive biopolymer nanocomposites for sensors

Fig. 15.12 SEM image of 50PA12/50 (PCL-3%CNT) co-continuous CPC.

Reproduced from Lu et al. (2009a).

(p.390) nanotube concentration. Moreover the temperature dependency of CPC resistivity was described by consideration of the gap-width modulation of tunnel fluctuation-induced conduction.

Moreover, CPC piezo-electrical properties can be used for actuating, when instead of measuring electrical response the reverse operation is done, i.e., a voltage is applied. Spinks et al. (2007) have fabricated fibres composed of chitosan, polyaniline (Pani) and SWCNT using a wet spinning method. They have demonstrated that it was possible to generate a composite structure that allows both pH switching and electrochemical actuation. The pH strains were on the order of 2% and the electrochemical strains up to 0.3%. Ozarkar et al. (2008) studied actuating properties of Chit-CNT electrospun fibres after functionalization of CNT by nitric acid to improve their dispersion. They recorded strain responses after submitting samples to voltage of 5 to 50 V. An optimum of properties was found for a CNT loading close to 0.1 wt%, whereas under that content fibres could not survive beyond three cycles of switching electric field and over they became too rigid and maximum strain rate decreased from 5 to 2%.min–1.

15.2.7  Principle of conductive biopolymer nanocomposite electrochemical biosensors

Contrary to resistive sensor there is an abundant literature on electrochemical biosensors. We must mention their principle as some of their components involve CPC but their systematic review is beyond the focus of this chapter. A biosensor is an integrated device capable of providing specific quantitative or semi-quantitative analytical information using a biological recognition element (biochemical receptor), which is retained in direct spatial contact with a transduction element (Zhu et al. 2002, Liu et al. 2005b). Conductive biopolymer nanocomposites are exploited in biosensors due to a combination of active biomaterial and conductive nanoparticles as electron transferring agents (Davis et al. 2003). Either conductive nanofillers are dispersed within the matrix or they are used as nano electrosupportive objects, preserving scrupulous architecture via grafting of biomacromolecules or as a co-immobilization matrix to incorporate active enzyme. Selectivity is brought by both the specificity of the enzymatic reaction towards the substrate and by the selectivity of the detector towards the physicochemical parameter. As shown in the following reactions glucose can be selectively recognized by catalytic oxidation-reduction reaction by glucose oxidase (GOD) enzyme:

glucose + GOD ( FAD ) gluconolactone + GOD ( FADH 2 )
GOD ( FADH 2 ) + O 2 GOD ( FAD ) + H 2 O 2
H 2 O 2 O 2 + 2 H + + 2e -

Although the reactions are all related to a glucose sensor, they can be widely applicable to an enzyme-related system, such as a sucrose sensor, a fructose sensor, an alcohol sensor, a lactic acid sensor, a cholesterol sensor or an amino acid sensor, via replacing the glucose oxidase with an enzyme, e.g. invertase, mutarotase, fructose dehydrogenase, alcohol oxidase, lactate oxidase, lactate dehydrogenase, cholesterol oxidase, amino acid oxidase or xanthine oxidase. The response of the biosensor is not linear in all the ranges of substrate concentration; it is necessary to define the range in which the concentration is determined with good precision. The range depends both on the detector type, particularly to its detection limit, and on the nature of the diffusion and reaction of the enzymatic system. Based on their operating principle, the (p.391) electrochemical biosensors can employ potentiometric, amperometric and impedimetric transducers, converting the chemical information into a measurable amperometric signal (Yoshioka et al. 1994, Pohanka and Skládal 2008).

15.2.8  Applications

Resistive sensors based on advanced nanobiotechnology are leading smart materials researchers in pursuit of multitalented roles for various applications. Many applications are already on the market, such as the monitoring of food or beverage quality, providing information on their acceptability as foodstuffs and possible spoilage because of bacteria and humidity or temperatures issues. In this case, the analysis of gas/vapour emissions caused by bio-reactions will make it possible to quantify and guarantee product quality. Thus, meat or seafood freshness, milk spoilage and fruit and vegetable quality monitoring appear as promising areas for CNT-based CPC sensors. CPC sensors can also be used to monitor pollutants (i.e. pesticides; Lin et al. 2003) or toxic substances level close to chemical processes (i.e. chlorinated, fluorinated), and eventually to detect explosive chemical leaks in pipes or containers used for industrial purpose (i.e. alcohols, alcanes). Moreover CPC sensors can find specific applications in defence, safety or security areas for detection of biological and chemical weapons, drugs or explosives (Yinon 2003). More recent research has been focused on medical applications for non-invasive diagnostics. The objective is to detect the disease in its early stage so that medical care can be quickly conducted. Implementation of data based on exhaled breath sensing (Peng et al. 2009) provides preliminary knowledge of lung cancer, as also confirmed by Sponring et al. (2009) with another technique, gas chromatography mass spectrometry. Other diseases, like childhood asthma, diabetes or kidney disease, lead to drastic changes in the composition of exhaled breath and could therefore be delectated. Moreover implantable strain sensors can be used for remote strain measurement inside the human body. The implementation of such sensors in biochips appears to be a very promising technology that will open new areas for advanced investigation (Liu et al. 2010).

15.3 Conclusion

Throughout this chapter we have reported on both the complexity and potential of a new generation of materials with unique structure, topology, morphology and sensing properties. Conductive biopolymer nanocomposites combine the exceptional performances of nanoparticles with the eco-friendly character of biopolymer matrices to generate a new species of smart material that can be considered suitable for a variety of interesting possibilities in sensor design. Their possible applications vary from biomedical to aerospace including environmental, health and security, promising these smart materials a bright future. Nevertheless more efforts are required for the implementation of CPC in microelectronic devices.

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