Holzforschung 2018; 72(3): 215–223
Justine M. Kalaw* and Fortunato B. Sevilla III Discrimination of wood species based on a carbon nanotube/polymer composite chemiresistor array https://doi.org/10.1515/hf-2017-0097 markers to this purpose. Though the methods of anatomi Received June 9, 2017; accepted September 15, 2017; previously pub- cal and histochemical analysis are well established (Wid lished online October 23, 2017 enhoeft and Regis 2005), these approaches fail in the case of closely related wood species, particularly if the anatom Abstract: Fast and efficient alternative methods for wood ical differences are not unambiguous. In such situations, species identification are needed to combat illegal logging differentiation is possible by means of chemotaxonomi and to control fair trade. One of the possibilities of rapid cal and genetic methods, which are especially useful in wood recognition is via chemiresistor gas sensor arrays the protection of endangered and similar wood species (“electronic nose”), the application of which is described (Cordeiro et al. 2012; Wu et al. 2017) and in the control of in the present paper. Carbon nanotube composites (CNTs) illegal logging of woods from protected areas (Hermanson of eight insulating polymers were prepared through solu and Wiedenhoeft 2011). DNA-based methods for wood tion processing and spin casting. The optimum amount identification have gained momentum in the past decade of CNTs in the composites was determined by resistance (Jiao et al. 2014, 2015; Sandak et al. 2015; Yu et al. 2016; measurement and the CNTs were characterized by scan Hung et al. 2017; Wu et al. 2017). Rapid methods have been ning electron microscopy. In the case of static headspace developed for the evaluation of the physical and chemi analysis, the sensor responses were reproducible and cal properties and the possible economic value of woods discernible for the wood species. This was demonstrated (Garneau et al. 2004; Zhao et al. 2014). However, wood based on five wood species (Pterocarpus indicus, Aca- species identification based on macroscopic features is cia auriculiformis, Gmelina arborea, Vitex parviflora and difficult in the industrial routine, when non experts in Diospyros philippinensis). Discrimination of the data was wood science have to take rapid decisions, and when the achieved through principal component analysis (PCA) wood is already converted to sawdust or to veneers. and hierarchical cluster analysis (HCA). PCA score plots Methods for chemotaxonomical wood identification and groupings in HCA dendrograms rendered possible are mostly based on the chemical analysis of extractives the discrimination of these wood species. The potential (Sandermann 1962) and spectroscopic techniques involv application of the sensor array approach for wood species ing diffuse reflectance infrared (IR) (Nuopponen et al. identification is high. 2006), fluorescence (Dickson et al. 2017), Raman scatter Keywords: carbon nanotube/polymer composite, chemire ing (Nguyen et al. 2017), near infrared (NIR) absorption sistor sensor array, chemometric discrimination, chemo (Nisgoski et al. 2016, 2017), and Fourier transform infrared taxonomy, electronic nose, wood species identification (FTIR) absorption (Rana et al. 2008; Emmanuel et al. 2015; Traoré et al. 2016; Wang et al. 2017). DNA analysis has also been employed (Yu et al. 2016). Most of these techniques are tedious and time consuming, and cannot be performed on Introduction the sampling site and require specially-trained personnel. Almost all conventional methods for wood analysis are As a rule, wood species identification is performed via applied on solid samples. A simple alternative approach macroscopic and microscopic methods, when the ana involves headspace analysis which measures the distinc tomical differences in the species in question offer typical tive volatile compounds (VOCs) emitted by wood species. The headspace of wood samples can be characterized by means of gas chromatography/mass spectroscopy (GC/MS) *Corresponding author: Justine M. Kalaw, Graduate School, (Rinne et al. 2002; Muller et al. 2006; Fedele et al. 2007). University of Santo Tomas, Espana Blvd., Manila 1015, Philippines, Headspace analysis can also be carried out through + Phone: (632) 2758677, e-mail: [email protected] “electronic nose” technologies that are based on conduct Fortunato B. Sevilla III: Graduate School, University of Santo Tomas, Espana Blvd., Manila 1015, Philippines; and Research Center for the ing polymers (Wilson et al. 2005; Cordeiro et al. 2012, 2016) Natural and Applied Sciences, University of Santo Tomas, Espana and graphite/polymer composites (Garneau et al. 2004). Blvd., Manila 1015, Philippines The described materials are efficient, but newer sensing 216 J.M. Kalaw and F.B. Sevilla III: Wood species identification based on chemiresistors materials, such as carbon nanotubes composites (CNT) are considered to be premium wood species due to their excellent could bring about more advantages. Composites of CNT with hardness and durability (Gazal et al. 2004; Bankoff 2007; Pobar 2013), while A. auriculiformis and G. arborea are commercial wood species polymers are innovative materials, which exhibit remark used for sawn timber and pulpwood (Sayre 2004; Hai et al. 2008). able electrical conductive behavior, extraordinary mechani Fresh stems of the five wood species were collected from healthy cal properties and good thermal stability (Moniruzzaman and mature trees on the grounds of the University of Santo Tomas, and Winey 2006). The CNT imparts to the composites high Manila (14° 36′ 33″ North and 120° 59′ 24″ East), and authenticated electrical and thermal conductivities and large surface areas by the UST Herbarium, Research Center for the Natural and Applied (Moniruzzaman and Winey 2006). The insulating polymers Sciences, University of Santo Tomas. The samples were chopped to small pieces, stored in air-tight plastic containers and frozen (<−10°C) induce chemical selectivity, because the polymer interacts in until use. Prior to each analysis, the wood samples were thawed and a selective manner with the vapor molecules to be analyzed. milled after debarking. Chemiresistors with CNT/polymer composites based on cellulose (Qi et al. 2015), polymethyl methacrylate Sensor fabrication: The MWCNT/polymer composites were prepared (Abraham et al. 2004), polyethyleneglycol (Niu et al. 2007), via solution blending technique (Moniruzzaman and Winey 2006). and polystyrene (Zhang et al. 2005), have been applied for The polymer (0.5 mg) was dissolved in 10 ml of tetrahydrofuran (THF, RCI Labscan), and varying amounts of MWCNT were dispersed in this the analysis of the chemicals toluene, methanol, water, solution under ultrasonication for 30 min. This mixture led to a CNT/ chloroform, isopropanol, tetrahydrofuran, dichlorometh polymer solution with an optimized concentration of 0.1 mg μl−1. PVP ane, ethanol, heptane and cyclohexane. is not soluble in THF, so dimethylformamide (DMF, J.T. Baker) was the The present study aimed at the development of an solvent. The CNT/polymer solution (2.5 μl) was deposited in the gap array of chemiresistor gas sensors based on CNT/polymer between the gold electrodes in a laboratory-constructed chemiresis composites for a rapid, non-destructive, and cost-effective tor by means of a spin-coating technique at 25°C (speed of 2000 rpm for 30 s). The chemiresistor consisted of two gold circular planar elec discrimination of wood species. Five Philippine wood trodes, formed by inserting gold wires (18 karat, Ø 0.05 cm, L 0.5 cm) species will be analyzed for the first time through head into a Teflon block (5.0 mm × 5.0 mm × 5.0 mm) with a gap of 200 ± 20 space analysis in combination with chemometric tech μm between the wires. niques, such as principal component analysis (PCA) and hierarchical cluster analysis (HCA). Sensor array instrumentation: The sensor array consisted of eight fabricated chemiresistors, each with a composite film bridging the two gold electrodes. The composites in the chemiresistors are listed in Table 1 together with other information. Figure 1 illustrates the sen Materials and methods sor array instrumentation. It is composed of a sample container with a sensing chamber, and a data acquisition system (Picolog 1216 data Materials: Multiwalled carbon nanotubes (MWCNT, purity >95%; logger, Pico Technology, Saint Neots, Cambridgeshire, UK) coupled to length 10–50 μm; diameter 10–30 nm) were obtained from Iljin a personal computer. The sensing chamber housed the sensor array, Nanotech, Seoul, South Korea and used as received without any fur with each sensor connected to a voltage divider circuit coupled to the ther purification. The polymers investigated were polyethylene gly data acquisition system, which also serves as the DC power source (2.5 col (PEG) (Ave. MW 3,350, Sigma Chemical Co., St. Louis, Missouri, V) and the electrical conductance measuring device. The voltage meas USA), polyvinyl chloride (PVC) (low MW 100 000, BDH Limited, urement circuit of the data acquisition system depicts each sensor as Poole, Dorset, England, UK), polyvinylpyrrolidone (PVP) (Ave. R1. Each sensor was coupled with a corresponding resistor (R2) that MW 10 000, Sigma-Aldrich, St. Louis, Missouri, USA), poly(methyl methacrylate) (PMMA) (commercial grade, Republic Chemical Industries, Quezon City, The Philippines), poly(ethylene-co-vinyl Table 1: Optimal composition of carbon nanotube (CNT)/polymer acetate) (PEVA) (Ave. MW 150 000, Aldrich Chemical Co., St. Louis, composites in the fabricated chemiresistor sensors. Missouri, USA), epoxy (commercial grade, Republic Chemical Industries, Quezon City, The Philippines), poly(styrene) (PS) (Ave. Sensor Film MW 280 000, Aldrich Chemical Co., St. Louis, Missouri, USA), and # Polymer CNT (%) resistance (Ω) polydimethyl siloxane (PDMS) (commercial grade, Republic Chemi cal Industries, Quezon City, The Philippines). 1 Polystyrene 15.0 1084 (±32) 2 Polyvinyl chloride 12.5 600 (±6) 3 Polyethylene glycol 12.5 247 (±1) Wood samples: Five important Philippine wood species were in focus: 4 Poly(ethylene-co-vinyl acetate) 15.0 2883 (±14) Pterocarpus indicus Willd. (narra), Gmelina arborea Roxb. (gmelina), 5 Poly(methyl methacrylate) 12.5 167 (±0) Vitex parviflora Juss. (molave), Diospyros philippinensis (Desr.) Gürke 6 Polydimethyl siloxane 12.5 274 (±15) (kamagong) and Acacia auriculiformis A. Cunn. (acacia). According 7 Polyvinylpyrrolidone 12.5 290 (±3) to the Red List of Threatened Species (IUCN 2015), D. philippinensis is 8 Epoxy 15.0 334 (±1) endangered, P. indicus and V. parviflora are vulnerable, while A. auricu- liformis is of least concern. Gmelina arborea has not been assessed for The average data of three measurements are presented including the IUCN Red List. Diospyros philippinensis, P. indicus and V. parviflora the SD in parenthesis. J.M. Kalaw and F.B. Sevilla III: Wood species identification based on chemiresistors 217
Sensor array
Computer Data acquisition system
Sample container/ sensing chamber
Small terminal boardPicoLog data logger
R1 Voltage C1 Channel 1 V R IN R ADC V measurement Channel 1 2 1 MΩ ADC
circuit GND GND
Figure 1: Schematic diagram of the chemiresistor array set-up with its components.
For the voltage measurement circuit (small terminal board): R1 = sensor and R2 = resistor. has a resistance close to that of the initial sensor. This ensures that the resistor will not affect the measured sensitivity of the sensor. Results and discussion
Sensor measurement: The measurement involved static headspace Electrical and morphological characteristics analysis of the wood samples, which were placed in the sensing chamber. The empty chamber was purged with N2 to remove any impurities adsorbed in the sensing materials and to stabilize the out The electrical resistance of the CNT/polymer composites put resistance baseline. For each headspace analysis cycle, the sen varied with the amount of CNT dispersed in the polymer sors were exposed to the headspace of the wood samples for 10 min matrix and the values decreased dramatically with increas and the resistance of the sensors was monitored. After the measure ing concentration of CNT up to 15%. Beyond this value, the ment, the empty sensing chamber was flushed with N gas again for 2 resistance change became smaller. Thus a 15% CNT con 10 min to restore the resistance baseline. Three cycles were done for each measurement on each wood sample. centration can be considered as an optimum. The relevant The relative response (R) of each sensor served for comparison data in this context are listed in Table 1, which can be inter of the response patterns as well as for the chemometric analysis: preted as a manifestation of the formation of a conductive network of the carbon nanotube filler in the non-conduc =− (1) R ()RRsample0/,R0 tive polymer matrix with 15% CNT content (Bauhofer and Kovacs 2009). The SEM micrographs in Figure 2a and b where R and R are the values of the resistance of the sensors 0 sample provide evidence for the formation of a conductive network before and after exposure to the headspace a wood sample, respec in the CNT/PS composites with 5 and 12.5% CNT. At lower tively. The R data of the array are essential due to the baseline resist ance differences of the polymer composites. The average R data for concentrations, the CNTs (black spots) were organized as the three cycles are presented. Four replicate measurements were separate aggregates in the polymer matrix (transparent). done on the wood samples. At higher concentration, the conductive CNT fillers in the composite are highly inter-connected. Morphological characterization: A scanning electron microscope (SEM, Hitachi TM3000) was available (100–3000× normal view) for the observation of the composite film surfaces. Chemiresistor response Chemometric analysis: PCA and HCA were performed based on the data by the XLSTAT software package (Addinsoft, New York, In preliminary experiments, the electrical resistance of USA). the CNT/polymer composites was found to be sensitive 218 J.M. Kalaw and F.B. Sevilla III: Wood species identification based on chemiresistors
Figure 2: SEM micrographs of (a) 5% CNT/PS and (b) 12.5% CNT/PS composites and (c) 12.5% CNT/PVC before exposure and (d) after expo- sure to wood headspace. At a low CNT concentration (a), separate aggregates are formed, while at a high concentration (b) a highly interconnected network is formed. Before exposure (c), small holes are present, but after exposure, the hole size increased.
N2 Sample Sample Sample 3500 N2 N 2 N2
3000 CNT/PVC
2500
2000 CNT/PEG
CNT/PEVA 1500 CNT/Epoxy Resistance (ohm) 1000 CNT/PDMS CNT/PMMA CNT/PVP 500 CNT/PS
0 0 600 1200 1800 2400 3000 3600 Time (s) Figure 3: Time profiles for the vapor sensing responses of the eight CNT/polymer composites to the headspace of wood A. auriculiformis. The polymers used are polystyrene (PS), polyvinylpyrrolidone (PVP), polymethyl methacrylate (PMMA), polydimethyl siloxane (PDMS), epoxy, polyethylene-co-vinyl acetate (PEVA), polyethylene glycol (PEG) and polyvinyl chloride (PVC). to the components of (VOCs), such as aromatic hydrocar decreased in the presence of wood. However, in case of bons, alcohols and ketones (Abraham et al. 2004; Zhang CNT/PEG and CNT/PVP based sensors, an elevated sensor et al. 2005; Niu et al. 2007; Qi et al. 2015). Figure 3 shows resistance was observed. This might be due to the high the response of the various sensors in the headspace of polarity of the polymer components. The sensor responses the A. auriculiformis. Similar behavior was seen with reached a steady state within 113–240 s and the responses other wood samples and the chemiresistors in the sensor exhibit a good reversibility and repeatability, with a mean array responded rapidly. The resistance of most sensors relative standard deviation (SD) of 6.9%. J.M. Kalaw and F.B. Sevilla III: Wood species identification based on chemiresistors 219
Figure 4: Radar plots of the response of the sensor array towards the wood samples. The polymers used in the sensors are polystyrene (PS), polyvinylpyrrolidone (PVP), polymethyl methacrylate (PMMA), polydimethyl siloxane (PDMS), epoxy, polyethylene-co-vinyl acetate (PEVA), polyethylene glycol (PEG), polyvinyl chloride (PVC).
The sensor responses are wood specific as illustrated surface, but after exposure to the headspace, the holes on the corresponding radar plots of the normalized sensor sizes increased, which causes a lessened interconnectiv responses in Figure 4. Distinctive patterns can be recog ity of the conducting filler and leads to a conductance nized for each sample, which have a high fingerprint decrement. ing potential for the headspace identification of wood species. Two possible mechanisms are conceivable. One could be the result from the electronic charge transfer Optimization of sensor response from adsorbed gas molecules to the CNTs in the polymer matrices (Zhang and Zhang 2009). Another one could also The factors sample mass, sample matrix and volume of the involve the reversible adsorption of gas molecules to the sample container affect the sensor array response towards polymer matrix through specific intermolecular interac the samples. With increasing sample masses the magni tions. The interactions cause the polymer composite to tude of the response was also increasing and the response swell or shrink, and consequently disrupt the conduc time was decreasing. This effect is certainly related to the tion network of the dispersed CNTs in the polymer matrix greater amount of the VOCs in the headspace in the case of (Zhang and Zhang 2009). It is likely that polar VOCs would higher sample amounts. It was observed that milled wood interact to a higher degree with highly polar polymers (MW) samples gave the same relative responses as chips such as PEG and PVP through dipole-dipole interaction or with a similar mass, but the response time was shorter. hydrogen-bonding, while low polarity and non-polar com This is probably related to the higher diffusion rate of pounds would interact with the non-polar/low-polarity VOCs in the case of MW, compared to unmilled chips. An polymers such as PS, PVC, PEVA, epoxy, PDMS and PMMA increase of the sample container volume caused a signifi through van der Waals or hydrophobic interactions. The cantly larger response time but did not affect the relative SEM micrographs in Figure 2c and d reveal a difference response of the sensor array. Clearly, the slower diffusion in the sensing surface with and without a sample in the of the VOCs in a larger volume towards the sensing surface headspace vessel. Initially, small holes are present on the is the reason for this observation. An optimum sensor 220 J.M. Kalaw and F.B. Sevilla III: Wood species identification based on chemiresistors
Figure 5: (a) Score plot of the principal components of the responses of the sensor array to the wood samples: P. indicus (N), G. arborea (G), V. parviflora (M), D. philippinensis (K) and A. auriculiformis (A). (b) Loading plot from the PCA of the headspace analysis of wood samples showing the pattern of the variables (sensor array). Data points in (a) clustered according to the wood species. Some of the sensors gave similar or related responses to the wood species. response can be obtained from 3.0 g of a MW sample in a content (MC) had a positive influence on VOCs, which 75-ml container. was observable from the very low responses of air-dried Agitation of the sample during measurement resulted samples compared to that of fresh ones. This effect was in a longer response time of the sensor array, which may also reported in an earlier paper (Wilson 2012), in which be due to convection drifts inside the sample container aged-dried wood samples had to be rehydrated through that is interfering with the diffusion movement of gas soaking in water to produce a sensor response. In view molecules. Application of heat had an adverse effect of these effects, the measurement of the sensor response on the response of the composites, therefore, measure was carried out on fresh MW samples without heating ments were done at room temperature. The moisture and agitation. J.M. Kalaw and F.B. Sevilla III: Wood species identification based on chemiresistors 221
Figure 7: Dendrogram from HCA calculations (Ward’s algorithm) of the sensor array responses to the wood samples: P. indicus Figure 6: Three-dimensional scatter plot (axes F1, F2 and F3: (N), G. arborea (G), V. parviflora (M), D. philippinensis (K) and 87.20%) of the principal components of the responses of the sensor A. auriculiformis (A). array to the wood samples: P. indicus (N), G. arborea (G), V. parvi- flora (M), D. philippinensis (K) and A. auriculiformis (A).
P. indicus and A. auriculiformis are well separated from each other. Principal component analysis (PCA)
PCA was applied for response quantification of the chemoresistive sensors. The resulting PCA scatter plot Hierarchical cluster analysis (HCA) (Figure 5a) revealed a clear data clustering in terms of the wood species with a total variance of 76.8%. The HCA yielded dendrograms (computed by the agglom first component F1 contributed to the differentiation of erative Ward’s method) are presented in Figure 7, where G. aborea and D. philippinensis. The second component dissimilarity relationships between each wood species F2 can be credited for the differentiation of V. parviflora are visible. The horizontal axis is the Euclidean distance and P. indicus. The clusters exhibited by the responses to among the groups and the vertical axis indicates the wood P. indicus and A. auriculiformis were close to each other. samples similarity. The relatedness of the wood groups of This may be due to their common family (Fabaceae). On P. indicus and A. auriculiformis in the dendrogram is again the other hand, the VOC emissions of special trees can be evident. The results of HCA complement those of PCA very different within the same taxonomical family. The demonstrate that clear discrimination of five Philippine loading plot presented in Figure 5b illustrates the relat wood species could be achieved through the analysis in a edness of the variables (responses of the sensors) to each chemiresistor array headspace. other. It reveals that sensors based on PDMS, PMMA, PS and epoxy composites, exhibit similar significant posi tive loadings in the positive side of the first principal component (F1) and, therefore, contributed distinctively Conclusions to the total variation of the data sets. These sensors are based on nonpolar polymers. On the other hand, A chemiresistor sensor array was developed for the dis the sensors based on PEVA and PEG show significant crimination of five Philippine wood species through positive loading contributions to the second principal headspace analysis. Unlike other sensors for the identi component (F2), while the sensor based on PVP shows fication of wood species, the array is based on carbon negative loading on F1. nanocomposites, which display remarkable electrical A three-dimensional (3D)-PCA plot yielded an conductive properties. These chemiresistive sensors increased variability of the score plot from 76.8% to 87.2% can be easily fabricated and assembled based on easily through the addition of another PCA axis (Figure 6). The available and low-cost materials, which have an esti 3D-PCA plots offer advantage as a better clustering of mated total cost of about 1 USD per sensor. The sensor the wood species can be observed, and the clusters of assembly provided data in a straightforward, rapid and 222 J.M. Kalaw and F.B. Sevilla III: Wood species identification based on chemiresistors non-destructive way, which could be evaluated and Hermanson, J.C., Wiedenhoeft, A.C. (2011) A brief review of machine visualized by chemometry for wood species identifica vision in the context of automated wood identification systems. IAWA J. 32:233–250. tion. The suggested approach has a high development Hung, K.-H., Lin, C.-H., Ju, L.-P. (2017) Tracking the geographical ori- and application potential to various applied purposes of gin of timber by DNA fingerprinting: a study of the endangered wood trade and technology. species Cinnamomum kanehirae in Taiwan. Holzforschung 71:853–862. IUCN (2015) International Union for Conservation of Nature and Acknowledgment: One of the authors (J.M. Kalaw) grate Natural Resources, Red List of Threatened Species Version fully acknowledges the scholarship and thesis grant 2015-4. Available at
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