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Publication: Sensors & Transducers
Date published:
Language: English
PMID: 104212
ISSN: 17265479
Journal code: SNTD

(ProQuest: ... denotes formulae omitted.)

1. Introduction

Detection of volatile organic chemicals (VOCs) in high concentrations is a very important problem of Health and Safety. The presence of VOCs is concentrations between LEL and UEL may cause fire or/and explosion triggered by an accidental electrical spark. There were several incidences ofthat type recorded recently; and this stimulates great demands on VOC sensors. The most common and commercially available nondispersive infrared (NDIR) sensors are based on detection of characteristic infrared spectral characteristics of VOCs [I]; these sensors are quite complex and relatively expensive. At the same time the research in calixarene thin film films carried out in recent years revealed their unique properties of fast and fully reversible adsorption of VOCs in high concentrations close to the saturated vapour pressures [2, 3]. The concentration range fits very well into LEL-UEL band. The mechanism of capillary condensation of VOCs in nanoporous structure of thin films of amphiphilic calixarene derivatives was suggested to explain the observed behaviour [4]. Further research work was focused on the utilisation of quartz crystal microbalance (QCM) coated with calixarenes for detection of VOCs and has resulted in the development of prototype VOCs sensor devices. Although the selectivity of particular calixarene derivatives to VOCs is rather poor, the recognition of VOCs can be achieved with QCM impedance measurements [5, 6] or QCMD method [7, 8]. An alternative way of VOCs indentification lies in the use of array of QCM sensors coated with different calixarene derivatives [9]. The revival of interest to the subject of VOC detection was stimulated by European research programme in robotics focused on the use of mobile robots to assist search and rescue operations in potentially hazardous environments where, under normal circumstances human presence would be risky. The chemical sensor array is designed to be mounted on the mobile robotic platform. The data obtained will be used for both gas detection (monitoring) and olfactory based navigation - localizing the source of the potential hazard and also to provide route planning information.

This work is therefore dedicated to the development of an array of quartz crystals coated with different calixarenes and its testing to a range of VOCs of different concentrations in LEL-UEL range. Another aspect of the work is related to realization of the principles of olfactory navigation for mobile robots [10]. It has been suggested that robots can follow "scent marks" using the sensor array approach. Previous attempts in this direction were made with the use of metal oxide semiconductor (MOS) sensors [10] and different scent marks including camphor [H]. The choice of camphor essential oil was justified by unique "smell signature" of this compound as compared to other VOCs, as well as by its slow evaporation rate which makes the scent marks last longer. From our point of view, QCM sensors are more suitable for detection of camphor. Therefore, several tests of a coated QCMcalixarene sensor array on camphor were performed aiming at realization of the robotics olfactory navigation in future.

2. Sensor Array Design and Measurements Routine

A sensor array shown in Fig. 1 contains eight QCM sensors coated with different calixarene derivatives (one uncoated quartz crystal is used as reference). In addition, six commercial MOS sensors (Figaro Engineering Ine) are introduced for the benefit of comparison as well as for detection of other gases, such as oxygen, carbon monoxide, natural gas, cyanide gas, etc. Analysis of other gases however is beyond the task of this work which is focused entirely on detection of VOCs. All sensors were assembled in the PTFE circular chamber with inlet and outlet tubes for gases (vapours) injection and flushing with fresh air, respectively.

The secondary electronics, i.e. oscillation circuits for QCM, power supplies for MOS and multiplexers have been designed and fabricated in house. The crystal driver circuits are based on a Pierce oscillator using a standard Texas Instruments crystal oscillator driver integrated circuit. The outputs of which are fed into an 8 channel computer controlled relay multiplexer; subsequently an Agilent frequency counter measures the resonant frequency of each crystal. The MOS devices simply form part of a potential divider network and the output voltages are sampled using a 12 Bit ADC at 1 second intervals. All MOS measurements are controlled via an embedded microcontroller.

All the secondary electronics reside outside the exposure chamber with only the sensing elements exposed to the analytes inside the PTFE cell. All sensors are connected to a PC using an RS232 interface with the overall data acquisition performed through a Lab View program which collects the readings from 14 channels corresponding to the QCM and MOS sensors. All further data processing is performed within the Lab View software.

The following amphiphilic calixarene molecules were used in this work: (i) a range of calix[4]resorcinarenes having substituting alkyl chains of different length, e.g. Ci7H35, Ci5H3I1 CnH23 , C5Hn, and CH3 named, respectively, as C4RA-C17, C4RA-C15, C4RA-C11, C4RA-C5, and C4RACl; (ii) tetra-tertbutyl calix[4]arene; and (iii) sexta-tertbutyl calix[6]arene. The above calixarene derivatives, chemical structure of which are shown in Fig. 2, have different size of inner cavities and therefore provide a base for pattern recognition of different VOCs. The above calixarene derivatives were synthesised at Sheffield University, UK and in the Institute of Organic Chemistry, Academy of Sciences of Ukraine. Thin films of all calixarenes were deposited on AT-cut quartz crystals (Euroquartz Limited) by spin coating using the Electronic Microsystems model 4000 spinner. A special holder for quartz crystals has been designed in-house. The spin speed was selected as 2000 rpm. All compounds were dissolved in ethanol in concentrations of 15 mg/ml. The deposition parameters were optimised in order to achieve the required film thickness and good homogeneity [5, 6]. The quartz crystals were numbered as follows: crystals Ql, Q2, Q3, Q4, and Q5 were coated with C4RA molecules having C 17, C 15, CIl, C5, and Cl alkyl chains, respectively; Q6 and Q7 were coated with ttb-C4A and ttb-C6A, respectively.

The thickness of films deposited on silicon substrates was controlled with ellipsometry. J.A. Woollam M2000V spectroscopic ellipsometer was used for this purpose. Typical values of thickness were in the range of 320 nm. Similar thickness values were obtained by comparison of the oscillation frequency of quartz crystals before and after coating using Sauerbrey equation [12]. The homogeneity of calixarene coatings on silicon substrates was checked with AFM using Nanoscope Ilia instrument (Digital Technology) operating in tapping mode with Veeco tips of 4 to 7nm in radius. Typical AFM images shown in Fig. 3a demonstrate a smooth surface for majority of calixarene films with the mean roughness of around 0.4-0.45 nm; the films of C4RA-C1 however contain pin-holes which have little impact on QCM measurements. The film thickness of around 300 nm can be estimated from the depth of the scratch on the AFM image in Fig. 3b.

The sensor array was tested with a wide range of vapours, produced by organic solvents from different classes, namely hexane (hydrocarbons), n-propanol (alcohols), benzene, toluene, m-xylene (aromatic), acetone (ketones), chroloform (chloro-hydrocarbons). All the solvents of high purity were purchased from Aldrich, UK. In order to achieve the required vapour concentration, the respective liquid solvents were injected into the chamber with a micro-syringe. The injected amount of liquid solvents was varied from 0.1 µ? to 50 µ? depending on the type of solvent.

The respective vapour concentrations in ppm units were calculated using known data, such as molecular weight (M), density (p), for all solvents used:

... (1)

where Qs is the quantity of liquid solvent in µl, V is the volume of the gas cell, and 22.4 is the molar gas volume constant. The relation between vapour concentrations measured in (ppm) and (g/I) is established as:

... (2)

where R^sub g^ = 8.206 . 10^sup -2^ (Fatm-K^sup -1^mol^sup -1^) is the gas constant, T is the temperature in degrees Kelvin. Sometimes it is useful to express the concentration in the units of pressure relative to the saturated vapour pressure (P/Ps); this is particularly the case for capillary condensation of vapours in calixarene films [4]. The values of the saturated vapour pressure are known parameters for all the solvents used are presented in Table 1 . The vapour pressure in atmospheres ... can be calculated from the respective concentration as:

... (3)

where C is the concentration in grams per litre, M is the molecular weight of the solvent in grams, Fis the volume of a cell. All physical parameters of the solvents used are summarized in the Table 1.

In addition to the VOCs mentioned above, camphor essential oil from cinnamon was used to produce vapours of camphor. The values of M = 152.24, p=0.9091 g/cm3 and Ps = 0.0867 kPa used were characteristics of pure camphor. The content of camphor in the essential oil is unknown unfortunately, so the obtained concentrations of camphor can be considered as a maximal estimate. The background effect of liquid oil on QCM was negligible as was confirmed by test measurements of vapours produced by refined neutral (odourless) vegetable oil.

4. Experimental Results and Analysis

Typical responses of QCM and MOS sensors to the injection of 9 µ? of benzene are shown in Fig. 4. The sampling number in the horizontal axes in the graph corresponds to about 5 second periods. QCM sensor shows a decrease of the oscillation frequency of more than 100 Hz, while MOS sensor gives an increase in the conductivity measured through a change in voltage at the ADC input. As one can see, the MOS sensor responds very quickly (few seconds) but soon reaches the upper limit of detection although the vapour concentration of about 0.042 P/Ps is far away from saturated vapours. The recovery of MOS sensors is rather poor (about 70 % in 100 s); full recovery may take much longer (up to 30 min).

QCM sensors responds slower than MOS ones but recover quicker. A relatively slow (120-150 s) response of QCM sensors is due to evaporation of liquid solvent in a large (0.55 I) chamber; our early experiments in smaller (50 ml) chamber showed much faster response in the range of 10-15 s [5-7]. Recovery is much faster (40-50 s) and complete, which is typical for thin LB/spun films amphiphilic calixarenes [5, 6]. Comparison of responses of QCM and MOS sensors clearly shows the advantages of the latter in detection of high concentrations of organic vapours. MOS sensors are simply not suitable because they saturate at much smaller concentrations.

The series of responses of all QCM sensors to different concentrations of propanol in Fig. 5 show an obvious correlation between the values of frequency change and the alkyl chain length of calixarene derivatives and the size of the calixarene cavity. Long alkyl chains C 17, Cl 5, and Cl 1 in Ql, Q2, and Q3 yield large response than Q4 and particularly Q5 having shorter alkyl chain of C5 and Cl. Q6 coated with ttb-C4A gives even smaller response; and Q7 coated with ttb-C6A is practically nonresponding. Also the stability of operation of QCM Q1-Q4 is much better as compared to that for Q5Q7 where a drift of the baseline frequency is observed. Longer alkyl chains provide better lipophilic interaction with molecules of organic solvents (propanol this time) and thus cause larger response. Also, calixarenes with longer alkyl chains form more porous films structure where the effect of capillary condensation is more pronounced as compared to more condensed films of calixarenes with shorter alkyl chains. The films of ttb-C6A molecules having the largest size of the cavity practically do not adsorb any of the analytes studied. The most likely reasons for that may be related to almost equal adsorption and desorption rates in such molecules or simply to much more dense film structure with no pores and therefore no capillary condensation.

Concentration dependence of responses of all QCM to propanol vapour is shown in Fig. 6.

All the curves presented show the trend of saturation at the pressures close to the saturated vapour pressure. The highest response is recorded for Q3 (C4RA-C1 1) with slightly smaller responses of other long alkyl chain calixarenes: Q2 (C4RA-C15 and Ql (C4RA-C17). It seems that C4RA-C11 molecules provide an optimal adsorption conditions for propanol. Calixarenes with shorter alkyl chains give much smaller response (see Q4, Q5, and Q6); Q7 coated with ttb-C6A does not respond practically. This trend is repeated for all the analytes studied with some deviations. The results are very much in line with the concept of capillary condensation [4], according to which calixarenes with long alkyl chains provides sufficient liphophilic interaction with the molecules of solvent and at the same time give larger pore size in thin films. In contrast, calixarenes with shorter alkyl chains provides more closely packed film structure with smaller pore size as well as a lower level of lipophilic interaction resulting in smaller responses.

Fig. 7 shows the concentration dependences of the response of Q3 (C4RA-C11) to different analytes including camphor. The concentration is given in ppm units this time since the exact value of the saturated vapour pressure of camphor essential oil is not known. It can be noticed that less volatile VOCs give larger response because they produce much less vapour pressure at given concentration. For that reason, a very viscous camphor oil is standing alone from the rest of analytes. In fact, the actual vapour concentration of camphor can be even smaller. The calculations have been performed on the assumption of 100% pure camphor which is not the case. Because of that, the curve for the camphor can be even further away from the rest of analytes. The experimental data presented proved the suggestion that camphor produces characteristic signature smell different from the rest of common VOCs and therefore can be used as a "scent mark" for robotic navigation.

Recording of responses of QCM sensors coated with different calixarene derivatives to exposure of different VOCs in different concentrations aims at building a database for pattern recognition of VOCs. The diagram in Fig. 8 shows a comparison of responces of all QCM sensors to the vapours of propanol and m-xylene of 0.5 P/Ps. Apart from Q7 which does not practically respond, other crystals Ql -Q6 gives slightly different responces although the general trend of domination of Q1-Q3 is clear. These differences are enough for pattern recognition of VOCs in a 7-dimensional space of sensor responces, which can be done using approprate software, for example ANN (artificial neural network).

5. Conclusions

An array QCM and MOS sensors was build and tested on vapours of different VOCs. QCM sensors were found to be more suitable for detection of VOCs in high pre-explosive concentrations in the range of 103 -104 ppm, while MOS are more suitable for detection of VOCs in concentrations of at least two orders of magnitude lower, as well as for other gases such as oxygen, carbon monoxide, natural gas, cyanide gas, and many more.

The use of quartz crystals coated with different amphiphilic calixarene derivatives facilitates for pattern recognition of different VOCs. The experimental work of building a database for pattern recognition of VOCs is underway. The ANN is an obvious choice for further data analysis in order to identify and quantify various gases and vapours associated with fire. The complete sensor array installed on the mobile robotic platform will be used as for monitoring the concentration of VOCs and other toxic gases associated with fire as well as for robotic navigation.

Camphor was found to have a characteristic response pattern different from the rest of VOCs, and it can be used as a 'scent mark' for robotic navigation.


This work was supported by two EU projects IST-ViewFinder-045541 and IST-Guardians -045269.


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Author affiliation:

Alan F. Holloway, Alexei Nabok, *Abbass A. Hashim, Jacques Penders

Sheffield Hallam University, Materials and Engineering Research Institute,

Sheffield, Sl IWB5UK

Tel: 441 142253682, fax: 441 142253433


Received: 19 January 201 0 /Accepted: 1 9 February 201 0 /Published: 26 February 201 0

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