© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

phys. stat. sol. (b) 245, No. 10, 2343–2346 (2008) / DOI 10.1002/pssb.200879581 p s sbasic solid state physics

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Drift effect of fluctuation enhanced gas sensing on carbon nanotube sensors

Peter Heszler*,1, Zoltan Gingl2, Robert Mingesz2, Attila Csengeri2, Henrik Haspel3, Akos Kukovecz3, Zoltan Kónya3, Imre Kiricsi3, Radu Ionescu4, Jani Mäklin5, Tero Mustonen5, Géza Tóth5, Niina Halonen5, Krisztián Kordás5, Jouko Vähäkangas5, and Hannu Moilanen5

1 Research Group on Laser Physics of the Hungarian Academy of Sciences, University of Szeged, PO Box 406, 6701 Szeged,

Hungary 2 Department of Experimental Physics, University of Szeged, Dóm tér 9, 6720 Szeged, Hungary 3 Department of Applied and Environmental Chemistry, University of Szeged, Rerrich Béla tér 1, Szeged, Hungary 4 Department of Electronic Engineering, Universitat Rovira i Virgili, Avda. Països Catalans 26, 43007 Tarragona, Spain 5 Microelectronics and Materials Physics Laboratories and EMPART Research Group of Infotech Oulu, University of Oulu,

P.O. Box 4500, 90014 Oulu, Finland

Received 29 April 2008, accepted 8 August 2008

Published online 10 September 2008

PACS 07.07.Df, 73.50.Td, 73.63.Fg, 81.07.De

* Corresponding author: e-mail peterh@physx.u-szeged.hu, Phone: +36 62 544 421, Fax: +36 62 544 658

© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

1 Introduction The simplest resistive gas sensors use the principle that the mean resistance of the sensor changes as the gas to be sensed is adsorbed on the sensor surface. This effect is significant in semiconductors and this type of devices are called Taguchi-type of gas sensors [1]. The disadvantage of Taguchi sensors is the lack of chemical se-lectivity. There are several efforts to overcome this e.g., by operating the sensor(s) at different temperatures, as the optimal temperature usually alters for different gases for a specific sensor [2]. The dynamic counterpart of this method i.e., the temperature modulation technique, whereby the sensor temperature is modulated, can also be applied [3]. Fluctuation enhanced sensing (FES) is a novel method, where the noise spectrum (more closely power spectral density, (PDS)) of the fluctuations of the sensor resistance (conductance) carries the chemical information

[4, 5]. It was shown to be sensitive for odors [6] and the method can even be more sensitive, as the amplitude of the PSD serves as sensor signal, as compared to the mean re-sistance change [7] of the Taguchi sensors.

Nanostructured materials, as e.g., nanoparticle films, are ideal active layers for FES. These films exhibit high surface area in porous structures, furthermore significant electronic conductance fluctuations can be observed in these layers [8]. Carbon nanotubes (CNTs) are also promising candidates for FES. Theoretical studies have predicted a significant variation in the electronic properties of CNTs as a result of gas adsorption [9]. In addition, both isolated single wall carbon nanotube [10] and multi-walled CNT sensors presented gas sensing properties at room temperature [11].

A low-noise electronic system is built and tested for fluctua-

tion enhanced sensing. This latter is a new technique and

based on the determination of the power spectral density of

the stationary resistance fluctuations of semiconductor gas

sensors. Its use is advantageous for improving the chemical

selectivity of sensors. However, subsequent to an initial fast

change of the sensor mean resistance, as a sensor is exposed

to an analyte gas, a typical drift of the resistance can be ob-

served. This effect hinders evolving stacionary conditions and

thus acquiring fast measurements when applying fluctuation

enhanced sensing. Therefore, this drift effect is studied both

experimentally and theoretically. Functionalized carbon nano-

tube layers on silicon chips serve as active material for the

experimental investigations. Power spectral density functions

are measured and simulated numerically with and without

drift conditions. The results are compared and the effect of

resistive drift on fluctuation enhanced sensing is discussed.

2344 P. Heszler et al.: Drift effect of fluctuation enhanced gas sensing on carbon nanotube sensors

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In this communication we present a realization of a FES system and in addition, we analyze the measurement setup regarding the mean resistance drift during the acquisition time of the PSD. The drift is an unavoidable effect and can be significant for long measurement times as aiming to reduce the uncertainties of the PSD amplitudes. To be quantitative, PSD spectra with different drift values will be generated and analyzed with similar characteristics as observed for the carbon nanotube gas sensors. The measured PSD functions will be further analyzed by pattern recognition, more closely by principal component analysis (PCA).

2 Experimental 2.1 Sensor setup A four probe system was used for the FES measure-ments with gold electrodes. Two outer electrodes were used to inject the stabilized probe current (100 μA) and two other inner electrodes, with a separation of 15 μm, were applied for the fluctuation measurements (see details in next subsection). The experiments were performed at room temperature and the sensor material was annealed at ~ 150

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C for 20 min in order to refresh the sensor between each gas exposure. The active material of the sensor was a film consisting of multi-walled carbon nanotubes (MWCNTs) produced by catalytic chemical vapor deposi-tion, functionalized by 1,8 diamino-octane. The mean re-sistance value of the applied sensor film was ~ 2.8 kΩ in this study. For obtaining the desired concentrations of the test gases in dry synthetic air, calibrated Mass Flow Controllers (MFC) were used. The outlets of the MFC first were con-nected to a buffer (14 cm long tube) to promote gas mixing, followed by the test chamber that was a four-way cross. The buffer and test chamber had a diameter of 4 cm and made of stainless steel (standard KF SS components). The gas flow was aligned in one direction, while the sensor chip was placed through the perpendicular opening of the four-way cross. N2O was applied as test gas in the ppm region diluted in dry synthetic air (SA) with a total flow rate of 100 sccm. 2.2 Electronic data acquisition system The sig-nal conditioning for the Data Acquisition System (DAS) is presented in Fig. 1. A precision low noise voltage refer-ence is used to drive a selectable range low input current and low noise voltage-to-current converter. The current flows through the gas sensor resulting in a voltage drop that is amplified by a differential amplifier (DA). The out-put of the DA drives a low pass filter (LPF), also working as an anti-aliasing filter, whose output serves as an input (#1) for the DAS where the signal is digitized first by an analogue to digital (A/D) converter. This signal is propor-tional to the mean resistance of the gas sensor. The resis-tance fluctuation of the sensor is measured, after the differ-ential amplifier by using a high pass filter to remove the

DC component, followed by a programmable gain ampli-fier and another anti-aliasing low pass filter. This signal is then digitized by a second A/D converter of the DAS (in-put #2). These two independent high accuracy 16-bit sigma-delta A/D converters are operated at a sampling rate of 10 kHz and incorporated in the data acquisition system.

Figure 1 Schematics of the signal conditioning for the FES

measurement system. DAS – Data Acquisition System.

The flexibility and efficiency of the DAS is guaranteed by a digital signal processor (DSP, type ADSP 2181) used to control the sampling process, A/D converters and the isolated USB interface to communicate with the host com-puter. The DSP is programmed in its flexible algebra as-sembly language. Figure 2 Overall scheme of the FES measurement system. DAS

– Data Acquisition System.

The whole measurement system is controlled by the host computer, programmed by using LabVIEW and Vis-ual C++ environments, for the overall scheme see Fig. 2. The gas flow controllers are also connected via isolated USB ports and driven by using the HART protocol imple-mented in LabVIEW. These implementations provide a continuous uninterrupted data collection. The data analysis is performed based on LabVIEW's analysis libraries. We utilized sample lengths of 4096 points at a sample rate of 10 kHz for the results presented below, however the sys-tem allows continuous data collection as well. The power spectral densities (PSD) are calculated from the acquired

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phys. stat. sol. (b) 245, No. 10 (2008) 2345

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4096 data points that are considered as one data set. Typi-cally 50 to 200 data sets are used for calculating one PSD in order to improving the signal to noise ratio. 3 Experimental results Prior to any measurement, the sensor was reset by applying a heat pulse (see Fig. 3a) in a continuous flow of synthetic air. The heat pulse re-sulted in a typical temperature change of 150 °C and its time-duration was 20 min. After a cooling period, during the resistance value was stabilized, a certain concentration of N2O test gas was injected into the reactor. Usually, these concentrations resulted in a very small change of the DC resistance e.g., ΔR/R was 0.2% for 50 ppm N2O, see Fig. 3b. This also means that this type of CNT sensor is not ap-plicable as a “classical” semiconductor gas sensor due to its small sensitivity. PSD functions were acquired for both pure SA and after applying N2O exposures, see Fig. 4. Nearly no difference can be seen between the SA and 50 ppm N2O exposures, however the application of the princi-pal component analysis with the PSD functions as inputs, could differentiate between the mentioned gases showing the potential of fluctuation enhanced sensing, see Fig. 5.

Figure 3 Resistance change versus time for a typical measure-

ment including a) heat pulse on and off followed by introduction

of 10 and 50 ppm N2O with a b) close up region for 50 ppm N2O

injection.

Acquiring PSD functions requires steady-state condi-tions in optimal case. This latter means that the mean value of the resistance of the sensor is supposed to be constant. However, a small drift can also be observed before and af-ter the gas injection takes place, see Fig. 3b. This drift was typically 2-5 x 10–3 Ω/s at about 2.8 kΩ sensor resistance.

This drift, depending on its magnitude, has an effect on the low frequency part of the PSD spectra and thus could modify the results of PCA analysis. In order to check this issue, model calculations were performed to generate PSD functions with and without drift that will be discussed in the following section.

Figure 4 Simulated 1/f (its amplitude is lowered by an order of

magnitude) and measured PSDs of a CNT sensor, applying 100

data sets. The measurement was taken upon 50 ppm N2O expo-

sure and pure synthetic air. The simulation was performed with a

mean resistance of 2.8 kΩ with no drift, RMS of the resistance

variance was 0.01 Ω, probe current 100 μA. These latter data cor-

respond to the experimental values. The peaks at 50, 100, 150,

250 and 350 Hz in the measured spectrum are the fundamental

and higher harmonics of the 50 Hz line frequency.

Figure 5 Score plots of PCA for exposures of synthetic air (open

circle) and 50 ppm N2O (full circles).

4 Model calculations PSD functions of 1/f type and added variable drift were generated with similar character-istics as it was observed for the CNT sensors. To this end, first a time series of white noise data were generated by a suitable random number generator. Then this data set was Fourier transformed, multiplied by 1/f 1/2 and inverse Fou-rier transformed back to the time domain. After this, a cer-tain drift was added and then this data were spectrally ana-lyzed. Figure 4 shows a simulated PSD spectrum with no drift and compared with measured PSD functions. As can be seen, the agreement between the measured and simu-lated (1/f) spectra are very good. From this also follows that the response of the FES CNT sensor can typically be described as a 1/f type of noise. Simulated spectra with dif-

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2346 P. Heszler et al.: Drift effect of fluctuation enhanced gas sensing on carbon nanotube sensors

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ferent drift values are presented in Fig. 6. Both the ampli-tude and the spectral (1/f) shape do not change up to a drift (d) value of 0.1 Ω/s, however significant alteration can be seen for d = 1 Ω/s in the low frequency (< 100 Hz) region. Figure 6 Simulated spectra with different drift (d) values in Ω/s,

mean sensor resistance 2.8 kΩ.

Figure 7 Score plots of PCA analysis of the simulated PSDs for

various drift (d) values in Ω/s. Symbols with full and open circles

correspond to drift and no drift, respectively. The frequency win-

dow of the PSD is 2-5000 Hz.

The effect of different drift values on the PCA analysis of the simulated power spectral densities is drawn in Fig. 7. The points with drift (filled circle) start to differentiate

from the points with no drift (hollow circle) at a drift value of 0.1 Ω/s. Thus it can be concluded that mean resistance drift less than 0.1 Ω/s, with a mean value of 2.8 kΩ does dot affect the PCA analysis based on PSD. These data correspond to a 4x10–5 1/s relative drift. Since the typical observed drift for CNT sensors was about 2-5 x 10–3 Ω/s, it can be also concluded that drift does not affect the results of PCA analysis of the applied CNT sensors in this study. 5 Summary and conclusions In summary, an elec-tronic setup was presented for fluctuation enhanced gas sensing. Fluctuations that can be described as 1/f type could be observed from MWCNT sensors. It was shown that 4x10–5 1/s relative drift of the mean resistance of the sensor (in our particular case 0.1 Ω/s drift over 2.8 kΩ mean sensor resistance) did not affect the results of PCA analysis when the acquired PSDs in the 2-5000 Hz fre-quency window served as input for the pattern recognition.

Acknowledgements This work was supported by the EC

FP6 STREP project „SANES” (NMP4-CT-2006-017310-

SANES) and grant OTKA #K69018. Z: Gingl and A. Kukovecz

acknowledge support from the Bolyai Fellowship of the Hungar-

ian Academy of Sciences. R. Ionescu acknowledges support

MEC under 'Jose Castillejo' program. K. Kordas is grateful to the

Academy of Finland for the research fellow post and incentive

funding (120853).

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