978-1-4799-1205-6/13/$31.00 ©2013 IEEE

POLYMERIC CHEMORESISTOR MODEL USE IN DESIGN AND SIMULATION OF

SENSOR Saifur Rahman1, S. Hasan Saeed2, M.J.Siddiqui3,Saima Rahman4

Deptt of ECE, Integral University, Lucknow, India1,2 Deptt of Electronics Engg.,AMU, Aligarh, India3

Deptt of CSE,Integral University,Lucknow,India4 1 rehman.saifur@gmail.com, 2s.saeed@rediffmail.com, 3mjs_siddiqui@rediffmail.com,4nasir.saima@gmail.com

Abstract- For the simulation of a smart gas sensor a tale parametric representation of a polyleptic chemoresistor is projected. The simulation of equally the stagnant and active reaction of a chemoresistor to a combination of unlike gases have been implemented using Cadence software. The effects of surrounding heat (temperature), dampness and sound of sensor is also taken into account .If the values of the imitation featurers and/or the numerical representation of the subordinate-group that evaluates the gas sensor response is changed, it is likely to enlarge its function to the reproduction of chemoresistors in dissimilar configurations and with dissimilar chatter receptive materials. Keywords: Resistive gas sensors; Smart sensors; Carbon-black polymer composites; Parametric model

I. INTRODUCTION

Sensors, based on conducting polymer resistors, are very attractive for vapour/odour sensing applications because of the wide range of available polymer combinations and their ease of deposition, their ability to operate at room temperature (i.e. low device power consumption), and sensitivity to a broad range of volatile organic compounds[1–3]. For the simulation of a smart gas sensor a novel parametric model of a polymeric chemoresistor is proposed. The resistive principle, in which a change in the sensor resistance ∆RS is monitored while the sensor is exposed to the gas, is the most commonly used principle within the field of vapour/odour sensing [2,4–7,9]. . Here we illustrate our model in the design and simulation of resistive sensors employing carbon-black polymer composite films as the class of gas sensitive material. The batch to- batch variation in baseline resistance and its large temperature and humidity coefficients are some disadvantages commonly associated with polymeric chemoresistors. For instance, transient and dynamic sensor responses [1,10–13] can be monitored and used to extract information that can improve the gas recognition performance. Development of parametric models for polymeric chemoresistors can help in the design of new devices with improved characteristics and in the interpretation of experimental data.

Model Description

We present a parametric model of a polymeric chemoresistor developed for use in the design and simulation of smart gas/odour sensor systems. The Cadence software is used for implementation of this model, thus allowing both the implementation of resistive elements in smart sensor design and the simulation of the chemical static response and chemical step response of a polymeric chemoresistor to a mixture of different gases. The temperature and humidity effects are also taken into account in this model and simulates the noise present in polymer sensors, such as flicker or 1/f noise [21–23] and Johnson [20]. Sample delivery system (SDS) parameters, such as the volumetric flow rate and volume of the sensor chamber that can be customised to specific experimental set-ups are also taken into account in the new model. To demonstrate the practical application of the new model in the design of polymeric chemoresistors and to simulate their behaviour Polymer-carbon black composite films are used here as gas sensitive materials. Lewis at Caltech firstly reported the films, which consist of conducting black nano carbon spheres dispersed into a non conducting polymer base films. The polymer within the composite film absorbs the vapour and swells reversibly when exposed to gases. This swelling causes the distances between the conductive carbon black particles to increase and thus, induces a resistance change in the composite film [4,6]. An array of several sensors made up by different polymer composite films have the pattern response of a ‘fingerprint’ to classify different gas or chemical mixture [1,8].

Fig 1 Basic block diagram of E-Nose

Electronic Nose Decision

Sensing Classification

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Sensing: Corresponds to variation in sensorespect to exposure to odour/gas concentrati

Classification:Detects the gas sample tdecision taken from concentration of differin odour. Classification corresponds to mneural network , principle comp(PCA),gaussian mixture model etc.

Sensor Simulation Model and Simulation

After verification of sensor simulation described in this section it was used to simuof polymer composite sensors when exposor mixture of gases like carbon black polymcell comprising a chemoresistor mrepresentation and a layout design, wasimulation a d.c. voltage of 2.4 V is appliedthe output signal is the voltage difference terminal R1 &R2. To convert the sensor voltage that can be further amplified, proceto other devices, some gas sensorecommended to use a voltage divider cvariation in resistance of the gas-sensitive1–50%), subsequent to exposure to a gasCgas, can then be measured through the chavoltage Vout which is function of the sensor

Fig 2 Sensor Simulation M

Vout= Vref* RS (C)/Rref+RS(C) sensitivity S of the potential divider is ddVout/dRS = Vref*Rref/(Rref+RS(C))2

maximum for Rref=RS. The reference resischosen to be equal to the sensor baseline v

maximize sensitivity for small changes injection and sensor temperature are simu

sources.

Temperature

Gas Concentration (Cgas)

Humidity (H)

IEEE

or resistance with ion (Cgas).

type as per the rent gases present method related to ponent analysis

n results

model which is mulate the behavior

ed to a single gas mer. The complete

model, schematic as used. In this

d to the input while across the sensor resistance into a

essed or interfaced or manufacturers configuration. The e device (typically s of concentration ange in the output r resistance Rs.

Model

The defined as S= which has a

stance value was value (10 k Ω) to of RS Gases

ulated by voltage

Simulation RFor without Sample del

Fast ResponseAt ON time

Slow ResponsAt OFF time

Fig: 3 Without Sample d

Fig 4 Change in Rdyn w.r.t humidity at

In the above figure it can be seen changes with respect to humidity H=0 and 10 K ppm, T=40°C)

Change in dynamic resis

Fig 5 Change in Rdyn w.r.t humidity at co

In the above fig 5 it can be seenconstant temperature 50°C even thto 10 K ppm.

Result ivery System :

Cgas 10K ppmH=0 T=40 0 C

∆R0 Static Resistance Change (Ω)

∆R0 Dynamic Resistance Change (Ω)

e

delivery system

t constant temperature T=40°C

that the dynamic resistance at constant temperature. (

stance

onstant temperature T=50°C

n that Rdyn not changes at he humidity changes from 0

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In fig 6 it can be seen that the Rdyn changes at different temperature and at constant humidity(T=40°C & 50°C, H=0 Kppm) whereas in fig 7 it can be seen that when H=10 K ppm and temperature varies from 40°C to 50°C then also Rdyn changes.

Fig 6 Change in Rdyn w.r.t temperature at humidityH=0 K ppm

Fig 7 Change in Rdyn w.r.t temperature at humidityH=10 K ppm

Simulation Result For with Sample delivery System :

Cgas 10K ppmH=0 T=40 0 CSDS is First order Response

∆R0 Static Resistance Change (Ω)

∆R0 Dynamic Resistance Change (Ω)

Fast ResponseAt ON time

Slow ResponseAt OFF time

Fig 8 With Sample Delivery System

0 500 1000 1500 2000 2500 30000

500

1000Change in Cgas in prsence of SDS

0 500 1000 1500 2000 2500 3000-200

0

200

400

600Change in Static Resistance in prsence of SDS

0 500 1000 1500 2000 2500 3000-200

0

200

400

600Change in dynamic Resistance in prsence of SDS

de

l Ro

time (sec)

H= 0K ppm T= 40 C

H= 10K ppm T= 40 C

Fig 9 Change in Rdyn with SDS at different H and Constant T

In the above figure it can be seen that with sample delivery system the dynamic resistance changes with respect to humidity and at constant temperature. ( H=0 and 10 K ppm, T=40°C). Whereas in figure 10 (a) it can be seen that the Rdyn changes when temperature is kept constant at 50°C and humidity changes from 0 to 10 Kppm. In the above figure it can be seen that with sample delivery system the dynamic resistance changes

Fig 10 Change in Rdyn with SDS at different combination of H and T.

largely with respect to temperature and at constant humidity. Figure 10(b) has ( H=0 K ppm T=40°C & 50°C). Whereas in figure 10(c) it can be seen that the Rdyn changes when humidity is kept constant at 10 Kppm and temperature changes from 40°C to 50°C. Variation in R is much sensitive to change in temperature and it shows very small variation with respect to change in humidity.

0 500 1000 1500 2000 2500 3000-200

0

200

400(a)Change in dynamic Resistance in prsence of SDS

del

Ro

time (sec)

H= 0K ppm T= 50 C

H= 10K ppm T= 50 C

0 500 1000 1500 2000 2500 30000

200

400

600(b)Change in dynamic Resistance in prsence of SDS

del R

o

time (sec)

H= 0K ppm T= 40 C

H= 0K ppm T= 50 C

0 500 1000 1500 2000 2500 3000-200

0

200

400

600(c)Change in dynamic Resistance in prsence of SDS at

del R

o

time (sec)

H= 10K ppm T= 40 C

H= 10K ppm T= 50 C

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Fig 11 Change in Rdyn with SDS at different H and T for simulated and experimentally tested results The chemiresistor sensor used here are Carbon black/poly vinyl pyrrolidone (PVP) composite (20 wt % carbon black) (Aldrich). For experimental validation test was carried out in a closed gas station with independent control of the concentrations of water and methanol vapors. Chemiresistor response for the gas exposure was obtained by recording the changes in resistance. The resistances and thermocouple voltage are measured by digital multimeter and recorded in LabVIEW. Thes recorded Data was then transferred to Excel sheet and in a text format it is read by Matlab. These results were compared with the results of our simulation model. Figure 11 represents the validation of our proposed model in comparison with the reading recorded by experimental result at different temperature and humidity. Both curves in figure 11( a to d) are showing close resemblance and we get max error of 0.05% in steady state values of dynamic resistance. Conclusion

We have presented and validated a model developed newly which is used for the simulation and design of different resistive polymeric cell that can be used after standardization in the design of smart gas based systems. The model simulates the chemical static response and chemical step response to a gas or mixture of gases. Temperature, humidity and sensor noise effects are taken into consideration. By changing the parameters values it is possible to implement different

polymeric chemoresistors with different sensing characteristics.

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