1. COMSOL Simulation of MEMS Particulate Matter extraction and
E nose sensor
Kolluri Sai Srikar Advisor: Prof. Xingguo Xiong
Department Electrical Engineering , University of Bridgeport, Bridgeport, CT 06604
Abstract
Abstract: In this poster, the design and simulation of a three part system consisting of a MEMS motor ,virtual impactor for separating particulate matter and E nose sensor to make an explosive detection system .This particles travel through the nozzle and particles distributed according size into different channels and feed through the virtual impactors to channels consisting of e nose sensors based on the Sno2 and other conducting polymers which generate electric pulses when come in contact with the highly volatile components called VOCs and generate unique pattern for each matter. Thus this technique can be used detect explosives for example SEMTEX, TNT other plastic explosives which generate VOCs. There are other types senses are made all the more effective because manufacturers of commercial explosives, like Semtex, may add odor tags, like DMDNB (2,3-dimethyl-2,3-dinitrobutane). Dogs (and some specialized mobile ion mass spectrometry devices), can reportedly detect DMDNB at levels below one part per billion. Thus the device was verified with COMSOL simulation.
Fig 2.BAW sensor
Introduction
Electronic noses, E-Noses, incorporate an array of chemical sensors of different specificities, which simultaneously respond to the volatile chemicals present in a gas sample. The two main components of an electronic nose are the sensing system and the automated pattern recognition system. The sensing system can be an array of gas sensors or it can be a single device. Gas sensors, based on chemical sensitivity of semiconducting metal oxides, are readily available an have been more widely used to make arrays for odor measurements than any other single class of gas sensor .They are characterized by a relatively fast response, typically less than 10 secs they have high sensitivity to a range of organic vapors.
Metal oxide (MOx) sensors consist of a metal-oxide semiconducting film (e.g. SnO2, TiO2, ZnO, ZrO2) coated onto a ceramic substrate (e.g. alumina). Most often the device also contains a heating element. Oxygen from the air is dissolved in the semiconductors lattice, setting its electrical resistance to a background level. During measurement, volatiles are adsorbed at the surface of the semiconductor where they react with the dissolved oxygen species causing a further modification of the resistance of the device . 1
Fig 3. MicroMotor and dielectrophoretic separator
Working
The basic surface acoustic wave device consists of a piezoelectric substrate, an input interdigitated transducer (IDT) on one side of the surface of the substrate, and a second output interdigitated transducer on the other side of the substrate.
The space between the IDTs, across which the surface acoustic wave will propagate, is known as the delay line. This region is called the delay line because the signal, which is a mechanical wave at this point, moves much slower than its electromagnetic form, thus causing an appreciable delay. This type of sensor is usually operated at a temperature slightly higher than room temperature. On the other hand, chemo-resistors based on gas-sensitive metal oxide materials and MOSFETs coated with catalytic meta with operating temperatures up to 450°C and 200°C, respectively, exhibit high power consumption.
Taguchi was the first to develop metal-oxide gas sensors to the level of an industrial product. These Taguchi-type sensors are still on the market. However, most of the commercially available sensors nowadays are manufactured using screen-printing techniques on small and thin ceramic substrates. Screen-printing has the advantage that thick films of gas-sensitive metal-oxide sensors are produced in batch processing. This technology is well established, and high performance has been achieved using screenprinted ceramic sensors in various field applications.
Design Consideration
Figure 1.Mesh Structure of the sensor . Figure 2. Structure of sensor
For design a simple glass plate channel through a series of modules of devices have been arranged first a micro motor pump to heat up and transfer the VOC components subjected to dielectrophoretic separation to separate particles by sizes and interact with sensor array of BAW resonators generating a unique electronic signal for a type of VOC substance.
The materials are used here are various components on glass substrate
BAW sensor consisting of Silicon(SI),Aluminum(AL),Zinc oxide(ZnO)
A thin-film bulk acoustic resonator (FBAR or TFBAR) is a device consisting of a piezoelectric material sandwiched between two electrodes and acoustically isolated from the surrounding medium.
FBAR devices using piezoelectric films with thicknesses ranging from several micrometers down to tenth of micrometers resonate in the frequency range of roughly 100 MHz to 10 GHz. Aluminum nitride and zinc oxide are two common piezoelectric materials used in FBARs.
Conclusions and Future Work
There are numerous potential applications of electronic noses from the product and process control to the environmental monitoring of pollutants and diagnosis of medical complaints. However, this requires the developments of application-specific electronic nose technology, that is electronic noses that have been designed for a particular application. This usually involves the selection of the appropriate active material, sensor type and pattern recognition scheme. The work has led to several commercial instruments, one employing commercial tin oxide sensors Fox 2000, Alpha MOS and another employing conducting polymer sensors (NOSE, Neotronics Ltd, UK). Future developments in the use of hybrid microsensor arrays and the development of adaptive artificial neural networking techniques will lead to superior electronic noses.
COMSOL Simulation
In Comsol all above discussed modules have been implemented separately
Fig 4 and 5 are the BAW sensor other simulations include the Micro motor to move the particles in to chamber to separate them as seen in fig 6 and then applied to array of BAW sensor.The Admittance and quality factor of the sensor have been plot below