Specialists in artificial olfaction electricity generation efficiency

##########

In academic literature systems based on (for example) a mass-spectrometer in combination with pattern recognition are sometimes presented as an ‘electronic nose’ application or artificial olfaction. However, in this section only relatively low-cost sensor technologies are discussed which are in principle suitable for bench-top or portable devices are discussed.

The requirement that a multi-dimensional measurement signal is generated excludes single detection elements used for example PID meters. This is often overcome by using an array of broadly sensitive elements with different sensitivities to important chemical compounds. gas utility As an electronic nose device is frequently exposed to volatile chemicals arrays of potentiometric sensors are not useable.

Conducting polymers are polymers which are either intrisically conducting or non-conducting types which have been ‘loaded’ with graphite. In the former type the conductivity may alter when exposed to volatiles. electricity magnetism and electromagnetic theory pdf In the latter case the graphite provides an electrical resistance path which can be measured very easily. When gas molecules associate with the polymer, it will swell thus breaking contact points between graphite particles and thus changes the resistance.

The relatively cheaper and more portable devices of Scensive Ltd (Bloodhound) and Smiths Detection (Cyranose models) frequently reported in academic literature are both based on conducting polymer arrays. All of the current devices are intended to strictly be used as laboratory instruments analogous to HPLC/GC’s and spectrometers. All of these devices need to be individually calibrated for a particular application.

This is the currently non-existing segment of low-cost, low power, mass-producible and mass-employable electronic noses. The target is to have a tiny, battery operated electronic nose with consistent unit-to-unit properties thus enabling ‘calibrate on one unit, apply to all units’ methods. The latter is required to make mass-production and mass-employability possible.

The workhorse and basic core of our eNose application devices is essentially a stand-alone module in its smallest form. b games virus This module measures 13.5×41.5 mm. The module contains a sensor with the supporting temperature control and measuring electronics, a microcontroller and ram and flash memory. The module also has a silicon serial number chip with a unique identifier that allows tracking and management of each manufactured module.

The microcontroller performs the temperature control and calibration of the sensor heater, measures the thermal cycles according to the settings, and buffers measurements until they can be unloaded to a central data store. electricity labs high school Numerous settings are stored persistently in the onboard ferro-static ram (FRAM). The module communicates over a serial bus system with a host.

As explained in the previous section, the redox reactions are temperature dependent. This allows the generation of much more specific patterns by a technique called temperature cycling. This is depicted in the left figure. In this example a sinusoidal temperature cycle is depicted, with the resulting sensor responses as function of the temperature variation in time. electricity consumption The time axis is the same for the upper and lower parts of the figure.Different substances exhibit strong responses at differing temperatures for the same chemical sensor type.

The elegance of this approach is that one acquires the relative temperature dependent redox reactions on a single sensor. The relative information is not device dependent as it is with arrays of sensors used at a static temperature. electricity and magnetism study guide 8th grade The crux of the pattern recognition problem is to get the actual temperatures right in all units/sensors. The variation in heater element production (even using MEMS fabrication) can result in temperature variations in the order of ±50 °C between two sensors of the same type.

The eNose company has developed a patented unique automatic temperature calibration technique. With this technique the standard deviation in inter-unit temperature variation is slightly less than 1°C. This is a requirement to allow calibration models developed on one unit to be used on another ‘as is’ without alteration or chemical tuning. 6 gas laws Data analysis

Data analysis is a crucial factor in any electronic nose device. The complex nature of the measurements combined with timing effects due to sampling mechanisms (such as exposure/recovery dynamics) creates multi-way data. When the thermal cycling technique of 2.3 is applied, each sensor will generate a 3 dimensional matrix of (exposure) time versus thermal cycle versus response value. When a combination of chemically different sensors is used to measure a sample, the chemistry of the sensor forms a fourth dimension in the multi-way data. This is illustrated in the left part of the figure below for a system with three sensors A,B and C.

Classical multi-variate pattern recognition techniques require that a single measured sample is represented as a 1-dimensional vector giving a 2-dimensional matrix for the data set as a whole. One dimension of the matrix represents the individual measurements, the other the elements of the vector belonging to each measurement. This is illustrated in the right part of the figure in which each row represents a single sample.

A naïve unfolding of the 3-way or 4-way data in order to derive a 1-dimensional vector is not feasible as the number of elements of the resulting vector would exceed the number of measurements/samples by orders of magnitude leading to unresolvable over-fitting. In many cases researchers by necessity resort to using only a very small subsection or even a single point of the measurement data and thus waste most of the available information. Thus a form of pre-processing is needed which generates 1-dimensional vectors for each sample while retaining the essential information needed for the application.

The eNose company has developed a highly sophisticated hybrid TUCKER3/PARAFAC algorithm that is tailored to this particular type of multi-way data. With this algorithm the full information contained in the measured data is preserved when generating 1-dimensional vectors for each measurement while at the same time elegantly removing redundancy, noise and scaling information. gas bike alley Software specificity