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Development of High Performance Breath Acetone Sensing DeviceAuthor: Anand Thati Date: 2017-03-31 Report no: IIIT/TH/2017/22 Advisor:Shubhajit Roy Chowdhury,Tapan Kumar Sau AbstractResearchers have demonstrated that breath acetone is an effective biomarker of Type 2 diabetes which is the most common form of diabetes. Diabetes treatment requires intermittent monitoring of the blood glucose levels in the patients. Conventional way of detecting glucose levels is through invasive technique which involves pricking the finger and collecting blood sample. This is not only painful and blood consuming but also time consuming and expensive. Therefore, there has been a great demand for the non-invasive techniques of blood glucose determinations in the commercial market. Researchers have been attempting to develop a number of non-invasive techniques where the glucose can be measured by different methods outside the body, without puncturing the skin or without taking the blood sample. However, out of various non-invasive techniques developed, many of them are not suitable for real-time, point-of-care, and routine uses because they involve high cost and require time-consuming and complicated sample pretreatment, large space and infrastructure for operations. Detection of breath acetone can be a rapid, noninvasive, and patient compliant viable alternative to the conventional methods of blood glucose determination. Acetone in the breath appears due to increased lipolysis. The breath acetone levels are found to be less than 0.9 ppm in healthy people and more than 1.8 ppm in diabetes patients. This has led researchers to develop alternative acetone sensors, especially semiconducting metal oxides (SMOs)-based chemo-resistive sensors due to their several favorable attributes. However, achieving the target selectivity, sensitivity, fast response and recovery, and low detection limit have often been very challenging for the SMO-based sensors, especially for the sample like exhaled breath that consists of plethora of gases including a large quantity of water vapor and carbon dioxide. The thesis work involves designing an embedded system to measure blood glucose levels from breath acetone. First, it involves testing with the existing SMO-based acetone sensor followed by reducing the effects of other parameters by using Artificial neural network model. We observed that we still need improvements in various fronts of sensor design. Sensors for breath acetone monitoring must show high sensitivity and selectivity and very small response and recovery times, in addition to a good reversibility and stability. Exhalation is a very fast process ( ≈ 3 s) and a small quantity of acetone (≈0.1 - 10 ppm) is present in the exhaled breath. Therefore, detection of acetone below 10 ppm is of great significance in breath acetone sensing. Towards this goal, we developed highly efficient SMO-based acetone sensors made of Pd nanoparticle-loaded nanostructured SnO 2 particles (Pd@SnO 2 ). The sensors were prepared by using simple chemical reduction and sol-gel synthesis methods with no requirements for dispersingagents or complicated synthesis and fabrication steps. Our Pd@SnO 2 sensors exhibited very high sensor response with small response and recovery time at relatively low operating temperature. Further, the sensors exhibit excellent reversibility, selectivity over ethanol and noninterference from water vapor and CO 2 , two major constituents of the exhaled breath. These parameters satisfy the requirements of a real-time breath acetone sensor. Full thesis: pdf Centre for VLSI and Embeded Systems Technology |
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