Abstract
In this work, a three-electrode fuel cell sensor is integrated with a miniaturized potentiostat (LMP91000) and a microcontroller (nRF51822) for selective detection of acetone. Elevated level of acetone in breath or sweat is an indication of systemic ketosis, hyperglycemia or type-I diabetes [1]. Rise of ketone concentrations leads to an increase in acid levels in the blood, called 'Ketoacidosis'. Ketoacidosis is a serious hyperglycemic condition in patients with type-I diabetes. Among the conventional sensing methods (e.g. breathalyzer for lung ketone analysis, chemical strips, laboratory blood test and urine test), continuous monitoring is a challenge [2]. Also, though real time detection of acetone from different biofluids is promising, interference from other biomarkers remains an issue [3]. To address the above in non-invasive detection, we have developed a statistical model and algorithm which enable separation of signals and eliminate interference from other associated biomarkers. In the developed sensing platform, our fabricated sensor comprises an electrolyte membrane, nafion and monel mesh, as electrodes. Catalytic oxidation of acetone occurs on the working electrode in presence of moisture while oxygen is reduced on the counter electrode. Applying a constant potential across the working and reference electrodes, current is measured between the working and counter electrodes. With the change of concentrations of acetone, the current varies linearly. From this study, it is observed that the sensor is capable to detect as low as 1 ppm of acetone with the sensitivity of 60 nA ppm-1 cm-2. The developed platform has been based on the pre-calibrated data to interpret precise measurement of acetone.
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