The low-power sensing platform proposed by the Convergence project is foreseen as a wireless, low-power and multifunctional wearable system empowered by energy-efficient technologies. This will allow meeting the strict demands of life-style and healthcare applications in terms of autonomy for quasi-continuous collection of data for early-detection strategies. The system is compatible with different kinds of sensors, able to monitor not only health indicators of individual person (physical activity, core body temperature and biomarkers) but also the environment with chemical composition of the ambient air (NOx, COx, NHx particles) returning meaningful information on his/her exposure to dangerous (safety) or pollutant agents. In this article, we introduce the specifications and the design of the low-power sensing platform and the different sensors developed in the project, with a particular focus on pollutant sensing capabilities and specifically on NO2 sensor based on graphene and CO sensor based on polyaniline ink.
Current sensing solutions must combine an ultra-low energy consumption trend with high reliability. The challenge lies on a fine setting of the detection threshold with the assurance of a sufficient sensitivity. In this article, the uncertainty introduced on gas sensing applications by the inherent sensor noise is studied. A 1/f model of the electronic noise in polypyrrole-based ammonia (NH3) sensors is presented and used to estimate the intrinsic signal-to-noise ratio (SNR), giving an effective precision of 10.7 bits, i.e., down to 31.4 ppb in terms of NH3 concentration. No significant improvement in SNR is achieved by increasing the bias voltage and hence the power consumption.
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