Optimized Sampling Rate for Voltammetry-Based Electrochemical Sensing in Wearable and IoT Applications

Abstract

The recent advancements in electrochemical measurements are guiding the development of new platforms for in situ point-of-care monitoring of human-metabolite, markers, and drugs. Despite this, the application of voltammetry-based sensing (VBS) techniques is still limited in wearable, portable, or Internet-of-Things (IoT) systems. In order to use VBS approaches to measure analytes in small and low-power electronic platforms for diagnostics, several improvements are required. For example, the definition of a method to achieve the right tradeoff between sample rate and sensing performance is still missing. To develop a method to define the best sampling rate, we present here an extensive analysis of experimental data to prove that it is feasible to detect drugs such as paracetamol by using staircase cyclic voltammetry or differential pulse voltammetry direct detection methods, with low sampling frequency. Our results prove that the proposed method helps the development of systems capable of discriminating the minimum pharmacology concentration of the metabolite under analysis with a massive reduction of the sampling frequency.