Enhancing the Sensitivity of a Class of Sensors: A Data-Based Engineering Approach

This paper's primary motivation is developing non-invasive glucose monitoring methods and devices by detecting hydrogen peroxide in fluids like tears, sweat, or saliva since it is a subproduct molecule of various biochemical processes directly correlated with glucose concentration. The availability of those mentioned tools could facilitate the rapid development of reliable and cheap sensors without the complications of using invasive methods, especially when blood samples are repeatedly required. Therefore, this work aims to lay the foundations for developing non-invasive and highly sensitive glucose detection methods as the ultimate proposal. Consequently, contributing to track and control the high glucose levels that cause significant health and economic problems in our society. Thus, this paper presents an approach for enhancing the hydrogen peroxide sensitivity in sensors nanostructured with nanowires. In contrast to most of the standard design methodologies, this scheme does not rely on phenomenological models but experimental data and statistical modeling. Firstly, for a sensor with a given design, the data obtained with cyclic voltammetry allows finding the potential at which the sensor's response is the highest for hydrogen peroxide's concentrations ranging from 0 to 20 millimoles. Secondly, after calculating the optimal potential, a linear regression correctly relates current density with the concentration, representing the sensor's sensitivity as such a linear model's slope. Finally, using planar (non- nanostructured) sensors as a benchmark, a statistical test allows concluding that the sensitivity is significantly higher for nanostructured sensors using gold and nickel self-supported nanowires arrays than planar.