Study of interference in multi-ions sensing for wearable applications

**** Master Project ****

Contacts: Sandro Carrara
                 
Ivan Ny Hanitra

 

Introduction

Potentiometric ion sensors are ubiquitous in healthcare monitoring and wearable physiology, enabling the detection of a wide range of electrolytes through biological fluids such as sweat. In sports applications, electrolyte monitoring allows the subject to assess how his body reacts to physical effort, it provides feedbacks on when to hydrate himself and when to have rest. Namely, following an intensive physical exercise, depletion of potassium and sodium could lead to dehydration, muscle cramps, or to more severe physiological dysfunctions. Calcium levels indicate bone mineral loss, and other ions are monitored in order to control electrolyte balance. All-solid-state sensors enable miniaturization and integration of array of potentiometric sensors into wearable systems. The ion-selective electrodes (ISEs) are functionalized with nanostructures [1] and ion-selective membranes, in order to ensure ion-to-electron transduction, and selectivity toward the target ion.

 

Fig. 1: Wearable potentiometric sensing for sports performance and healthcare monitoring [2]

 

One important challenge in multi-ions sensing comes from the impact of interference in sensor response. Interference arises from the sample matrix that contains background electrolytes that hinder detecting accurately very diluted amount of ions or trace metals present in sweat.

 

 

Project Aim

The master project aims investigating the impact of interference in the detection of the main ions present in sweat: potassium, sodium, calcium, or other trace metals (lead, lithium). All-solid-state sensors will be developed on commercial screen-printed electrodes, or on microfabricated multi-sensing platforms. The selectivity coefficients of the developed sensors will be assessed. Sensor response of array of ISEs will be measured within different sample compositions. The resulting dataset will be used to train data processing algorithms in order to increase the prediction accuracy of the concentration of the target ions of unknown samples.

 

 

Project Tasks

 

Prerequisites/desired competences:

 

 

References

[1] F. Criscuolo et al., “Highly-stable Li+ ion-selective electrodes based on noble metal nanostructured layers as solid-contacts”, Analytica Chimica Acta, vol. 1027, pp. 22-32, 2018.

[2] M. Parrilla, M. Cuartero, and G. A. Crespo, “Wearable potentiometric ion sensors,” TrAC - Trends in Analytical Chemistry, vol. 110, pp. 303–320, 2019.