working conditions. [12-15] More precisely, in human sweat, the concentration of Na + changes between 10 × 10 −3-100 × 10 −3 m, it is sweat rate-dependent and associated with dehydration. [3,14,16] K + concentration level ranges between 1 × 10 −3-18.5 × 10 −3 m with a sweat rate-independent partitioning. [3,16] Also, pH can strongly vary between 3-8 units, [3] with changes associated with dehydration and muscle fatigue. [14,15] For the next generation of wearable ion sensors, key requirements include mechanical flexibility, simple array patterning for multi-parametric analysis, and microfluidics integration for continuous sampling. [17-23] Conventionally, selective ion measurements are performed using potentiometric two-electrode systems, in which the potential drop between an Ion Selective Membrane (ISM) and a reference electrode is measured. [21,24,25] However, standard potentiometric sensors are difficult to integrate into an array configuration in a microfluidic platform, due to their high output impedance, [18] and the difficult miniaturization of a stable reference electrode. [26-28] Organic electrochemical transistors (OECTs) are an interesting alternative to conventional potentiometric sensors, overcoming some of these limitations. [29] The OECTs are three-terminal devices (drain, source, and gate), with the source and the drain electrodes connected by a conducting polymeric channel. The organic channel is based on conjugated polymer-polyelectrolyte blends, such as the mainly used poly(3,4-ethylene dioxythiophene):polystyrene sulfonate (PEDOT:PSS). [22,30-32] This active material enables mixed ionic and electronic charges interaction, with ionic conduction provided by the PSS polyelectrolyte chains and electronic conduction by nanometric-sized PEDOT crystallites. [33-35] In the presence of an electrolyte and once a positive gate voltage is applied, the dissolved cations are injected into the PEDOT:PSS channel. The cations compensate electrostatically the sulfonate anions of the PSS phase, subsequently lowering the drain current (hole de-doping) in the bulk of the layer. [2,29] This technology, without the need for a reference electrode, allows a facile miniaturization. [28,36] Moreover, the mechanical flexibility of the PEDOT:PSS channel, [22,37] the compatibility with digital manufacturing such as inkjet printing, [38-41] and the very low output impedance, [18] make OECTs promising candidates for Organic electrochemical transistors (OECTs) show remarkable promise as biosensors, thanks to their high signal amplification, simple architecture, and the intrinsic flexibility of the organic material. Despite these properties, their use for real-time sensing in complex biological fluids, such as human sweat, is strongly limited due to the lack of cross-sensitivity and selectivity studies and the use of rigid and bulky device configurations. Here, the development of a novel flexible microfluidics-integrated platform with an array of printed ion-selective OECTs enables multi-ion detection in a wearable fashion. T...

Simultaneous access to the different biofluids enables an accurate analysis of multiple analytes, leading to a precision diagnosis and medication. Additionally, establishing a relationship between various markers at different biofluids...

No abstract

Sweat secreted by the human eccrine sweat glands can provide valuable biomarker information during exercise in hot and humid conditions. Real-time noninvasive biomarker recordings are therefore useful for evaluating the physiological conditions of an athlete such as their hydration status during endurance exercise. In this work, we describe a platform that in-cludes different sweat biomonitoring prototypes of cost-effective, smart wearable devices for continuous biomonitoring of sweat during exercise. One prototype is based on conformable and disposable soft sensing patches with an integrated multisensor array requiring the integration of different sensors and printed sensors with their corresponding functionalization protocols on the same substrate. The second is based on silicon based sensors and paper microfluidics. Both platforms integrate a multi-sensor array for measuring sodium, potassium, and pH in sweat. We show preliminary results obtained from the multi-sensor prototypes placed on two athletes during exercise. We also show that the machine learning algorithms can predict the percentage of body weight loss during exercise from biomarkers such as heart rate and sweat sodium concentration collected over multiple subjects.