An Artificial Sweating System for Sweat Sensor Testing Applications

A versatile artificial skin platform for sweat sensor development

Research targeting the development of on-body sensors has been significantly growing in recent years - an example is on-skin sweat sensing. However, the wide inter and intra person variability of skin characteristics make in vivo testing of these sensors and included materials such as skin adhesives difficult, which hampers especially the initial development phase of such wearables. Besides the development of wearable sweat sensors, companies developing deodorants, cosmetics, medical adhesives and wearable textiles now need to perform expensive human subjects testing with little control over the exact sweat mechanisms. Hence, there is a need for a realistic, adaptable and stable test platform, or artificial skin. We present a versatile artificial skin platform that faithfully recapitulates skin topography, active sweat pores, skin wetting behaviour and sweat rate, and that can be tuned to mimic the specifications of the targeted body location and sweating characteristics. The developed artificial skin is capable of generating sweat rates as low as 0.1 nL min^-1 pore^-1 and as high as 100 nL min^-1 pore^-1, spanning the whole range of physiological sweat rates. Specifically, the platform can be used for the development of sweat sensors for sedentary persons whose sweat rates are commonly lower than currently delivered by any other artificial skin platform.

Printed microfluidic sweat sensing platform for cortisol and glucose detection

Wearable sweat biosensors offer compelling opportunities for improved personal health monitoring and non-invasive measurements of key biomarkers. Inexpensive device fabrication methods are necessary for scalable manufacturing of portable, disposable, and flexible sweat sensors. Furthermore, real-time sweat assessment must be analyzed to validate measurement reliability at various sweating rates. Here, we demonstrate a "smart bandage" microfluidic platform for cortisol detection and continuous glucose monitoring integrated with a synthetic skin. The low-cost, laser-cut microfluidic device is composed of an adhesive-based microchannel and solution-processed electrochemical sensors fabricated from inkjet-printed graphene and silver solutions. An antibody-derived cortisol sensor achieved a limit of detection of 10 pM and included a low-voltage electrowetting valve, validating the microfluidic sensor design under typical physiological conditions. To understand effects of perspiration rate on sensor performance, a synthetic skin was developed using soft lithography to mimic human sweat pores and sweating rates. The enzymatic glucose sensor exhibited a range of 0.2 to 1.0 mM, a limit of detection of 10 μM, and reproducible response curves at flow rates of 2.0 μL min^-1 and higher when integrated with the synthetic skin, validating its relevance for human health monitoring. These results demonstrate the potential of using printed microfluidic sweat sensors as a low-cost, real-time, multi-diagnostic device for human health monitoring.

Recent Impact of Microfluidics on Skin Models for Perspiration Simulation

Skin models offer an in vitro alternative to human trials without their high costs, variability, and ethical issues. Perspiration models, in particular, have gained relevance lately due to the rise of sweat analysis and wearable technology. The predominant approach to replicate the key features of perspiration (sweat gland dimensions, sweat rates, and skin surface characteristics) is to use laser-machined membranes. Although they work effectively, they present some limitations at the time of replicating sweat gland dimensions. Alternative strategies in terms of fabrication and materials have also showed similar challenges. Additional research is necessary to implement a standardized, simple, and accurate model representing sweating for wearable sensors testing.

Aggregation kinetics of diesel soot nanoparticles in artificial and human sweat solutions: Effects of sweat constituents, pH, and temperature

Soot nanoparticles (SNPs) are airborne contaminants that could potentially penetrate skin, but their aggregation after contact with sweat may lower their health risks. This study investigated SNP aggregation kinetics in 4 artificial sweat standards and 21 human sweat samples. Effects of sweat inorganic (NaCl, Na₂HPO₄, and NaH₂PO₄) and organic (L-histidine, lactic acid, and urea) constituents, pH, temperature, and concentrations were examined. Results showed that SNP aggregation rates in 4 standards followed American Association of Textile Chemists and Colorists (AATCC) > British Standard (EN) > International Standard Organization (ISO) pH 5.5 > ISO pH 8.0, and could be described by the Derjaguin-Landau-Verwey-Overbeek (DLVO) theory. The aggregation rates increased with concentrations of SNPs, inorganic salts, L-histidine, and lactic acid, decreased with increasing pH and concentration of urea, and were weakly influenced by temperature. Systematic characterizations revealed SNP adsorption for organic sweat constituents. SNPs aggregated rapidly to ∼1000 nm in AATCC, but remained stable in ISO pH 8.0 and > 14/21 human sweat fluids over 20 min. The SNP aggregation rates correlated negatively with pH (r = -0.531*) and |ζ potential| (r = -0.464*) of human sweat samples. Sweat evaporation could promote aggregation of SNPs, hence lowering their potential harm via dermal exposure.

Sweat as a Source of Next-Generation Digital Biomarkers

Sweat has been associated with health and disease ever since it was linked to high body temperature and exercise. It contains a broad range of electrolytes, proteins, and lipids, and therefore hosts a broad panel of potential noninvasive biomarkers. The development of novel smartphone-based biosensors will enable a more sophisticated, patient-driven sweat analysis. This will provide a broad range of novel digital biomarkers. Digital biomarkers are of increasing interest because they deliver various relevant longitudinal health data. To date, investigations on digital biomarkers have focused on creating objective measurements of function. Sweat analysis using smartphone-based biosensors has the potential to provide initial noninvasive metabolic feedback and therefore represents a promising complement and a source for next-generation digital biomarkers. From this viewpoint, we discuss state-of-the-art sweat research, focusing on the clinical implementation of sweat in medicine. Sweat provides biomarkers that represent direct metabolic feedback and is therefore expected to be the next generation of digital biomarkers. With regard to its broad application in various fields of medicine, we see a clear need to evolve the internet-enabled field of sweat expertise: iSudorology.