Non-invasive and accurate access of biomarkers remains a holy grail of the biomedical community. Human eccrine sweat is a surprisingly biomarker-rich fluid which is gaining increasing attention. This is especially true in applications of continuous bio-monitoring where other biofluids prove more challenging, if not impossible. However, much confusion on the topic exists as the microfluidics of the eccrine sweat gland has never been comprehensively presented and models of biomarker partitioning into sweat are either underdeveloped and/or highly scattered across literature. Reported here are microfluidic models for eccrine sweat generation and flow which are coupled with review of blood-to-sweat biomarker partition pathways, therefore providing insights such as how biomarker concentration changes with sweat flow rate. Additionally, it is shown that both flow rate and biomarker diffusion determine the effective sampling rate of biomarkers at the skin surface (chronological resolution). The discussion covers a broad class of biomarkers including ions (Na(+), Cl(-), K(+), NH4 (+)), small molecules (ethanol, cortisol, urea, and lactate), and even peptides or small proteins (neuropeptides and cytokines). The models are not meant to be exhaustive for all biomarkers, yet collectively serve as a foundational guide for further development of sweat-based diagnostics and for those beginning exploration of new biomarker opportunities in sweat.

Paper-based immunoassays are becoming powerful and low-cost diagnostic tools, especially in resource-limited settings. Inexpensive methods for quantifying these assays have been shown using desktop scanners, which lack portability, and cameras, which suffer from the ever changing ambient light conditions. In this work, we introduce a novel approach of quantifying colors of colorimetric diagnostic assays with a smartphone that allows high accuracy measurements in a wide range of ambient conditions, making it a truly portable system. Instead of directly using the red, green, and blue (RGB) intensities of the color images taken by a smartphone camera, we use chromaticity values to construct calibration curves of analyte concentrations. We demonstrate the high accuracy of this approach in pH measurements with linear response ranges of 1-12. These results are comparable to those reported using a desktop scanner or silicon photodetectors. To make the approach adoptable under different lighting conditions, we developed a calibration technique to compensate for measurement errors due to variability in ambient light. This technique is applicable to a number of common light sources, such as sun light, fluorescent light, or smartphone LED light. Ultimately, the entire approach can be integrated in an "app" to enable one-click reading, making our smartphone based approach operable without any professional training or complex instrumentation.

Enhanced electroluminescent efficiency using a deoxyribonucleic acid ͑DNA͒ complex as an electron blocking ͑EB͒ material has been demonstrated in both green-and blue-emitting organic light-emitting diodes ͑OLEDs͒. The resulting so-called BioLEDs showed a maximum luminous efficiency of 8.2 and 0.8 cd/ A, respectively. The DNA-based BioLEDs were as much as 10ϫ more efficient and 30ϫ brighter than their OLED counterparts.

Wearable digital health devices are dominantly found in rigid form factors such as bracelets and pucks. An adhesive radio-frequency identification (RFID) sensor bandage (patch) is reported, which can be made completely intimate with human skin, a distinct advantage for chronological monitoring of biomarkers in sweat. In this demonstration, a commercial RFID chip is adapted with minimum components to allow potentiometric sensing of solutes in sweat, and surface temperature, as read by an Android smartphone app with 96% accuracy at 50 mM Na(+) (in vitro tests). All circuitry is solder-reflow integrated on a standard Cu/polyimide flexible-electronic layer including an antenna, but while also allowing electroplating for simple integration of exotic metals for sensing electrodes. Optional paper microfluidics wick sweat from a sweat porous adhesive allowing flow to the sensor, or the sensor can be directly contacted to the skin. The wearability of the patch has been demonstrated for up to seven days, and includes a protective textile which provides a feel and appearance similar to a standard Band-Aid. Applications include hydration monitoring, but the basic capability is extendable to other mM ionic solutes in sweat (Cl(-), K(+), Mg(2+), NH4(+), and Zn(2+)). The design and fabrication of the patch are provided in full detail, as the basic components could be useful in the design of other wearable sensors.

Monitoring aspects of human performance during various activities has recently become a highly investigated research area. Many new commercial products are available now to monitor human physical activity or responses while performing activities ranging from playing sports, to driving, and even sleeping. However, monitoring cognitive performance biomarkers, such as neuropeptides, is still an emerging field due to the complicated sample collection and processing, as well as the need for a clinical lab to perform analysis. Enzyme-linked immunosorbent assays (ELISAs) provide specific detection of biomolecules with high sensitivity (picomolar concentrations). Even with the advantage of high sensitivity, most ELISAs need to be performed in a laboratory setting and require around 6 h to complete. Transitioning this assay to a platform where it reduces cost, shortens assay time, and is able to be performed outside a lab is invaluable. Recently developed paper diagnostics provide an inexpensive platform on which to perform ELISAs; however, the major limiting factor for moving out of the laboratory environment is the measurement and analysis instrumentation. Using something as simple as a digital camera or camera-enabled Windows- or Android-based tablets, we are able to image paper-based ELISAs (P-ELISAs), perform image analysis, and produce response curves with high correlation to target biomolecule concentration in the 10 pM range. Neuropeptide Y detection was performed. Additionally, silver enhancement of Au NPs conjugated with IgG antibodies showed a concentration-dependent response to IgG, thus eliminating the need for an enzyme-substrate system. Automated image analysis and quantification of antigen concentrations are able to be performed on Windows- and Android-based mobile platforms.