Sweat provides direct information of the real-time emotional and cognitive state of the subject, with applications ranging from situational awareness and mission effectiveness of armed forces to disease diagnosis for clinicians. Development of a broad class of human performance monitoring devices to quantify sweat biomarkers necessitates non-invasive, real-time monitoring of ultra-low concentrations (μM to fM) of hormones, proteins, and neurotransmitters. Field effect transistors are the predominant sensor approach whereby the gate electrode is modified with a selective bio-recognition element (BRE). However, FETs have diminished sensitivity in high ionic strength environments associated with sweat. Alternatively, BRE-modified photonic integrated circuits (PICs) have high sensitivity in high ionic strength fluids, low cost at the manufacturing scale, and enable a number of novel device concepts to achieve ultra-low levels of detection. One major technological challenge is to predict the limit of detection (LoD), or sensor response function, for a particular PIC geometry in a microfluidic chamber. LoD is highly dependent on analytic capture efficiency, fluid dynamics and affinity, analyte/light interaction, and analyte concentration. This work presents finite element simulations to emulate microfluidic BRE sweat sensors and provide a predictive limit of detection for different sensing structures or elements. Specifically, the optimum mass transfer and kinetics for sensing approaching single molecule detection is discussed, including flow characteristics, biomarker size, adsorption and desorption kinetics, and sensor geometry. Key metrics include capture efficiency (molecules being captured over molecules entering channel ), time to reach steady state, and temporal adsorption site occupancy to predict PIC system LoD. It is found that these systems are kinetically controlled, with capture efficiencies remaining below 1% even for kads/kdes ratios of 1010 . The need for adsorption kinetics measured for flow systems instead of stationary fluid systems is stressed, as these parameters are what need to be optimized to greatly increase analyte capture.
Jonathan Harris, Justin Bickford, Pak Cho, Matthew Coppock, Mikella Farrell, Ellen Holthoff, and Erin L. Ratcliff, "Approaching single molecule sensing: predictive sweat sensor design for ultra-low limits of detection," Proc. SPIE 11010, Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XX, 110100F (Presented at SPIE Defense + Commercial Sensing: April 15, 2019; Published: 17 May 2019); https://doi.org/10.1117/12.2518543.
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