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In stressful situations, concentrations of various molecules in the human body shift in response to the stressor. These molecules are measurable indicators of stress and are therefore called stress biomarkers. In many stress conditions, such as in overtraining syndrome, early detection of these biomarkers is highly important as the conditions are often not fully reversible. Early detection of the stress symptoms could be achieved with wearable sensors that would continuously monitor health information from different body fluids, such as sweat, urine, saliva, tears and blood. Compared to more conventional electrochemical or optical methods, plasmonic sensing could offer higher sensitivity, better stability and faster data collection while enabling implementation to compact devices. In this work, a sensor chip, based on grating-coupled surface plasmon resonance, is proposed for stress biomarker detection. In this work, we show a highly sensitive grating-based SPR sensor working in concert with a tunable laser within the wavelength range of 1528-1565 nm. The SPR sensor was designed using COMSOL Multiphysics software and was fabricated by means of UV nanoimprinting lithography. The implemented SPR sensor shows sensitivity close to 1200 nm/RIU, with a figure of merit (a ratio between the sensitivity and the full width at half maximum of the SPR dip) exceeding 400. The experimental results are strongly in agreement with COMSOL simulations. Such impressive characteristics of the fabricated sensor are among the best reported in the literature. The sensitivity of the chip was tested with two different stress-related biomarkers: glucose and lactate. With the tested range of 0 to 1.1 M, in the current version of the setup, without a receptor layer, the detection limits of glucose and lactate were 5.9 and 36.9 mM, respectively, which are close to the physiological ranges of these analytes in body fluids. The detection limit can be further improved with the sensor functionalization, thermal stabilization and mechanical isolation. When integrated into a wearable device, this approach has a potential in future healthcare applications, such as in continuous stress monitoring.
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