Optimizing an Optical Cavity-Based Biosensor for Enhanced Sensitivity.

IEEE Sens J

Department of Electrical and Computer Engineering, Baylor University, Waco, TX 76798 USA.

Published: November 2023

The rapid advancement of biosensor technology has revolutionized healthcare, offering improved sensitivity, specificity, and portability. We have developed an optical cavity-based biosensor (OCB) as a promising solution due to its label-free detection, high sensitivity, real-time monitoring, multiplexing capability, and versatility. The OCB consists of an optical cavity structure (OCS), optical components, and a low-cost camera. The OCS is created by two partially reflective surfaces separated by a small gap, where the interaction between target analytes and immobilized receptors leads to a shift in the resonance transmission spectrum, caused by minute changes in the local refractive index (RI). In our previous work, we successfully detected these small changes with a simple OCS and cost-effective components using a differential detection method. Building upon these achievements, this study focuses on optimizing the OCS, improving the camera settings, and enhancing the differential detection approach. By increasing the reflectance of the surfaces and optimizing the optical cavity widths correspondingly, we achieved an improved limit of detection (LOD). We also investigated how the charge-coupled device (CCD) camera shutter time affects the LOD. Additionally, we introduced a new differential equation to further enhance the sensitivity of our system. Through these advancements, we could improve the LOD of the OCB by 7.2 times, specifically for an OCS with a cavity thickness of 9.881 m and a silver thickness of 46.87 nm. These findings not only contribute to the ongoing effort of optimizing the OCB, but also pave the way for the development of advanced point-of-care biosensors with enhanced detection capabilities.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11115385PMC
http://dx.doi.org/10.1109/jsen.2023.3317678DOI Listing

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