Malkoc, Aldin

This work represents a preliminary proof-of-concept design and verification of a 3D-printed glucose biosensor. The proof of concept presented is the first example of glucose dehydrogenase sensor fabricated by a 3D-printer while maintaining similar features to current lab-industry standards. The sensor was verified to detect physiological glucose concentrations between 0 and 400 mg/dL with a linear coefficient as high as .97. This study showed that it was possible to use 3D-printed technology to create a biosensor sensitive to glucose detection. As availability and functionality of 3D-printers expands, this technology has the potential to be an option for diabetes management. This preliminary study shows that the 3D-printed sensor platform holds promise for sensitive glucose detection.

Background: Currently, glycemic management for individuals with diabetes mellitus involves monitoring glucose only, which is insufficient as glucose metabolism involves other biomarkers such as insulin. Monitoring additional biomarkers alongside glucose has been proposed to improve glycemic control. In this work, the development of a rapid and label-free insulin biosensor with high sensitivity and accuracy is presented. The insulin sensor prototype also serves as a prior study for a multimarker sensing platform technology that can further improve glycemic control in the future. Methods: Electrochemical impedance spectroscopy was used to identify an optimal frequency specific to insulin detection on a gold disk electrode with insulin antibody immobilized, which was accomplished by conjugating the primary amines of insulin antibody to the carboxylic bond of the self-assembling monolayer on the gold surface. After blocking with ethanolamine, the insulin physiological concentration gradient was tested. The imaginary impedance was correlated to insulin concentration and the results were compared with standard equivalent circuit analysis and correlation of charge transfer resistance to target concentration. Results: The optimal frequency of insulin is 810.5 Hz, which is characterized by having the highest sensitivity and sufficient specificity. The lower limit of detection was 2.26 ρ M which is comparable to a standard and better than traditional approaches. Conclusion: An insulin biosensor prototype capable of detecting insulin in physiological range without complex data normalization was developed. This prototype will be the ground works of a multimarker platform sensor technology for future all-in-one glycemic management sensors.