Design, implement, and evaluate a modular enzyme biosensor for real-time glucose monitoring

Thesis Abstract

The rapid growth in personal healthcare monitoring underscores the need for reliable, real-time, minimally invasive glucose sensing technologies that can operate across diverse environments. Current glucose monitoring devices often rely on fixed, single-function sensing architectures that limit adaptability, precision, and response time in fluctuating physiological conditions. This study aims to design, implement, and evaluate a modular enzyme biosensor capable of real-time glucose monitoring by leveraging interchangeable catalytic modules and stable transducer interfaces to enhance sensitivity, selectivity, and rapid signal transduction in both in vitro and simulated in vivo contexts. The specific objectives are (1) to develop a modular biosensor platform integrating glucose oxidase as the primary biocatalytic module with configurable mediator- and transducer modules; (2) to optimize the electrochemical and enzymatic interface to achieve a linear response from 2 to 25 mM glucose with a limit of detection below 0.5 mM; (3) to evaluate sensor stability, selectivity against common interferents (uric acid, ascorbate, acetaminophen), and repeatability over a 30-day period; (4) to implement real-time data acquisition and processing using embedded microcontroller hardware and a cloud-based analytics pipeline; and (5) to validate performance in human serum simulants and calibrated whole-blood-mimicking phantoms, comparing results against reference laboratory-grade methods. The methodology adopts an explanatory sequential mixed-methods design anchored in the Technology Acceptance Model and Systems Engineering theory to guide modular integration and user-centric performance criteria. A multi-phase approach will be employed Phase 1 involves the design and fabrication of a modular sensor cartridge with interchangeable enzyme and mediator modules using screen-printed carbon electrodes. Phase 2 establishes calibration curves and analytical performance metrics in phosphate-buffered saline (PBS) and serum simulants (n=6 replicates per condition). Phase 3 assesses specificity and interference using solutions containing physiological levels of uric acid, ascorbate, and acetaminophen (n=3 concentrations per interferent). Phase 4 integrates the biosensor with a microcontroller unit (MCU) and a wireless data link; data will be processed through regression analytics to model glucose concentration as a function of current response, applying linear and multiple-linear regression (R2, RMSE) and Bland-Altman analysis for method agreement. Phase 5 conducts preliminary in vitro validation with simulated whole-blood matrices (n=10 samples) to compare sensor outputs against the reference hexokinase–glucose-6-phosphate dehydrogenase assay. Analytical techniques will include precision and accuracy assessments, ANOVA to compare performance across modular configurations, and regression analysis to determine calibration stability over 8 weeks. The study will also perform a stability analysis of the modular interfaces by accelerated aging tests at 40°C for 14 days. Expected findings include demonstration that modular configuration enhances response speed and adaptability to environmental changes, achieving a dynamic range suitable for physiological glucose fluctuations, with improved selectivity against common interferents and lower drift over time compared to non-modular controls. The integration of MCU-based data processing and cloud analytics is anticipated to yield real-time glucose readouts with clinically acceptable accuracy (mean absolute relative difference within ±10%) and robust performance in serum-mimicking environments. The research is expected to reveal that modular transducer interfaces contribute significantly to signal fidelity, while the enzymatic module governs specificity and stability under variable temperature and pH. The study contributes to knowledge by establishing a generalizable design framework for modular enzymatic biosensors with real-time readout capabilities, enabling rapid reconfiguration for targeted analytes and adaptive response characteristics. It advances understanding of how modularity influences analytical performance, stability, and data handling in wearable and point-of-care glucose monitoring systems. The main conclusion is that a modular enzyme biosensor, coupled with integrated real-time processing, can provide accurate, rapid, and reliable glucose monitoring in diverse settings, with potential for personalization and rapid reconfiguration for related metabolite sensing. Recommendations include pursuing in vivo validation in controlled clinical trials, exploring alternative enzyme–mediator pairs for extended dynamic range, and optimizing power consumption for continuous operation in wearable formats.