Development of a Smartphone-Based Biosensor for Rapid Enzyme Activity Detection

Thesis Abstract

Enzyme activity measurement plays a crucial role in clinical diagnostics, biomedical research, and environmental monitoring, yet existing laboratory-based techniques are often limited by their complexity, cost, and the requirement for specialized equipment and skilled personnel, thereby restricting their application in point-of-care and resource-limited settings. This study aims to develop a portable, user-friendly, and highly sensitive smartphone-based biosensor capable of rapid detection and quantification of enzyme activity. The specific objectives include designing and fabricating a biosensor interface compatible with various smartphone models, optimizing enzyme detection conditions, and evaluating the biosensor’s performance against standard laboratory assays. The research adopts a mixed-methods approach, combining empirical laboratory-based experimentation with quantitative evaluation techniques. The study engages a sample population of 150 enzyme samples extracted from biological fluids obtained from healthy volunteers, with purposive sampling used to ensure diversity in enzyme concentration ranges relevant for diagnostic purposes. Data collection involves the development of a nanomaterial-enhanced biosensor platform integrated with a custom mobile application which records enzymatic reactions via optical or electrochemical signals. The biosensor’s analytical performance is evaluated through calibration curves, sensitivity, specificity, limit of detection, and reproducibility tests, employing techniques such as regression analysis, ANOVA, and Bland-Altman plots. To ensure validity and reliability, the biosensor undergoes repeated measurements and inter-device comparisons. Key expected findings include the identification of optimal sensor configurations that yield high conversion efficiency and stable readings within a short incubation period, with the biosensor demonstrating a limit of detection comparable to conventional spectrophotometric methods (approximately 0.5 U/mL), and an assay turnaround time of under five minutes. The biosensor platform is anticipated to exhibit high reproducibility with intra- and inter-device coefficients of variation below 10%. The mobile application is designed to facilitate real-time data visualization, analysis, and cloud-based data management, contributing to improved diagnostic accuracy and operational convenience. The integration of nanomaterials such as graphene oxide or gold nanoparticles is expected to significantly enhance signal transduction and sensitivity. This research contributes substantively to existing knowledge by providing a novel, portable modality for enzyme activity detection that leverages the ubiquity and computational capacity of smartphones, thereby bridging the gap between laboratory diagnostics and point-of-care testing. It combines theoretical principles of biosensing, nanotechnology, and user-centered design under the theoretical framework of the Technology Acceptance Model (TAM), emphasizing usability and adoption potential. The study advances understanding of miniaturized biosensor design and paves the way for scalable, low-cost diagnostic tools tailored for diverse healthcare and environmental applications in remote or underserved regions. The main conclusion highlights that smartphone-based biosensors are feasible, reliable, and cost-effective alternatives to traditional laboratory methods for enzyme analysis. It recommends further research into broadening the range of detectable enzymes, integrating machine learning algorithms for data interpretation, and conducting field validations in real-world settings. Overall, the study advances the development of accessible diagnostic technologies that can improve health outcomes and environmental management, with implications for policy, healthcare delivery, and future biosensor innovation.