Design and evaluation of a biosensor for rapid enzyme activity detection

Thesis Abstract

In the realm of clinical diagnostics, environmental monitoring, and bioprocessing, the rapid and accurate assessment of enzyme activity remains a critical challenge, often hindered by the limitations of existing methods such as spectrophotometry and chromatography, which are time-consuming, require complex sample preparation, and lack portability. This study aims to develop, implement, and evaluate a novel biosensor designed for the swift detection of enzyme activity, specifically targeting key enzymes such as lactate dehydrogenase and alkaline phosphatase, to facilitate real-time monitoring in diverse settings. The primary objectives are to design an electrochemical biosensor utilizing nanostructured conductive materials for enhanced sensitivity, to optimize the immobilization of enzyme-specific recognition elements, and to evaluate the sensor’s performance in terms of sensitivity, specificity, response time, reproducibility, and stability. The research adopts a mixed-methods approach, combining experimental design with quantitative analytical techniques to ascertain sensor efficacy. A purposive sample comprising 50 clinical plasma samples and 50 environmental water samples was collected from local hospitals and water treatment facilities, respectively. Biosensor fabrication involved material synthesis, including the integration of graphene oxide and gold nanoparticles with specific recognition elements such as enzyme-specific aptamers, on a flexible screen-printed electrode platform. The sensor’s analytical performance was characterized using electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), and differential pulse voltammetry (DPV). Data collection entailed systematic calibration using standard enzyme solutions across a concentration range of 0.1 to 1000 U/mL. The validity and reliability of the measurements were established through repeated testing and comparison with standard spectrophotometric assays (e.g., enzyme-linked immunosorbent assay, ELISA). Data were analyzed quantitatively using regression analysis to determine the calibration curve, analysis of variance (ANOVA) to compare performance metrics, and receiver operating characteristic (ROC) analysis to evaluate diagnostic accuracy. Expected findings suggest that the biosensor can detect enzyme activity within 30 seconds, exhibiting a limit of detection below 0.1 U/mL with high specificity for target enzymes, demonstrated by negligible interference from structurally similar biomolecules. The sensor is anticipated to show reproducibility with a coefficient of variation below 5% across multiple runs, and stability retaining over 90% of initial activity after 30 days of storage under controlled conditions. These results are expected to confirm that the biosensor surpasses conventional detection methods in terms of speed, cost-efficiency, portability, and ease of use. The contribution to existing knowledge involves advancing bioelectronic sensing technology by integrating nanomaterials with enzyme recognition elements, supported by a theoretical framework grounded in the Michaelis-Menten kinetic model and the theory of electrochemical transduction. The study also provides a scalable model for developing rapid diagnostic tools adaptable to point-of-care and field settings. The main conclusion emphasizes that the biosensor represents a significant improvement in enzyme activity detection, offering rapid, reliable, and cost-effective analysis suitable for clinical, environmental, and industrial applications. It is recommended that future work focus on miniaturization, real-time data transmission, and multiplexed detection to broaden application scope. Additionally, further research should explore long-term field deployment and integration with digital health platforms to enhance real-time monitoring and decision-making processes. This study ultimately contributes foundational knowledge that paves the way for next-generation biosensing technologies capable of transforming enzyme monitoring practices globally.