Design and evaluate a novel enzymatic biosensor for detecting environmental pollutants

Thesis Abstract

Environmental pollution poses a significant threat to ecosystems and human health, necessitating the development of rapid, cost-effective, and sensitive detection methods for environmental contaminants. Conventional analytical techniques such as chromatography and spectrometry, although highly accurate, are often constrained by their high cost, technical complexity, and limited on-site applicability. This research aims to design, optimize, and evaluate a novel enzymatic biosensor capable of detecting common environmental pollutants such as heavy metals (lead, cadmium), phenolic compounds, and pesticides with high specificity and rapid response times. The specific objectives include selecting appropriate enzyme biorecognition elements, integrating these into a portable sensor platform, and assessing the biosensor's analytical performance in aqueous samples collected from varied environmental sites. The research adopts a mixed-methods experimental design, combining material synthesis, electrochemical characterization, and field sampling approaches to comprehensively evaluate biosensor performance. The study's population encompasses environmental water samples from 15 different industrial, agricultural, and urban locations within a defined geographical region. A sample size of 150 water samples is targeted, with stratified sampling employed to ensure representative pollutant levels across different sources. Enzymes such as laccase, tyrosinase, or cholinesterase are immobilized onto modified screen-printed electrodes using glutaraldehyde cross-linking, optimized through response surface methodology. The biosensor's analytical performance—including sensitivity, detection limit, selectivity, reproducibility, and stability—is characterized via cyclic voltammetry, chronoamperometry, and electrochemical impedance spectroscopy. Data quality is ensured through calibration curves generated for each target pollutant, with validation against standard laboratory methods such as inductively coupled plasma mass spectrometry (ICP-MS) for heavy metals and gas chromatography-mass spectrometry (GC-MS) for organic pollutants. Statistical analysis is performed using regression analysis to evaluate sensor calibration, ANOVA to compare performance across different pollutants and environmental conditions, and receiver operating characteristic (ROC) curves to assess diagnostic accuracy. The experimental results are complemented by a theoretical framework grounded in enzyme kinetics and surface chemistry theories, alongside the application of the Michaelis-Menten kinetic model to interpret enzymatic responses. Expected findings include high sensitivity and rapid detection times (under 5 minutes), with detection limits below environmental safety thresholds for the targeted pollutants. The biosensor is anticipated to demonstrate high selectivity, reproducibility (coefficient of variation below 5%), and operational stability over 30 days. The overall analytical performance is expected to meet or surpass existing biosensing platforms, providing a promising alternative for real-time environmental monitoring. It is also anticipated that the biosensor's development will contribute to existing knowledge by elucidating enzyme immobilization techniques optimized for environmental sensing matrices and expanding the application scope of electrochemical biosensors in ecological contexts. The study contributes novel insights into enzyme-based biosensor design tailored for environmental applications, emphasizing feasibility for field deployment. It underscores the potential of integrating nanomaterials and surface modification strategies to enhance biosensor performance. The main conclusion is that the fabricated enzymatic biosensor provides a reliable, sensitive, and rapid detection tool suitable for on-site environmental monitoring, with implications for regulatory agencies, environmental scientists, and community stakeholders. Based on findings, recommendations include scaling up biosensor production, integrating wireless data transmission for remote monitoring, and conducting longitudinal field studies to evaluate long-term performance. Future research should focus on multiplexing capabilities to simultaneously detect multiple pollutants, thus broadening environmental surveillance capacity.