Biomimetic and Biological Sensing Technologies for the Assessment of Water Contaminants

Biological sensors, including biosensors and biomimetic sensors, have gained significant attention in recent years due to their exceptional capabilities in detecting and monitoring pollutants in water environments. These sensors employ biological components such as bacteria, Fungi, enzymes, antibodies, microbial cells, or biomimetic materials to recognize and quantify specific contaminants to pathogens, with high sensitivity and selectivity. This review provides an in-depth analysis of the current state-of-the-art in biological sensor technology for water quality assessment. It discusses the underlying principles of biological sensing mechanisms and explores recent advances in sensor fabrication, including materials, immobilization techniques, and signal transduction methods. Moreover, the review examines the diverse applications of biological sensors in water monitoring, ranging from detecting chemical pollutants to assessing microbial contamination. Additionally, challenges such as stability, reproducibility, and field deployment are addressed, along with future directions for research and development to further enhance the performance and applicability of biological sensors in safeguarding water resources and public health. Here, we employed three models—SVM, logistic regression (LR), and random forest (RF)—to determine the biological and chemical components of water, such as bacteria, fungi, enzymes, antibodies, microbial cells, or biomimetic materials, in order to determine whether it is being utilized for drinking water. Because there are fewer datasets available for biological and chemical components, the dataset was created using machine learning (ML) models. Three machine learning models were used to calculate water pollutants percentages, with SVM proving most effective for irrigation and domestic water, and LR for drinking water, resulting in accurate water supply.