Executive Summary : | Analysis of the low abundance of toxic heavy metal ions in groundwater and agricultural soil samples remains challenging. several powerful analytical techniques have been used; however, the instrumentation has been complex, leading to time-consuming and costly analysis resulting in an incomplete assessment of pollutant distribution. Nanoparticle-based devices offer many advantages to this task by reducing analysis time, improving detection limits, and allowing real-time monitoring of relevant target species with high precision and accuracy. The long-term goal of this project is to develop a nanomaterial-based sensor that produces signals for specific heavy metal ions (here As³+, As⁵+) binding and involves quantifying the same toxic ions present in water and soil samples using logic-based concepts, models, and Machine Learning (ML) algorithm. A detailed investigation will be carried out to analyze arsenic in groundwater and agricultural soil using Artificial Intelligence-Enabled Nanosensing Technologies. |