Machine learning framework for selective and sensitive metal ion sensing with nitrogen-doped graphene quantum dots heterostructure

Abstract

This study introduces a machine learning (ML) framework to optimize photodetector performance for sensor applications. Using the data from the fabricated photodetector with the heterostructure of nitrogen-doped graphene quantum dot and gold nanoparticles (Au@N-GQDs), various supervised ML models (more than 20 models) are trained and tested for the selection and refinement of the most effective algorithm for our work. Depending on the best-performed ML model, the optimized working wavelength of the photodetector is found for the detection of metal ions. Remarkably, the ML-based sensor shows a high level of selectivity and sensitivity in nM level towards Fe³⁺ ions in Brahmaputra river water. A strong alignment between model predictions and experimental outcomes validates the efficacy of the proposed ML-based framework. Moreover, data visualization techniques such as heatmaps, classification algorithms, and confusion matrices are introduced to identify the trends in the database. The mechanistic insight of the sensor performance towards Fe³⁺ ion sensing is further explained with heatmap analysis and experimental verification, which emphasizes the role of photo-induced charge transfer and Fe–O bond formation between metal ions and Au@N-GQDs due to the high electron affinity of Fe³⁺ ions.