Robotic platform for accelerating the high-throughput study of silver nanocrystals in sensitive/selective Hg2+ detection

Abstract

Colorimetric determination of trace mercury ions (Hg2+) with high reproducibility and sensitivity is urgently required for water safety and environmental monitoring. Herein, an automated robotic platform is described for high-throughput and controllable synthesis of colloidal silver nanocrystals (Ag NCs) and sensitive/selective colorimetric determination of Hg2+ in aqueous solutions. The predicted models of optical simulation are constructed as data-driven models to evaluate the quality of Ag NCs synthesized by a wet-chemical method. Based on an experimental database derived by optical characterization, the machine-learning (ML) model which covers multiple synthesis parameters is established for uniform synthesis of Ag NCs with controllable sizes matching the classical growth model. Moreover, the database of more than 1,200 valid samples established for the optical properties of Ag NCs is digitized to correlate with their average size. Optimized manual re-synthesis of high-quality Ag NCs demonstrates the practical feasibility and scalability of established models. The prepared Ag NCs can be used directly or modified with polymer ligands for quantitative detection of Hg2+ in the linear range from 0.01 to 200 µM with a detection limit of 3 nM. The strategy provides a scientific and effective way for the high-throughput study of nanoscale optical materials in chemical engineering and environmental monitoring.