Colorimetric Sensing of Mercury with Machine Learning Based on Nanozyme Activity of Chitosan Functionalized MXene/ZIF‐L Nanocomposite

Abstract Mercury is a highly toxic, global neurotoxin and carcinogen, causing significant risk to human health and environmental safety in small concentrations. This research paper explores the innovative, eco‐friendly, and inexpensive colorimetric detection of mercury ions (Hg ²⁺ ) by MXene/ZIF‐L (MXZL) nanocomposite with machine learning (ML). MXZL nanocomposite has a high surface area of 211.19 m ² g ⁻¹ , making it compatible for functionalization with chitosan. The sensing principle is based on the bio‐catalytic property of chitosan functionalized MXene/ZIF‐L (CS‐MXZL) that oxidizes 3,3′,5,5′‐tetramethylbenzidine (TMB) with H ₂ O ₂ and gives a blue colour. The chitosan‐captured Hg ²⁺ ions MXZL surface do not exhibit this property. The colorimetric assay with nanozyme activity achieves a limit of detection (LOD) of 50 nM, analyzed via a UV−vis spectrophotometer. This is integrated with ML models, namely Linear Regression, Decision Tree Regression, Support Vector Regression (SVR), k‐NN, and XGBoost, to accurately estimate analyte concentrations. The k‐NN model demonstrated superior predictive accuracy, recording Mean Absolute Error (MAE) of 0.1200, Root Mean Square Error (RMSE) of 0.1350, and the Coefficient of Determination (R ² ) score of 0.9693. Hence, the developed ML‐enhanced colorimetric platform offers a cost‐effective, portable solution for mercury detection, advancing efforts in environmental sustainability and hazardous materials management.