Machine learning predicts selectivity of green synthesized iron nanoparticles toward typical contaminants: critical factors in synthesis conditions, material properties, and reaction process

Abstract

Green synthesized iron nanoparticles (FeNPs) have gained popularity in contaminant removal due to their low cost and environmentally friendly properties. However, a gap remains in understanding how synthesis conditions (SC), material properties (MP), and reaction processes (RP) affect their removal capacities on typical contaminants. This study utilizes advanced machine learning methods to explore complex dependencies in contaminant removal, achieving high predictive accuracies with R² rankings of XGBoost (0.9867) > RF (0.9749) > LightGBM (0.8545), and detailed SHAP analyses that elucidate the specific impacts of features. The model revealed that RP significantly influenced FeNPs' removal capacity. Both linear and SHAP analyses demonstrated that SC indirectly affected removal efficiency by influencing MP, thereby weakening their impact on FeNPs' removal capabilities due to their strong linear correlation. For all three contaminants (antibiotics, dyes and heavy metals), the removal capacity of FeNPs was primarily influenced by the C/Fe ratio and the type of plant present in the SC, as well as the pore volume of the MP. Antibiotics removal depends on antibiotic type and FeNPs' Fe content. The interaction time between Fe ions and plant extracts during SC and the specific surface area (SSA) of MP significantly influenced dyes removal, while the pore diameter in MP and the pH in RP were vital for heavy metals removal. MP impacts antibiotics removal more than SC, but SC's indirect effects are more significant for dyes and heavy metals. SHAP analysis clarified the importance and independent roles of specific features in the predictive modeling of removal efficiencies.