Mechanistic Insights into N-Organic Electrochemical Probes for Heavy Metal Ions via DFT and Machine Learning

Nitrogen-containing organic compounds are widely used in sensing, membrane separation, and adsorption for heavy metal ion detection and removal, yet their selective recognition mechanisms remain unclear. Here, we systematically investigated the interactions between representative nitrogen-containing compounds and various heavy metal ions. The results reveal that Cu2+ preferentially coordinates with nitrogen atoms rather than oxygen or sulfur, independent of external electric fields. Supervised machine learning (accuracy = 0.9449) identified three key descriptors including LUMO energy, hardness, and softness, governing selectivity based on the hard–soft acid–base principle and molecular orbital theory. Guided by these insights, an electrochemical probe derived from oxidized p-aminothiophenol was developed, exhibiting high Cu2+ selectivity with a linear detection range of 1 nM–100 μM and sensitivity of 28.157 μA/(nM·cm2), confirming the proposed mechanism.