Nonplanar Pt(II) Molecular Electrocatalytic Interface for Selective Norepinephrine Sensing with Machine Learning-Assisted Quantification

Abstract Norepinephrine (NE) is an important catecholamine neurotransmitter and hormone, and abnormal fluctuations in its level are closely associated with various cardiovascular and neurological disorders. However, accurate determination of NE in complex biological samples remains analytically challenging because of interference from coexisting electroactive species. Herein, we report a mononuclear Pt(II) complex ((Rac)Pt3a) featuring a sterically induced nonplanar configuration as a molecular electrocatalytic interface for NE sensing. The results suggest that the sterically distorted molecular architecture, together with the cyano-substituted ligand framework and Pt–C/N coordination environment, contributes to the favorable interfacial electron-transfer behavior. Experimental results demonstrate that the (Rac)Pt3a-modified electrode exhibits a good linear response toward NE over the concentration range of 0.2–128 μM, with a low detection limit of 0.02 μM. Combined with outstanding stability and anti-interference performance in artificial urine, the proposed electrode is feasible for quantifying elevated NE concentrations in simulated urine matrices. Density functional theory (DFT) calculations further reveal a stronger adsorption interaction between NE and the Pt(II) interface compared to typical interfering species, providing insight into the observed selectivity. Furthermore, machine learning (ML) algorithms were adopted to fit electrochemical response signals for reliable NE quantification in complicated matrices, among which support vector machine (SVM) regression exhibited superior numerical prediction performance. While further validation using clinically characterized samples is required to assess potential clinical applicability, this work provides a proof-of-concept strategy for combining molecular interface engineering with data-driven analysis in electrochemical sensing.