Machine‐Learning‐Decoded Dual‐Channel Photoelectrochemical Fingerprinting at Porphyrinic MOF‐on‐MOF Heterointerfaces for Intelligent Molecular Profiling

Accurate discrimination of structurally analogous sulfur‐containing molecules with overlapping photoelectrochemical (PEC) responses remains a major challenge for conventional PEC sensors that rely on one‐dimensional photocurrent readouts. Herein, we develop a dual‐channel PEC fingerprinting platform that pairs Zn‐TCPP with a rationally constructed Cu‐TCPP@Zn‐TCPP MOF‐on‐MOF heterostructure and integrates their paired responses with machine‐learning (ML) algorithms. Unlike traditional single‐signal approaches, Zn‐TCPP and the layered Cu‐TCPP@Zn‐TCPP heterointerface serve as two distinct transduction interfaces that convert molecule‐dependent interfacial interactions into dual‐interface PEC fingerprints. Experimental and theoretical analyses reveal that these discriminative responses arise from coupled molecular and interfacial effects. These effects include molecular‐size‐dependent interfacial accessibility, frontier‐orbital‐influenced hole consumption, and, in the case of glutathione (GSH), Cu–S‐related modulation of the cathodic oxygen‐reduction‐related electron‐consumption pathway. These differences in molecular–interface interactions are used to establish a paired PEC fingerprint dataset. Subsequent ML‐assisted decoding, particularly with an artificial neural network (ANN), resolves the overlapping response patterns. The platform achieves high‐accuracy classification of five representative sulfur‐containing molecules, discrimination among predefined GSH concentration levels, and recognition of binary mixtures and spiked real‐water matrices. This work integrates MOF‐on‐MOF heterointerfacial chemistry with data‐driven fingerprint decoding, providing an interface‐design strategy for intelligent recognition of sulfur‐containing molecules in complex aqueous environments.