✨Source 

Advanced Functional Materials 

📄Original Title

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

🔍Research

Idea In this work, a porphyrinic MOF‑on‑MOF heterostructure of Cu‑TCPP@Zn‑TCPP is designed. Together with Zn‑TCPP, a dual‑channel photoelectrochemical (PEC) sensing interface is fabricated. The interactions between sulfur‑containing molecules and the interface are translated into dual‑channel photocurrent fingerprints. Machine learning algorithms are adopted to deconvolve overlapping response signals for molecular recognition.

💡Highlights 

Establishment of a dual‑channel PEC fingerprint system based on MOF‑on‑MOF heterointerfaces; integration of interfacial material chemistry and data‑driven decoding to break the limitations of conventional single‑signal sensing; elucidation of the multi‑coupling mechanism between molecules and interfaces.

🧱Material Development

 (1) Material: layered porphyrinic Cu‑TCPP@Zn‑TCPP MOF‑on‑MOF heterostructure (2) Function: cathodic photoelectrochemical conversion, capturing characteristic interfacial responses of sulfur‑containing molecules and outputting dual‑path signals interpretable by machine learning

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17768014569 (Dr.Gao)

17761925168 (Dr. Zhang)

18013729810 (Dr. Yang)