Tea contains a structurally diverse family of polyphenols, and subtle variations in their molecular architecture critically influence their biochemical activity. Yet resolving such closely related analogs remains an ongoing analytical challenge. Here, we report a single-molecule structure-response framework for identifying tea polyphenols using a phenylboronic acid-functionalized nanopore. Variations in hydroxylation pattern, galloylation, and stereochemistry produce reproducible ionic current fingerprints that correspond to defined diol binding sites, allowing for unambiguous differentiation of eight structurally similar catechins. With the aid of machine-learning-based classification, an identification accuracy of 96.9% is achieved. Beyond discrimination, this platform enables rapid polyphenol profiling from tea samples within 30 min, as well as single-molecule monitoring of polyphenol-metal interactions. By establishing a direct link between electrical signatures and specific binding states, this work extends nanopore sensing from molecular detection toward real-world applications and the mechanistic interrogation of complex natural products.
Li et al. (Thu,) studied this question.