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Deciphering structurally similar thiol analogues is vital for disease diagnosis, yet conventional one‐to‐one sensing strategies are intrinsically bottlenecked by signal cross‐interference. Herein, we report an in situ reconstructed interfacial oxygen vacancies engineering strategy to prepare vacancy‐rich Pt‐CeO 2 nanozymes for intelligent thiol differentiation. By combining incipient wetness impregnation with subsequent H 2 reduction, isolated Pt single atoms were transformed into uniformly dispersed Pt clusters on CeO 2 nanorods, simultaneously inducing abundant oxygen vacancies and dynamically coupled Pt‐CeO 2 interfaces. The optimized nanozyme exhibited significantly enhanced peroxidase‐like catalytic activity over its single‐atom state or directly deposited cluster counterparts. Crucially, the boosted catalytic kinetics significantly amplified the subtle response discrepancies among different thiols, yielding highly resolved time‐dependent fingerprints. By integrating these distinct kinetic profiles with multivariate statistical analysis and predictive machine learning models, accurate differentiation of thiol species, complex multicomponent mixtures and clinical serum samples was achieved. This work not only provides insights into reconstruction‐induced interfacial defect engineering but also establishes an effective framework for intelligent kinetic sensing in early disease screening.
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Lu et al. (2026) studied this question.
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