Synergistic Plasmonic-Molecular Recognition in Porous Multicore@Shell Ag@UiO-66 Nanostructures toward Selective and Ultrasensitive SERS Detection of Salicylic Acid

Surface-enhanced Raman spectroscopy (SERS) is a powerful analytical technique capable of providing rapid and noninvasive molecular fingerprint identification through characteristic vibrational signatures. However, developing SERS platforms that enable both ultrasensitive and highly selective detection, particularly for molecules with intrinsically weak Raman responses, remains a major challenge, yet it is highly desirable for expanding the currently limited application scope of SERS. Herein, we introduce a novel plasmonic porous multicore@shell Ag@UiO-66 nanostructure that synergistically integrates the localized surface plasmon resonances (LSPRs) of Ag nanoparticles (NPs) with the selective molecular capture capability of the metal-organic framework UiO-66. The hybrid structure is fabricated via a simple solvent-induced self-assembly strategy, enabling in situ formation of UiO-66 around dispersed AgNPs at room temperature. The resulting architecture immobilizes multiple AgNP cores within a porous UiO-66 shell matrix, simultaneously stabilizing their plasmonic activity and generating abundant interparticle hotspots. More importantly, the UiO-66 shell provides molecular recognition toward salicylic acid (SA─a bioactive aromatic compound) through synergistic adsorption interactions involving bidentate coordination with Zr nodes, hydrogen bonding, and π-π interactions. This cooperative plasmonic-molecular recognition mechanism effectively concentrates target molecules at electromagnetic hotspots, leading to significantly enhanced SERS signals. As a result, the developed platform achieves highly selective and ultrasensitive detection of SA with a detection limit down to 1 nM, far surpassing that of traditional SERS substrates (∼1 μM). Furthermore, machine learning-assisted spectral analysis confirms the reliable and selective identification of SA in complex mixtures. This work demonstrates an effective strategy for developing MOF-enabled selective and ultrasensitive SERS platforms, opening new avenues for advanced molecular sensing applications.