Programmable co-assembly of plasmonic nanostructures enabling self-calibrated SERS detection of nitrite

Reliable quantification of nitrite in complex food matrices using surface-enhanced Raman scattering (SERS) remains challenging due to signal drift and SERS substrate variability. Conventional internal standard (IS) strategies often occupy plasmonic hot spots, limiting analyte accessibility and compromising detection performance.

Herein, we report a size-complementary modular self-calibrated co-assembled (SCCA) SERS platform that integrates large gold nanoparticles (LAuNPs) with Au@Prussian blue (PB) core-shell nanospheres via interfacial self-assembly. The Au@PB nanospheres physically occupy the interparticle gaps of LAuNP arrays, providing both structural uniformity and a built-in Raman reference band at 2128 cm^-1 for ratiometric self-calibration without competing for plasmonic hot spots and compromising SERS sensitivity. The resulting SCCA substrate exhibits dense electromagnetic hot spots and good reproducibility, achieving quantitative nitrite detection through an acid-promoted S-nitrosation reaction with 2-thiobarbituric acid that generates a characteristic Raman band at 685 cm^-1. The method delivers a linear range of 0.15-2 mg/L, a limit of detection of 0.0368 mg/L, and excellent anti-interference tolerance in real food samples including cured fish, sausage, and pickled cucumber. The substrate retains 91.51% signal intensity after 60 days, confirming outstanding stability.

This work presents a universal strategy for constructing self-calibrated SERS substrates by spatially decoupling calibration elements from plasmonic hotspots. The SCCA design eliminates the intrinsic conflict between sensitivity and quantitative reliability, offering a modular and scalable approach for ratiometric SERS analysis.