A Novel SERS Silent-Region Signal Amplification Strategy for Ultrasensitive Detection of Cu.

Due to its unique molecular fingerprinting capability and multiplex detection advantages, surface-enhanced Raman scattering (SERS) has shown great application potential in the field of biological analysis. However, the weak signal intensity and large background interference significantly limited the application of SERS in biosensing and bioimaging. Loading a large amount of Raman molecules with signal in the silent region on the hotspots of the electromagnetic field of the SERS substrate can effectively avoid severe background noise signals and significantly improve the signal intensity, making the sensitivity and specificity of SERS detection remarkably improved. To achieve this goal, a new SERS signal-amplification strategy is herein reported for background-free detection of Cu by using Raman-silent probes loaded on cabbage-like gold microparticles (AuMPs) with high enhancement capabilities and single-particle detection feasibility. In this work, carboxyl-modified AuMPs were used to enable Cu adsorption via electrostatic interactions, followed by ferricyanide coordination with Cu to introduce cyano groups, therefore generating a stable SERS signal with nearly zero background signals owing to the Raman-silent fingerprint of cyano at 2137 cm. Based on the signal intensity of cyano groups correlated with Cu concentration resulting from the specific coordination between Cu and cyanide, a novel SERS method for Cu detection with high sensitivity and selectivity is proposed. It is noted that benefiting from per ferricyanide possessing six cyano groups, the established method with the advantage of signal amplification can significantly enhance the sensing sensitivity beyond conventional approaches. Experimental results demonstrated this SERS sensor possesses significant merits towards the determination of Cu in terms of high selectivity, broad linear range from 1 nM to 1 mM, and low limit of detection (0.1 nM) superior to other reported colorimetric, fluorescence, and electrochemical methods. Moreover, algorithm data processing for optimization of SERS original data was further used to improve the SERS signal reliability. As the proof-of-concept demonstrations, this work paves the way for improving SERS sensing capability through the silent-range fingerprint and signal amplification strategy, and reveals SERS as an effective tool for trace detection in complex biological and environmental matrices.