UCLA

Surface Enhanced Raman Scattering (SERS) has attracted explosive interest for the wealth of vibrational information it provides with minimal invasive effects to target analyte. Nanotechnology, especially in the form of noble metal nanoparticles exhibit unique electromagnetic and chemical characteristics that are explored to realize ultra-sensitive SERS detection in chemical and biological analysis. Graphene, atom-thick carbon monolayer, exhibits superior chemical stability and bio-compatibility. A combination of SERS-active metal nanostructures and graphene will create various synergies in SERS.

The main objective of this research was to exploit the applications of the graphene-Au tip hybrid platform in SERS. The hybrid platform consists of a periodic Au nano-pyramid substrate to provide reproducible plasmonic enhancement, and the superimposed monolayer graphene sheet, serving as “built-in” Raman marker. Extensive theoretical and experimental studies were conducted to determine the potentials of the hybrid platform as SERS substrate. Results from both Finite-Domain Time-Domain (FDTD) numerical simulation and Raman scattering of graphene suggested that the hybrid platform boosted a high density of hotspots yielding 1000 times SERS enhancement of graphene bands.

Ultra-high sensitivity of the hybrid platform was demonstrated by bio-molecules including dye, protein and neurotransmitters. Dopamine and serotonin can be detected and distinguished at 10-9 M concentration in the presence of human body fluid. Single molecule detection was obtained using a bi-analyte technique. Graphene supported a vibration mode dependent SERS chemical enhancement of ~10 to the analyte.

Quantitative evaluation of hotspots was presented using spatially resolved Raman mapping of graphene SERS enhancement. Graphene plays a crucial role in quantifying SERS hotspots and paves the path for defining SERS EF that could be universally applied to various SERS systems. A reproducible and statistically reliable SERS quantification approach using the hybrid platform was proposed. The SERS mapping based approach not only leverages the ultra-sensitivity but also minimizes the spot-to-spot variations.

Feasibility of biomedical diagnosis with the hybrid platform was exploited by colon cancer cell sensing and time-dependent SERS of amyloid β protein monomer. The capabilities of the platform are demonstrated by colon cancer cell detection in simulated body fluid background with cell concentration down to 50 cells /mL. Sensitivity of 95% was evidenced by Principle Components Analysis (PCA). Besides, a noticeable evolution profile of the Aβ SERS peaks was observed and attributed to the Aβ configurational change. Taken together, the results suggested the graphene-plasmonic hybrid platform can potentially deliver a biomedical detection and diagnostic imaging platform with superior sensitivity and resolution.