To overcome this critical hurdles, Prof. Ki-Bum Lee and his team at the Department of Chemistry and Chemical Biology of Rutgers developed an innovative sensing platform based on graphene-coated homogeneous plasmonic metal (Au) nanoarrays to synergize both electromagnetic mechanisms (EM)- and chemical mechanism (CM)-based enhancement. Through the homogeneous plasmonic nanostructures, generated by laser interference lithography (LIL), highly reproducible enhancement of Raman signals could be obtained via a strong and uniform EM. Additionally, the graphene-functionalized surface simultaneously amplifies the Raman signals by an optimized CM, which aligns the energy level of the graphene oxide with the target molecule by tuning its oxidation levels, consequently increasing the sensitivity and accuracy of their sensing system. Using the dual-enhanced Raman scattering from both EM from the homogeneous plasmonic Au nanoarray and CM from the graphene surface, this advanced graphene-Au hybrid SERS nanoarray was successfully utilized to detect as well as quantify a specific biomarker (TuJ1) gene expression levels to characterize neuronal differentiation of human neural stem cells (hNSCs). The team believes their unique graphene-plasmonic hybrid nanoarray can be extended to a wide range of applications in the development of simple, rapid, and accurate sensing platforms for screening various bio/chemical molecules and facilitating stem cell therapy in the clinical treatment of neurological disorders.
This work is through the collaboration between Rutgers University and Sogang University and funded by the National Science Foundation (NSF) and the New Jersey Commission on Spinal Cord Research (NJCSCR).
A large-scale homogenous substrate coupled with dual-enhanced Raman scattering has been developed for sensitive gene detection and monitoring stem cell differentiation. This unique sensing platform is composed of a uniform gold-graphene hybrid nanoarray that orthogonally modulates both electromagnetic and chemical enhancement of Raman signals, thereby improving reliability, selectivity and sensitivity effectively toward next-generation gene sensing and biomarker detections.
Dr. KiBum Lee
Dr. Letao Yang
Dr. Jin-Ho Lee