High-sensitivity SERS based sensing on the labeling side of glass slides using low branched gold nanoparticles prepared with surfactant-free synthesis. Issue 56 (18th September 2020)

• Record Type: Journal Article
• Authors: Tezcan, Tuğba; Hsu, Chia-Hsien
• Is Part Of: RSC advances. Volume 10:Issue 56(2020)
• Page Start: 34290
• Page End: 34298
• Publication Date: 2020-09-18
• Subjects: Chemistry -- Periodicals; 540.5
• DOI: 10.1039/d0ra02490b
• Languages: English
• ISSNs: 2046-2069

Abstract

Abstract : High-sensitivity dopamine detection on aggregated low branched nanoparticles on labelling side of glass slide as a SERS based sensor. Abstract : Surface-enhanced Raman scattering (SERS) has become a more attractive tool for biological and chemical sensing due to having a great detection potential to extremely low concentrations of analyte. Here, we report high-sensitivity SERS detection of low branched gold nanoparticles which are produced by a surfactant-free synthesis method. The effects of the size and branches of nanoparticles on the SERS signal intensity were also investigated. Among the prepared nanoparticles, a new type of nanoparticle with small protrusions produced by using a very low concentration of silver ions (2 μM in final solution) achieved the best enhancement factor of ∼4 × 10 5 for DTNB used as a probe molecule. SERS measurements were performed on the labeling side of microscope glass slides for the first time. The substrate exhibited a good reproducible SERS signal with a relative standard deviation (RSD) of 1.7%. SERS signal intensity obtained using the labelling side was three times larger compared to that obtained using bare glass. To validate the sensing platform, dopamine, an important modulatory neurotransmitter in the brain, was tested. The reported platform was able to achieve label-free detection of dopamine at picomolar and nanomolar concentration level in aqueous and fetal bovine serum (FBS) solution at pH 8.5 respectively. Due to its surfactant-free preparation and enhanced SERS-based sensing features, our reported platform represents a strong alternative to be used in SERS-based sensing applications.