Highly sensitive silver-based localized surface plasmon resonance (LSPR) biosensor for microRNA-21 detection: Discrete dipole approximation together with molecular polarizability method

• Published in: Applied surface science 2023-10, Vol.634, p.157681, Article 157681
• Main Authors: Siabi-Garjan, Araz, Kia, Solmaz, Mirzaee, Sharareh, Rostami, Ali
• Format: Article
• Language: English
• Subjects: Discrete dipole approximation; Gastric cancer; microRNA-21; Molecular polarizability method; Silver nanoparticles
• ISSN: 0169-4332; 1873-5584
• DOI: 10.1016/j.apsusc.2023.157681

[Display omitted] •Plasmonic based Gastric cancer biosensor with high sensitivity was made by simple method.•Sensing characterization was simulated accurately by combination of discrete dipole approximation (DDA) and molecular polarizability methods.•Formation of zig-zag chain structures of silver nanoparticles confirmed by experimental and theoretical results.•The presented method have high potential for deep investigating other plasmonic based cancer biosensors. In this research, a highly sensitive gastric cancer biosensor based on the (LSPR) properties of silver nanoparticles has been produced to identify the microRNA-21. This biosensor has been analyzed by UV–Visible spectrophotometer (UV) and Field Emission Scanning Electron Microscopy (FESEM). The obtained results show that our optimized synthesis process leads to the creation of approximately zig-zag chain structures consisting of 4 or 5 nanoparticles which can sense the cancer agent by a maximum shift in the plasmonic peak position. In addition, the mentioned results were confirmed by a comprehensive simulation of the biosensors using discrete dipole approximation (DDA) modified by the molecular polarizability method. The simulation process has been made by considering the effects of the actual molecular structures for both the receptor and target microRNAs on the surface of nanoparticles. The mentioned approach led to approximately accurate results that were matched with experimental achievements. This comparison indicates that the presented method is a suitable way for deep analysis of (LSPR)-based cancer biosensors.