Gold nanoparticle decorated blu-ray digital versatile disc as a highly reproducible surface-enhanced Raman scattering substrate for detection and analysis of rotavirus RNA in laboratory environment

Plasma treated bimetallic nanofibers as sensitive SERS platform and deep learning model for detection and classification of antibiotics

Design of a sensitive, cost-effective SERS substrate is critical for probing analyte in trace concentration in real field environment. Present work reports the fabrication of an oxygen (O2) plasma treated bimetallic nanofibers as a sensitive SERS platform. In contrast to the conventional nanofiber-based SERS platform, the proposed plasma-treated bimetallic nanofibers-based SERS platform offers high sensitivity and reproducibility characteristics. On top, the use of bimetallic nanoparticles provides a synergistic effect, contributing to both electromagnetic and chemical enhancement to SERS performance and the plasma treatment contributes to the controlled exposure of the embedded nanoparticles (NPs) to the analyte thereby enhancing the overall sensitivity of the proposed technique. With standard Raman active probe molecules - 1,2-bis(4-pyridyl) ethylene (BPE) and rhodamine-6G (R6G) the limit of detection (LOD) and the limit of quantification (LOQ) of the proposed sensing platform are estimated to be 3.8 nM and 11.6 nM respectively. The enhancement factor (EF) of the designed sensing platform is calculated to be ∼108 with a maximum signal variations of 5 %. The applicability of the designed SERS substrate has been realized through detection of two antibiotics - fluconazole (FLU) and lincomycin (LIN) widely used in poultry farms. Furthermore, a deep learning model - artificial neural network (ANN) has been implemented for effective classification of the analyte molecules from a mixed sample.

Electrochemically deposited bimetallic SERS substrate for trace sensing of antibiotics

Electrochemically deposited bimetallic copper-gold nanoparticles on indium tin oxide (Cu-AuNPs on ITO) glass are demonstrated to be a sensitive and reproducible surface-enhanced Raman scattering (SERS) platform. An optimal signal enhancement with reasonably good degree of homogeneity was obtained by tuning the deposition parameters of the electrochemical setup. For Raman active analytes such as malachite green (MG) and rhodamine 6G (R6G), the developed SERS platform yields a limit of detection (LOD) of 0.75 nM. The usability of the proposed SERS platform has been realized through detection of two important antibiotics namely sulfamethoxazole (SFZ) and tetracycline hydrochloride (TCH) commonly used in egg farms. Furthermore, a machine learning (ML)-based model coupled with a dimensionality reduction technique-principal component analysis (PCA)-has been implemented to classify the targeted analytes in egg samples.