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

Abstract

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.

Keywords: Bimetallic nanoparticles; Electrospun nanofibers; Enhancement factor; Fluconazole; Lincomycin; Raman spectroscopy; SERS.