Simulation Driven Design of a Multilayer Plasmonic Sensor Using Cu Ni and BaTiO3 for Waterborne Pathogen Detection

TL;DR

This paper presents a simulation-guided design of a multilayer plasmonic biosensor using copper, nickel, BaTiO3, and graphene oxide, optimized for waterborne pathogen detection with high sensitivity.

Contribution

It introduces a novel multilayer SPR sensor design optimized via transfer matrix and finite element methods for enhanced waterborne pathogen detection.

Findings

Achieved sensitivity of 157.8 deg/RIU

Optimized layer thicknesses for maximum performance

Demonstrated detection of E. coli in water

Abstract

We present a simulation guided design for a multilayer surface plasmon resonance (SPR) based biosensor capable of detecting refractive index changes in a target induced by analytes. Surface plasmons are excited using a hybrid Kretschmann configuration with a calcium fluoride (CaF2) prism under transverse magnetic polarization illumination. In the sensing architecture, copper (Cu) serves as the plasmonic metal and is overlaid with a thin nickel (Ni) layer to prevent oxidation. To enhance analyte coupling and electromagnetic field confinement, a dielectric layer of barium titanium oxide (BaTiO3) along with a monolayer of graphene oxide (GO) is incorporated. The multilayer structure is iteratively optimized using the transfer matrix method for angular interrogation at a wavelength of 1064 nm, focusing on key performance parameters such as sensitivity, minimum reflectivity, and figure of merit (FOM). Finite element method based simulations confirm efficient surface plasmon excitation, with optimal layer thicknesses of 30 nm for Cu and 5 nm for BaTiO3. The proposed SPR based sensor (CaF2 Cu Ni BaTiO3 GO) achieves a sensitivity of 157.8 deg per RIU and a figure of merit of 17.48 RIU minus one while detecting the presence of Escherichia coli bacteria in water, demonstrating its potential for waterborne pathogen sensing applications.