Random scattering of surface plasmons for sensing and tracking
File(s)
Author(s)
Berk, Joel Andrew
Type
Thesis
Abstract
In this thesis, a single particle biosensing setup, capable of sensing and tracking single nanoscale biological particles, is proposed and investigated theoretically. The setup is based on monitoring the speckle pattern intensity distribution arising due to random scattering of surface plasmon polaritons (SPPs) from a metal surface. An analyte particle close to the surface will additionally scatter light, perturbing the speckle pattern. From this speckle pattern perturbation, the analyte particle can be detected and tracked. Theoretical sensitivity analysis predicts a biological particle on the order of 10nm in radius gives a fractional intensity perturbation to the speckle intensity of 10^4, comparable to intensity contrasts used in existing interferometric scattering sensing techniques. A formula for the minimum detectable particle size is derived. In addition, an algorithm is derived capable of extracting the particle trajectory in the single scattering regime from the change to the speckle intensity perturbation over time and shown to be capable of errors of approximately 1nm on simulated data under optimal noise conditions. The effect of multiple scattering on the speckle pattern perturbation is studied, and it is shown that, by tuning the scattering mean free path and individual scatterer properties of a random nanostructure of scatterers on the metal surface, one can increase the magnitude of the speckle field perturbation by up to the order of 10^2. A neural network based localisation algorithm is developed to calculate the analyte particle position based on the speckle intensity perturbation and its performance on simulated data is studied. Mean errors on the order of 20nm were found, depending on the size of the region over which the particle must be tracked. Unlike the single scattering tracking algorithm, the neural network algorithm continues to function in the multiple scattering regime.
Version
Open Access
Date Issued
2021-12
Date Awarded
2022-03
Copyright Statement
Creative Commons Attribution NonCommercial Licence
License URL
Advisor
Foreman, Matthew
Paterson, Carl
Sponsor
Engineering and Physical Sciences Research Council
Grant Number
1992728
Publisher Department
Physics
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)