Novel methods for improving the robustness of quantitative metal nanoparticle surface-enhanced Raman measurements

Abstract

Since the discovery in 1977 that certain conditions enhance scattering per molecule, surface-enhanced Raman spectroscopy (SERS) has grown rapidly. SERS greatly increases the likelihood of inelastic light scattering, addressing the low sensitivity and fluorescence interference of conventional Raman spectroscopy. Metal nanoparticle (NP) colloids are popular SERS substrates due to their accessibility, cost-effectiveness, and significant enhancement of Raman signals. Aggregating colloidal NPs with simple salts forms "hot spots" between adjacent NPs, where large plasmonic fields amplify signals. The main challenge in SERS detection is achieving reproducibility, as variations i signal enhancement arise from electromagnetic and chemical fluctuations. Efforts to improve reproducibility include using internal standards (IS) and creating stable SERS substrates. However, traditional IS methods, which rely on strongly scattering molecules, have drawbacks, including competition for surface space with target analytes and potential signal overlap, especially for complex analytes.

This thesis first investigates an alternative IS approach using chemically-matched fragments of target molecules, which improves response consistency and extends detection to concentrations beyond monolayer coverage. This approach allows more accurate signal calibration for complex analytes. The second focus is the development of ultra-stable SERS-active colloidal aggregates using a low-viscosity, unreactive hydrogel to stabilize NP aggregates while minimizing interference with analyte diffusion. A model based on independent polymer coils preventing further aggregation of NP clusters was proposed. This substrate consistently produces reliable, time-independent SERS results, overcoming typical stability issues. Additionally, derivatives of this substrate demonstrate enhanced performance beyond traditional colloidal aggregates. This innovative approach provides a foundation for further applications in SERS-based analysis.

Thesis is embargoed until 31st December 2029.

Date of Award	Dec 2024
Original language	English
Awarding Institution	Queen's University Belfast
Supervisor	Steven Bell (Supervisor) & Panagiotis Manesiotis (Supervisor)