Towards Urine Sensing in Plasmonic Nanogaps

Change log
Grys, David-Benjamin 

Molecular sensing of biofluids has great potential for continuous health monitoring. Urine is of particular interest as it is available in large quantities and can be collected non-invasively. Urine contains thousands of metabolites and potential biomarkers at trace concentrations. Surface-enhanced Raman is a label-free spectroscopic technique which is particularly suitable for detecting trace amounts of molecules in liquids. By confining molecules to plasmonic hotspots between metal surfaces, low concentrations of analytes can be detected. This opens opportunities for multiplexed sensing, but also poses a challenge for the selective measurement of complex analytes, such as urine, which contain an overabundance of compounds spanning a wide range of concentrations.

This thesis investigates molecular sensing using self-assembled aggregates of spherical gold nanoparticles, either suspended in solution or deposited as films. In these nano-assemblies, millions of nanogaps, whose interparticle distance is precisely defined by a rigid molecular spacer, provide the plasmonic enhancement to enable both reproducible and extraordinarily sensitive Raman sensing.

Three layers of the sensing system are explored: (1) the low-level surface chemistry of citrate-capped nanoparticles is resolved, as it governs the reproducible formation of nanogaps. Ageing through the thermal restructuring of the gold surface is found to limit reproducibility. (2) At the intermediate level, assays are developed and optimised, revealing how molecules compete for nanogap sequestration in a multi-analyte environment. (3) Lastly, the application-side of automating fluidic sensing platforms for drug detection in urine samples is realised. The developed system involves chromatographic separation to mitigate the effects of interfering compounds.

Baumberg, Jeremy
Raman, SERS, biosensing, fluidics, nanoparticles, chromatography, urine
Doctor of Philosophy (PhD)
Awarding Institution
University of Cambridge
EPSRC (1783357)