Engineering catalytic gold nanoclusters and nanovesicles for detection of disease
File(s)
Author(s)
Chen, Kaili
Type
Thesis
Abstract
Timely and accurate disease detection is a crucial first step in effective disease management, including personalised medicine treatments. Point-of-care (PoC) diagnostics offer numerous advantages, such as user-friendliness, speed, and cost-effectiveness, making them valuable in resource-limited settings. Nevertheless, their limited test offerings often result in lower sensitivity and specificity when compared to laboratory-based diagnostics. To address this limitation, this thesis focuses on leveraging ultra-small renal clearable gold nanoclusters (AuNCs) as the foundation for developing straightforward, sensitive and cost-effective biosensors for in vivo disease monitoring. These NCs possess several benefits, including biocompatibility and the ability to be readily eliminated from the body. In this thesis, the catalytic activity of NCs for signal amplification was optimised firstly while ensuring they retain their renal clearable size, which aimed to enhance the sensitivity of biosensors and guarantee renal clearance for in vivo sensing. The second aim focuses on creating an enzyme-responsive biosensor for the rapid and sensitive detection of malaria infection while malaria remains a substantial global health challenge, particularly in developing nations. The final project involves encapsulating nanoclusters within liposomes to create a biosensor tailored for the detection of bacterial infections. This innovative biosensor holds the potential to revolutionise the diagnosis of bacterial infections. In conclusion, this PhD research thesis represents an important step towards the development of biosensors for in vivo disease detection using ultra-small renal clearable gold nanoclusters. The successful development of these biosensors could have a significant impact on global health, enabling early detection and treatment of diseases, improving patient outcomes, and reducing healthcare costs.
Version
Open Access
Date Issued
2023-10-01
Date Awarded
2024-03-01
Copyright Statement
Creative Commons Attribution NonCommercial Licence
License URL
Advisor
Stevens, Molly
Publisher Department
Materials
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)
Rights Embargo Date
2026-02-28