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The Effect of Capping Agents on Gold Nanostar Stability, Functionalization, and Colorimetric Biosensing Capability
Tozivepi Aaron Munyayi1, Barend Christiaan Vorster1, Danielle Wingrove Mulder1
1Human Metabolomics, North-West University, Potchefstroom Campus, Potchefstroom 2531, South Africa.
Nanomaterials (Basel, Switzerland)
|July 27, 2022
Summary
The choice of capping agents significantly impacts the stability and performance of gold nanostar (AuNS)-based biosensors. Understanding these effects is crucial for optimizing AuNS applications in diagnostics and biocatalysis.
Area of Science:
- Nanotechnology
- Biochemistry
- Analytical Chemistry
Background:
- Capping agents are essential for nanoparticle stabilization.
- These agents can alter the surface chemistry and properties of gold nanostars (AuNS).
- This modification affects the performance of AuNS-based biosensors.
Purpose of the Study:
- To investigate how different capping agents influence the stability and functionality of AuNS.
- To evaluate the impact of capping agents on AuNS-based biosensors for hydrogen peroxide (H2O2) detection.
- To provide insights into selecting appropriate capping agents for enzyme-conjugated AuNS applications.
Main Methods:
- Synthesis and characterization of capped and bioconjugated AuNS with various capping agents.
- Application of AuNS as localized surface plasmon resonance (LSPR)-based H2O2 sensors.
- Enzyme immobilization using glucose oxidase (GOx) as a model enzyme.
Main Results:
- Various capping agents demonstrated distinct effects on AuNS stability, functionality, and biocatalysis.
- The choice of capping agent influenced the colorimetric readouts and overall sensor performance.
- Capped and bioconjugated AuNS showed varying degrees of efficacy in H2O2 sensing.
Conclusions:
- Capping agents play a critical role in determining the properties and performance of AuNS biosensors.
- The selection of capping agents directly impacts the stability and downstream applications of enzyme-conjugated AuNS.
- These findings offer guidance for optimizing AuNS-based sensor design and development.

