Molecular Surface Quantification of Multifunctionalized Gold Nanoparticles Using UV-Visible Absorption Spectroscopy Deconvolution.
Jordan C PottsAkhil JainDavid B AmabilinoFrankie J RawsonMaria Luisa Pérez-GarcíaPublished in: Analytical chemistry (2023)
Multifunctional gold nanoparticles (AuNPs) are of great interest, owing to their vast potential for use in many areas including sensing, imaging, delivery, and medicine. A key factor in determining the biological activity of multifunctional AuNPs is the quantification of surface conjugated molecules. There has been a lack of accurate methods to determine this for multifunctionalized AuNPs. We address this limitation by using a new method based on the deconvolution and Levenberg-Marquardt algorithm fitting of UV-visible absorption spectrum to calculate the precise concentration and number of cytochrome C (Cyt C ) and zinc porphyrin (Zn Porph) bound to each multifunctional AuNP. Dynamic light scattering (DLS) and zeta potential measurements were used to confirm the functionalization of AuNPs with Cyt C and Zn Porph. Transmission electron microscopy (TEM) was used in conjunction with UV-visible absorption spectroscopy and DLS to identify the AuNP size and confirm that no aggregation had taken place after functionalization. Despite the overlapping absorption bands of Cyt C and Zn Porph, this method was able to reveal a precise concentration and number of Cyt C and Zn Porph molecules attached per AuNP. Furthermore, using this method, we were able to identify unconjugated molecules, suggesting the need for further purification of the sample. This guide provides a simple and effective method to quickly quantify molecules bound to AuNPs, giving users valuable information, especially for applications in drug delivery and biosensors.
Keyphrases
- drug delivery
- gold nanoparticles
- high resolution
- cancer therapy
- heavy metals
- single molecule
- electron microscopy
- photodynamic therapy
- metal organic framework
- reduced graphene oxide
- drug release
- machine learning
- healthcare
- deep learning
- single cell
- human health
- gene expression
- aqueous solution
- mass spectrometry
- quantum dots