journal article·2017-04Open access

Chemisorption of iodine-125 to gold nanoparticles allows for real-time quantitation and potential use in nanomedicine

Adrian A Walsh

Journal of Nanoparticle Research· Volume 19· Issue 4

Abstract

Gold nanoparticles have been available for many years as a research tool in the life sciences due to their electron density and optical properties. New applications are continually being developed, particularly in nanomedicine. One drawback is the need for an easy, real-time quantitation method for gold nanoparticles so that the effects observed in in vitro cell toxicity assays and cell uptake studies can be interpreted quantitatively in terms of nanoparticle loading. One potential method of quantifying gold nanoparticles in real time is by chemisorption of iodine-125, a gamma emitter, to the nanoparticles. This paper revisits the labelling of gold nanoparticles with iodine-125, first described 30 years ago and never fully exploited since. We explore the chemical properties and usefulness in quantifying bio-functionalised gold nanoparticle binding in a quick and simple manner. The gold particles were labelled specifically and quantitatively simply by mixing the two items. The nature of the labelling is chemisorption and is robust, remaining bound over several weeks in a variety of cell culture media. Chemisorption was confirmed as potassium iodide can remove the label whereas sodium chloride and many other buffers had no effect. Particles precoated in polymers or proteins can be labelled just as efficiently allowing for post-labelling experiments in situ rather than using radioactive gold atoms in the production process. We also demonstrate that interparticle exchange of I-125 between different size particles does not appear to take place confirming the affinity of the binding.
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Research context

OpenAlex topics: Nanoparticle-Based Drug Delivery · Nanoparticles: synthesis and applications · Gold and Silver Nanoparticles Synthesis and Applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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