7Be-recoil radiolabelling of industrially manufactured silica nanoparticles
Source abstract
Abstract
Radiolabelling of industrially manufactured nanoparticles is useful for nanoparticle dosimetry in biodistribution or cellular uptake studies for hazard and risk assessment. Ideally for such purposes, any chemical processing post production should be avoided as it may change the physico-chemical characteristics of the industrially manufactured species. In many cases, proton irradiation of nanoparticles allows radiolabelling by transmutation of a tiny fraction of their constituent atoms into radionuclides. However, not all types of nanoparticles offer nuclear reactions leading to radionuclides with adequate radiotracer properties. We describe here a process whereby in such cases nanoparticles can be labelled with 7 Be, which exhibits a physical half-life of 53.29 days and emits γ-rays of 478 keV energy, and is suitable for most radiotracer studies. 7 Be is produced via the proton-induced nuclear reaction 7 Li(p,n) 7 Be in a fine-grained lithium compound with which the nanoparticles are mixed. The high recoil energy of 7 Be atoms gives them a range that allows the 7 Be-recoils to be transferred from the lithium compound into the nanoparticles by recoil implantation. The nanoparticles can be recovered from the mixture by dissolving the lithium compound and subsequent filtration or centrifugation. The method has been applied to radiolabel industrially manufactured SiO 2 nanoparticles. The process can be controlled in such a way that no alterations of the 7 Be-labelled nanoparticles are detectable by dynamic light scattering, X-ray diffraction and electron microscopy. Moreover, cyclotrons with maximum proton energies of 17–18 MeV that are available in most medical research centres could be used for this purpose.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Nuclear Materials and Properties · Fusion materials and technologies · Boron and Carbon Nanomaterials Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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