Radiolabelling of nanoparticles by proton irradiation: temperature control in nanoparticulate powder targets
Source abstract
Abstract
Radiolabelled nanoparticles are useful tools for biodistribution or cellular uptake studies related to the risk assessment of nanomaterials. Such studies are ideally carried out with industrially manufactured nanoparticles. Irradiation of small quantities of such nanoparticles, in the form of dry powders, with neutrons or light ions allows radiolabelling while preserving their biologically relevant properties. However, nanoparticle powders exhibit poor thermal conductivity and may overheat under irradiation. Their effective thermal conductivity is not known and conventional temperature measurement methods are difficult to apply. Reasonably accurate temperature data could be derived from post-irradiation X-ray diffraction studies on anatase ST-01 TiO2-nanoparticles, with a primary particle size of 7 nm, subjected to proton beams of different intensities. The anatase-to-rutile phase transition starting at about 750 °C was identified by observing rutile peaks in X-ray diffraction patterns. The onset of growth of single diffracting TiO2-domains at around 200 °C was revealed by shape analysis of the diffraction peaks. Identifying these reference temperatures allowed a calibration of the calculated temperature profile. The effective thermal conductivity in the TiO2 powder target was found to be close to that of air trapped in interstices of the nanoparticulate powder. This suggests that the contribution of the nanoparticles to the heat removal from the target is negligible, thus necessitating the use of thin nanoparticle layers in the target in order to facilitate cooling and prevent thermally induced alterations of the nanoparticles.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Nuclear Materials and Properties · Nuclear materials and radiation effects · Ion-surface interactions and analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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