Articles | Volume 4, issue 2
https://doi.org/10.5194/gchron-4-501-2022
https://doi.org/10.5194/gchron-4-501-2022
Short communication/technical note
 | 
21 Jul 2022
Short communication/technical note |  | 21 Jul 2022

Technical note: Rapid phase identification of apatite and zircon grains for geochronology using X-ray micro-computed tomography

Emily H. G. Cooperdock, Florian Hofmann, Ryley M. C. Tibbetts, Anahi Carrera, Aya Takase, and Aaron J. Celestian

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Cited articles

Alves, H., Lima, I., and Lopes, R. T.: Methodology for attainment of density and effective atomic number through dual energy technique using microtomographic images, Appl. Radiat. Isot., 89, 6–12, https://doi.org/10.1016/j.apradiso.2014.01.018, 2014. 
Buijs, W., Van Der Gen, A., Mohn, G. R., and Breimer, D. D.: The direct mutagenic activity of α, ω-dihalogenoalkanes in Salmonella typhimurium: Strong correlation between chemical properties and mutagenic activity, Mutat. Res. Lett., 141, 11–14, https://doi.org/10.1016/0165-7992(84)90029-0, 1984. 
Bowring, S. A. and Schmitz, M. D.: High-precision U-Pb zircon geochronology and the stratigraphic record, Rev. Mineral. Geochem., 53, 305–326, https://doi.org/10.2113/0530305, 2003. 
Cooperdock, E. H. G., Ketcham, R. A., and Stockli, D. F.: Resolving the effects of 2-D versus 3-D grain measurements on apatite (U–Th)/He age data and reproducibility, Geochronology, 1, 17–41, https://doi.org/10.5194/gchron-1-17-2019, 2019. 
Dragonfly 2021.1: Object Research Systems (ORS) Inc, Montreal, Canada [computer software], 2021, http://www.theobjects.com/dragonfly, last access: 25 August 2021. 
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Short summary
Apatite and zircon are the most widely used minerals for dating rocks, but they can be difficult to identify in some crushed rock samples. Incorrect mineral identification results in wasted analytical resources and inaccurate data. We show how X-ray computed tomography can be used to efficiently and accurately distinguish apatite from zircon based on density variations, and provide non-destructive 3D grain-specific size, shape, and inclusion information for improved data quality.