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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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gchron-2022-7', Anonymous Referee #1, 11 Apr 2022
    • AC2: 'Reply on RC1', Emily Cooperdock, 14 Jun 2022
    • AC3: 'Reply on RC1', Emily Cooperdock, 14 Jun 2022
  • RC2: 'Comment on gchron-2022-7', Anonymous Referee #2, 12 May 2022
    • AC1: 'Reply on RC2', Emily Cooperdock, 14 Jun 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Publish subject to revisions (further review by editor and referees) (16 Jun 2022) by Shigeru Sueoka
AR by Emily Cooperdock on behalf of the Authors (16 Jun 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (17 Jun 2022) by Shigeru Sueoka
RR by Anonymous Referee #2 (19 Jun 2022)
RR by Anonymous Referee #1 (22 Jun 2022)
ED: Publish as is (23 Jun 2022) by Shigeru Sueoka
ED: Publish as is (25 Jun 2022) by Greg Balco (Editor)
AR by Emily Cooperdock on behalf of the Authors (25 Jun 2022)
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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.