These authors contributed equally to this work.
Apatite and zircon are among the best-studied and most widely used accessory minerals for geochronology and thermochronology. Given that apatite and zircon are often present in the same lithologies, distinguishing the two phases in crushed mineral separates is a common task for geochronology, thermochronology, and petrochronology studies. Here we present a method for efficient and accurate apatite and zircon mineral phase identification and verification using X-ray micro-computed tomography (microCT) of grain mounts that provides additional three-dimensional grain size, shape, and inclusion suite information. In this study, we analyze apatite and zircon grains from Fish Canyon Tuff samples that went through methylene iodide (MEI) and lithium heteropolytungstate (LST) heavy liquid density separations. We validate the microCT results using known standards and phase identification with Raman spectroscopy, demonstrating that apatite and zircon are distinguishable from each other and other common phases, e.g., titanite, based on microCT X-ray density. We present recommended microCT scanning protocols after systematically testing the effects of different scanning parameters and sample positions. This methodology can help to reduce time spent performing density separations with highly toxic chemicals and visually inspecting grains under a light microscope, and the improved mineral identification and characterization can make geochronologic data more robust.
Apatite and zircon are mineral phases widely used for geochronology and thermochronology using the U-Pb (e.g., Bowring and Schmitz, 2003), (U-Th)/He (e.g., Farley, 2002), and fission track (e.g., Tagami and O'Sullivan, 2005) methods. Particularly for (U-Th)/He, correct identification of these phases (e.g., Guenthner et al., 2016), characterization of the crystal shape (Farley et al., 1996), and the absence of mineral and fluid inclusions (e.g., Lippolt et al., 1994; Vermeesch et al., 2007) are important factors in producing reliable high-quality geo- and thermochronologic data. The standard approach to selecting apatite and zircon grains for geo- and thermochronology is to (1) crush and grind rock samples into their mineral constituents; (2) perform magnetic and density separation which may include a Frantz isodynamic separator, water table, and heavy liquids to filter for the mineral of choice; and then (3) pick individual grains from these separates under a transmitted light microscope (e.g., Gautheron et al., 2021).
Different heavy liquid solutions used for density separation can either
produce grain fractions that have apatite and zircon mixed together or
separated (e.g., Dumitru and Stockli, 1998; Koroznikova et al., 2008). The
density of apatite (
Both bromoform and MEI are known to be toxic. Specifically, MEI can cause acute symptoms through skin contact or inhalation, and acute toxicity and death have been documented for a case of ingestion (Weimerskirch et al., 1990). MEI has also been shown to be mutagenic, meaning that acute or long-term exposure may impact reproductive health, particularly in pregnant women (Van Bladeren et al., 1980; Osterman-Golkar et al., 1983; Buijs et al., 1984; Roldán-Arjona and Pueyo, 1993). In addition, samples separated with MEI are typically washed with acetone, and the mixture of these chemicals is highly flammable. Burning MEI has the potential to produce large amounts of free iodine, which also poses a significant health risk (Hauff and Airey, 1980). Due to its toxicity, MEI must be used in a vent hood with proper personal protective equipment (PPE) and requires special training in safe handling techniques (Dumitru and Stockli, 1998).
Safety precautions required for hazardous chemical handling may exclude workers or students with conditions that do not allow them to comply with the safety precautions. For example, personal protective equipment may only be available in restricted sizes, and fume hood design is often incompatible with the use of wheelchairs or other mobility devices. Thus, eliminating hazardous chemicals from laboratory procedures results in both a safer work environment and a more inclusive workplace.
Many labs elect to use lithium heteropolytungstate (LST), lithium
metatungstate (LMT), and sodium polytungstate (SPT) because they are
generally non-toxic and relatively inert (Munsterman and Kerstholt, 1996;
Mounteney, 2011). Similar to bromoform (but less toxic), these heavy liquids
can be used at densities of 2.8–3.0 g cm
Mistaken mineral identification can lead to significant issues in data analysis, quality, and interpretation. Depending on the geochronologic technique employed, this misidentification might be detected further along in the analytical procedures. In (U-Th)/He analysis, a mistake may be realized during degassing or dissolution. Due to their different diffusion behavior, zircon usually requires higher temperatures and longer laser heating times to fully extract He than for apatite (e.g., Farley, 2002). Apatite dissolves readily in a weak nitric acid, whereas zircon needs to be subjected to extensive Parr bomb pressure dissolution procedures using a mixture of nitric acid, hydrochloric acid, and hydrofluoric acid to be completely dissolved (Farley, 2002). As a result, a misidentified mineral may not be completely degassed or dissolved during the analytical procedure, leading to erroneous results. The presence of Ca or Zr in dissolved mineral solutions can be used during subsequent isotope-dilution inductively coupled plasma mass spectrometry (ICP-MS) analysis to test whether the correct phase was chosen for the analysis, as was demonstrated for (U-Th)/He by Guenthner et al. (2016).
Similar issues arise in other methods. In laser ablation analysis as part of U-Pb or (U-Th)/He dating, the ablation characteristics and the presence of Ca or Zr in the analyte can be used as diagnostic criteria. Etching parameters for fission track, such as the type and molarity of acids, etching time, and temperature conditions, are highly phase specific and need to be tightly controlled to yield reproducible and internally consistent data (Tagami and O'Sullivan, 2005). Applying zircon etching procedures to apatite grains might lead to the complete loss of a sample.
Given the amount of time and materials required by these analytical methods, misidentification of minerals can lead to significant monetary and time-effort losses. Many laboratories will use techniques to reduce mineral misidentification for challenging samples. These can include having a more experienced user look over selected grains, analyzing pre-selected grains under a scanning electron microscope (SEM) to measure elemental compositions with energy-dispersive spectroscopy (EDS), using Raman spectroscopy for phase identification, and others. Which of these techniques is employed at a given institution varies based on instrument availability, budget, and time allotted to this task.
Here we show that X-ray micro-computed tomography (microCT) scanning can be used as an effective pre-screening tool to distinguish between apatite and zircon and to detect misidentification of grains. MicroCT is growing in popularity in Earth science departments as benchtop systems make operations simpler and more affordable. Many universities already have microCT instruments available in engineering or health sciences departments.
The difference in apatite and zircon composition and densities (3.1–3.2 and 3.9–4.7 g cm
We selected Fish Canyon Tuff (FCT) as a test sample because it contains both
apatite and zircon and is used as an age standard in many applications of
geo- and thermochronology (McDowell et al., 2005; Donelick et al., 2005). We
obtained three separate FCT samples: one mineral separate of an MEI heavy
fraction given to us by the UTChron Laboratory at the University of Texas at
Austin (UT-FCT), and two that we collected from two FCT localities near
Monte Vista, CO (USC-FCT1:
The UT-FCT separate supplied by the University of Texas at Austin was
processed using the same mineral separation procedures with the following
exceptions: the samples were density separated on a Gemeni water table prior
to magnetic separation, and the sample experienced a two-step heavy liquid
separation using bromoform and MEI. These heavy liquids are more toxic than
LST but have densities of 2.95 and 3.32 g cm
As a reference for microCT imaging, we used mineral standards for apatite,
zircon, and titanite from existing collections. Two Durango apatite
standards from large apatite crystals were supplied by the UTChron
laboratory at the University of Texas at Austin (UT-DUR) and Caltech
(ClT-DUR). We used shards from large crystals of Sri Lankan zircon (SL1)
from Caltech (Farley et al., 2020) and Minas Gerais titanite (MG1) from the
Natural History Museum of Los Angeles County (more specific sample location
information is not known). These standard crystals were gently hand crushed
and sieved to
Mineral standards and unknowns used in this study. Large standard crystals were crushed to obtain shards to be used as a reference for microCT analyses. Unknown grains were extracted from FCT whole-rock samples.
Graduate students were tasked with picking mineral grains that looked like
apatite or zircon and covered a range of grain sizes and morphologies from
the three FCT samples using a Nikon SMZ25 optical microscope. It is notable
that all samples, including the MEI separate, yielded both apatite and
zircon. The selected grains were placed onto grain mounts for microCT
analysis (see Sect. 2.3). Each mount also included known mineral standards
for reference and normalization (Fig. 1a). Three grain mounts were
constructed (Mounts A, B, and C; see Fig. 2). Mount A included 36 grains from
UT-FCT “unknowns”, 10 shards of SL1 zircon, and 15 shards of ClT-DUR
apatite. Mount B included 39 grains of USC-FCT1 unknowns, 32 grains of
USC-FCT2 unknowns, 9 shards of SL1 zircon, and 24 shards of UT-DUR
apatite. Mount C included 11 shards of SL1 zircon, 15 shards of CIT-DUR
apatite, and 15 shards of MG1 titanite standards. We used the 75–250
We assembled grain mounts by cutting small plastic shapes (rectangles,
squares, or circles) out of 1 mm thick plastic slides and placing
double-sided adhesive tape on one side. Mounts for vertical scans (when the
mount is standing upright on the top of the sample holder) were constructed
by cutting
Prior to placing the grains, the plastic mounts were temporarily secured to a glass slide with double-sided tape to hold them in place. Individual crystals were selected from mineral separates and placed on the tape using tweezers and needles under a light microscope. Grains were spaced to avoid touching, with up to 104 crystals in total per mount. Optical micrographs of the mount and each individual crystal were taken with transmitted and reflected light as well as with crossed polarizers.
All microCT scans were acquired on a Rigaku CT Lab HX130 benchtop microCT
instrument at the USCHelium Laboratory at the University of Southern
California. Individual mounts were installed vertically (perpendicular to
the X-ray beam direction, parallel to the detector plane; see Fig. 1b) in
order to minimize the effect of interference from X-ray artifacts such as
shadowing between individual grains due to beam hardening and photon
starvation (see Sect. 3.2 and Fig. 7). Mounts were scanned at accelerating
voltages of 130 and 60 kV, with currents of 61 and 133
Scan parameters tested in this study.
The reconstructed microCT data were processed with Dragonfly (Version 2021.1; Dragonfly 2021.1, 2021) by Object Research Systems. Reconstructed volumes of each mount with all different scan times and X-ray energies were loaded into Dragonfly. The volumes scanned at 60 kV for 68 min were used as a reference since they displayed the best signal-to-noise ratio of all the tested scan parameters. Volumes were registered relative to the 60 kV/68 min scans using the Image Registration tool, which translates and rotates volumes to align scans. Grains were segmented in the 60 kV/68 min scan volumes by creating regions of interest (ROI) using histographic segmentation, which delineates grains from their surroundings (air or adhesive tape) based on threshold grayscale values. The resulting volumes were filtered by applying a 3D opening operation (a combination of erosion and dilation which removes small objects, like dust, while not changing the geometry of large volumes) and eroded by one voxel to remove the effect of rapid changes in grayscale value near the grain boundary.
Each grain was separated into an “object” by creating a multi-ROI (an ROI that contains multiple objects) from continuous segments in which voxels are connected by at least one of their faces (6-connected). Each grain object consists of hundreds to thousands of voxels that can be used to calculate grayscale statistics. Small fragments separated from larger grains of less than 100 voxels were not used for further analysis to ensure the measurements have statistical significance. In this way, individual grains were mapped out and distinguished from other small objects in the scan (e.g., chipped pieces or detritus on the adhesive tape). The geometry of the segmented objects was resampled to fit each volume, and information on the position, size, surface area, and grayscale value distribution of each grain was extracted from the multi-ROIs.
Absolute grayscale values can change between scans since they are dependent on the scan geometry, acquisition parameters, arrangement of grains, and processing, with internal normalization and scaling being applied during reconstruction. To make scans comparable, we chose to normalize the grayscale values of all grains on a mount by the average grayscale value of the SL1 zircon grains in the same volume. We also computed the ratio of the grayscale values of the 60 and 130 kV scans with otherwise identical scan parameters to yield a dual-energy parameter (see Supplement for measured grayscale values and RSDs).
To validate the different phases observed in microCT data, we determined the
mineral phase of 35 grains in Mount A and Mount B by Raman spectroscopy. This
included a subset of 28 unknown grains from FCT samples and 7 shards of
known mineral standards (Fig. 2). Representative grains were selected to
encompass a range of grain sizes and morphologies, positions on the mount,
and microCT grayscale contrast. After microCT scanning, the grain mounts
were transferred to a glass slide, and grains were analyzed using a HORIBA
XploRA PLUS spectrometer at the Natural History Museum of Los Angeles
County. Apatite, zircon, and titanite were identified by matching
baseline-corrected spectra with comparison spectra from the RRUFF database
(Lafuente et al., 2015) using CrystalSleuth. Raman spectral analyses were
conducted using a green 532 nm diode laser at 50 % laser power, a
diffraction grating of 1880 gr mm
Transmitted light micrographs
Different microCT scanning parameters were systematically tested on the same
three grain mounts to determine the optimal scan conditions for
distinguishing between mineral phases while minimizing cost, time, and data
file sizes. Individual microCT data file sizes range from 2 to 28 GB,
depending on acquisition and processing parameters. Reconstructing and
manipulating large datasets can require specialized computers with demanding
system requirements for data storage, memory, and processing power. The
microCT data for single grain mounts, like the ones used in this study, can
be cropped to produce manageable file sizes that can be viewed and analyzed
without the need for specialized computers. We determined that for the
instrument used here, a continuous scan time of 17 min at 60 kV (5.7
We calculated the theoretical X-ray total attenuation coefficients of
apatite, zircon, titanite, monazite, and rutile (Fig. 3a) for a range of
X-ray energies commonly used for microCT (
Based on these calculations, zircon has a much higher attenuation
coefficient than apatite across the energy spectrum. At lower energies, the
difference between the attenuation coefficients of other minerals relative
to zircon (Fig. 3b) is greater than at higher energies. The attenuation
coefficients of apatite, zircon, titanite, and rutile converge around
200–300 keV. Thus, energies less than
We use the 68 min continuous scans to assess how grayscale values of individual grains (or shards) vary at different scan energies and for different mineral phases. Grayscale values for individual grains of unknowns and standards were normalized by the average value of the SL1 zircon shards on each mount for each set of scan parameters. The absolute grayscale value in the volumes depends on scanning conditions and reconstruction settings, thus internal normalization makes the results comparable and independent of these parameters.
We found that apatite grains have grayscale values of about 22 % and 27 % (at 60 and 130 kV, respectively) of those of zircon grains (Fig. 4). The distributions are broad due to intra-grain, inter-grain, and inter-sample variability, but the apatite and zircon populations are distinct from each other so that individual grains can be uniquely identified. This also confirms the theoretical modeling (Fig. 3) and the observations of different X-ray attenuation of apatite and zircon grains in the X-ray projections (Fig. 1). The grayscale value distribution of titanite overlaps partially with that of apatite and is sample dependent, making a phase distinction possible for some but not all grains. For example, the MG1 titanite mineral standard more closely overlaps the apatite grains than the “unknown” titanite crystals picked from USC-FCT1 and 2, which are systematically slightly brighter (Fig. 5).
The separation between all of the distributions is greater for 60 kV than for 130 kV, as predicted by the theoretical modeling above (Fig. 4). Therefore, volumes from scans at 60 kV can be used to resolve smaller differences in X-ray attenuation than at 130 kV, which does not have a pronounced effect on the apatite–zircon distinction but can be useful when trying to distinguish between apatite and titanite. However, lower-energy X-rays are less penetrating and lead to more artifacts and noise in the resulting reconstructed data (Hanna and Ketcham, 2017). Therefore, there is a trade-off between the absolute separation of phases in grayscale-value space and the signal-to-noise ratio, the latter of which can be improved by longer scan times.
Kernel density estimates (KDEs) of all apatite, zircon, and titanite grayscale value measurements (including standards) for 68 min scans calculated with an adaptive bandwidth equal to the standard deviation of grayscale variation within each grain. Each KDE is an aggregation of data from three different sample mounts and shows all individual data points. The grayscale value of each grain was normalized by the average grayscale value of SL1 zircon grains in the same volume. The difference between the attenuation of the three minerals is greater at 60 kV than at 130 kV, as theoretically predicted.
We observed good reproducibility for average normalized grayscale values of
populations of the same sample across the three mounts (Fig. 5). For
example, the average normalized grayscale values of Durango apatite shards
(UT-DUR) are all within uncertainty at
Although average grayscale values across grain populations are reproducible, we observe a range of grayscale values for individual replicate grains from the same sample or of shards from the same crystal (Fig. 5). This may be due to differences in bulk composition and structure. For example, natural apatites are solid solutions of three different endmembers which have different densities. The exact composition of any apatite grain will have an impact on its X-ray absorption and hence the observed grayscale value. Zircon density is mainly controlled by radiation damage (Holland and Gottfried, 1955), which can cause different densities for different grains or of parts of the crystal in the case of pronounced zoning of radioactive elements. The effect of differing grayscale values between different samples is most pronounced between the titanite standard in Mount C and the titanite from FCT samples in Mount B (see Fig. 5). The density of titanite has also been shown to be a function of crystal damage (Vance and Metson, 1985).
We segmented grains based on their outer surface and calculated the average grayscale value of the material enclosed by that surface. It is necessary to exclude the outermost grain boundary, because it commonly appears falsely brighter due to beam hardening. However, if there is internal heterogeneity, such as inclusions with higher or lower grayscale values, the observed average grayscale value of any particular grain can be affected (expressed as RSDs). Grains with a large fraction of inclusions of a particular type can therefore change the average grayscale value and might lead to misidentification. One strategy to mitigate this would be to filter certain histographic ranges of values within the segmented grains to exclude inclusions and measure only the average grayscale value of the host grain. Alternatively, this could also be used as a tool to identify individual crystals with inclusions, which would display higher or lower average grayscale values than the rest of the population.
The grayscale value distribution within a particular mineral grain is
dependent on the natural variation of density and composition (such as
zoning) as well as measurement noise. The absolute 2
Mean grayscale values (normalized by SL1 zircon) for all grains
measured in 60 kV/68 min scans, given with 2
The change of the attenuation coefficient with X-ray energy is a function of material density and composition, and is characteristic for each mineral (Alves et al., 2014). Therefore, the ratio of the attenuation at two different X-ray energies can be used as an additional parameter to identify the mineral phase of a grain (e.g., Hanna and Ketcham, 2017). We observed a clear distinction between apatite and zircon in this parameter as well (Fig. 6a). Titanite again appears similar to apatite, but the separation between the two distributions is greater in dual-energy space than in the 60 or 130 kV data alone. Therefore, this dual-energy parameter can be used as an additional tool to distinguish phases that have similar absolute attenuation coefficients, and hence appear similar in terms of grayscale values. This necessitates two scans of the same mount at two different energies, as well as additional processing to align the two scans and compute average grayscale values for both scans. However, the resulting data can be used to map regions in dual-energy vs. single-energy plots (Fig. 6b), yielding a more robust phase identification for individual grains.
We tested the grayscale variability introduced by grain size, spatial distribution of the grains on a mount, and direction of the mount during microCT data acquisition. Each of these factors can affect the path that X-rays take through the grains and the preferential attenuation of parts of the X-ray spectrum of a polychromatic beam (beam hardening), which can result in artifacts that cause changes of the average grayscale for a given grain unrelated to the actual mineral-specific X-ray attenuation. We found that image quality and signal-to-noise ratio improved with increased scan time (Fig. 7), as is expected based on counting statistics. We quantified variability in our data by calculating the relative standard deviation (RSD) of grayscale value within each segmented grain, which is a measure of both natural variability of the material and any superimposed measurement noise.
A clear distinction between apatite and zircon can already be observed in
the 18 s scans (Fig. 7), although the RSDs are high (0.2–0.3) for both
apatite and zircon grains. The RSDs decline with increasing scan time for
otherwise constant experimental conditions (Fig. 6), asymptotically
approaching
We also found that the orientation of the mount during data acquisition has a significant effect on the data quality. A vertical orientation, perpendicular to the source and parallel to the detector plane, produced much lower RSDs for the same scan conditions than a horizontal position (Fig. 8). Highly attenuating phases (such as zircon) produce artifacts such as shadowing and streaking (e.g., Hanna and Ketcham, 2017). When these artifacts overlap with other sample grains, they can significantly alter the observed grayscale value of parts of grains, which does not reflect their actual X-ray attenuation and leads to erroneous measurements with increased RSDs (Fig. 8). X-rays passing through a horizontal mount traverse several grains in most orientations and produce strongly expressed artifacts, whereas data acquisition in a vertical position significantly decreases the number of rays that pass through more than one grain. Therefore, particularly for samples with highly attenuating phases, we recommend scanning mounts in a vertical position to reduce noise and improve reproducibility. A tilted orientation can achieve similar results but makes data cropping more difficult. Scanning mounts horizontally is another, more common option that may be suitable depending on the phase of interest.
The size and arrangement of the grains on the mount also had an influence on the observed grayscale values and their RSDs. We tested these effects with a grain mount (Mount C) composed of only shards of known standards (apatite, zircon, and titanite). For a vertical scan, the horizontal position did not have an observable effect on the measured grayscale values of grains (Fig. 8a) but the vertical position did have a significant effect, with grayscale values decreasing downwards (Fig. 9b). This effect was observed for both apatite and zircon. Titanite showed an even greater dependence on the vertical position, but this trend was exaggerated by the predominance of smaller shards in the top row and larger ones in the bottom row of the mount. These spatial effects are likely caused by the inhomogeneity of the total X-ray attenuation at any height above the sample holder due to clustering of grains at certain heights. These spatial effects can be minimized by distributing known standards throughout the grain mount and normalizing sample grain measurements by the closest standard, and by avoiding lines or grid shapes when placing grains.
We observed a general trend of decreasing grayscale values with increasing grain size for the set of all grains of this mount (Fig. 9c). This trend can be explained by beam hardening (see Hanna and Ketcham, 2017), which results from the preferential attenuation of low-energy parts of the X-ray spectrum by highly attenuating material. This effect makes the center of highly attenuating regions appear darker. This artifact can lower the observed average grayscale value of a grain, producing measurements that are not solely related to the attenuation coefficient of a phase. This can be counteracted by choosing standard grains/shards that are matched in size to the unknown sample grains. If beam hardening occurs, it will affect all grains equally, thereby allowing for a direct, unbiased comparison of the average grayscale values of sample grains and standards.
The geometric effect discussed above can change the average observed grayscale values of grains by 5 %–10 %. Even with these effects, apatite can still be distinguished from zircon due to their large relative difference in X-ray attenuation. However, precautions should be taken when distinguishing apatite from titanite, which displays a much lower relative contrast (see Figs. 4, 5, 6), to ensure that data quality is high and phase identification is robust and unique.
Slices of selected grains
Slices of horizontal and vertical scans of the same grain mount show the reduction of artifacts for the vertical scan position relative to the horizontal scan position. Highly attenuating zircon (bright) grains produce shadowing artifacts that overlap with apatite (less bright) grains, altering the overall grayscale value measured in the apatite grains. Some shadowing still occurs in the vertical position but is much reduced relative to the horizontal position. This is reflected in the relative standard deviation (RSD) of the grayscale value within each set of grains. The arrangement of grains in a geometric pattern leads to amplification of artifacts. Note: photographs have increased contrast to highlight the differences in artifacts.
Plots showing the effect of spatial parameters on the grayscale
values of the grains on Mount C, which contains shards of known apatite,
titanite, and zircon crystals (see Fig. 2). The measured grayscale values
have been normalized by the average of all grains of that mineral. Linear
regressions (dashed lines) show approximate trends.
Based on the calibrations above, we share a workflow that allows the identification of apatite and zircon grains in grain mounts for geo- and thermochronology using microCT. The same dataset can be used for grain-specific 3D inclusion mapping, surface area, and volume measurements. The methodology described here has the potential to eliminate the need for highly toxic heavy liquids (MEI and bromoform), reduce time spent picking grains, and curtail misidentification of apatite and zircon in geo- and thermochronological analyses. Instead, this method enables users to quickly pick suitable-looking grains without close visual inspection and appraisal of interference colors, crystal shape, etc. in mixed apatite and zircon separates after using less toxic heavy liquids (LST, LMT, SPT). This can reduce time spent on the microscope, particularly for “difficult-to-pick” samples, such as those with very challenging grain morphologies or large volume separates. Although not done in this study, it is conceivable to sprinkle a mineral separate onto adhesive tape and use microCT to scout (bright) zircon grains prior to more directed picking or LA-ICP-MS. This approach may also be preferable in cases in which microscope picking is not an accessible task (e.g., due to the physical setup, frequent migraines, etc.).
We found that using clear plastic slides (thickness
Unknown mineral grains can be picked from a separate and placed directly onto the grain mount with tweezers or a needle. The grains should be placed onto the adhesive tape firmly enough to ensure that enough surface area of the grain is in contact with the tape, but not so firmly that the grain breaks. We recommend strategically distributing the unknown grains in such a way that any individual grain can be easily identified after microCT for further analysis. Grains should be spaced at least one grain length apart to minimize the effect of artifacts from highly attenuating phases. Forming lines or a grid of grains should be avoided, since these shapes tend to amplify artifacts. Known mineral standards of expected phases should be included on every grain mount. They can be shards of larger crystals or mineral grains that have been identified by an independent method, such as through micro-Raman spectroscopy. These standard grains should broadly match the grain sizes of the unknowns and be distributed throughout the grain mount in the same way as the unknowns, to account for any spatial variation in X-ray attenuation. In some cases, the mineral standard can also be used as the age standard for further analysis (e.g., Durango apatite).
Vertical grain mount scans produce better overall results by reducing microCT artifacts (see Fig. 7). However, horizontal scans are likely sufficient in many applications, such as distinguishing apatite and zircon, and allow multiple grain mounts to be stacked on top of the sample holder. This allows four times the number of grains in a single scan (up to 400 grains). The resulting file sizes will be bigger, but the scan time is the same.
Scan time will vary based on the instrument. Here we show that for simple
mineral identification, rapidly acquired (
For some geo- and thermochronology applications it is necessary to detect
inclusions or fractures and measure grain volume and surface area. For these
applications, in addition to mineral verification, we recommend longer scan
times (
Herein, we present a rapid method for identifying or verifying apatite and/or zircon crystals in separates using microCT as a screening technique. This can serve several purposes depending on the goal of the research. First, it can reduce the misidentification of minerals prior to costly and time-intensive analyses. In the case of precious or low-yield samples, reducing human error is especially important.
The 3D grain-specific measurements acquired during the microCT scan provide
added value to (U-Th)/He thermochronology research, where grain shapes are
used to calculate
For detrital geochronology, the microCT pre-screening method described here can be used to identify mineral phases regardless of grain geometry, thereby enabling the use of grains with less-than-ideal geometries. Since apatite and zircon are mainly picked under a binocular microscope based on their grain shape, sub-euhedral or broken crystals, which typically represent the bulk of the crystals in a given separate, are often not chosen for further analysis. This can present a problem for samples with low yields or bias the results toward grains of specific morphologies (i.e., histories or age populations).
Furthermore, this method can be expanded beyond apatite, zircon, and
titanite. For example, we did not analyze monazite or rutile in this study.
However, based on MuCalc modeling and the characteristics of the microCT
scans analyzed here, monazite and rutile should be distinguishable from
apatite, zircon, and titanite at X-ray energies below
In laboratories with ready access to a microCT instrument, this protocol can be incorporated into the primary workflow for (U-Th)/He analysis and reduce the amount of time spent at the picking microscope. Apatite and zircon grains can be placed directly onto a microCT mount without the need for careful identification or 2D measurements. A 2 h microCT scan would provide mineral ID verification, screen for inclusions or fractures, and provide 3D grain-specific volume and surface area measurements. Once data reduction and processing protocols are established and users are trained, data analysis can take anywhere from 15 min to a few hours, depending on the size of the dataset. More than 100 grains (including known mineral standards) can be placed onto a single mount and scanned vertically, or multiple mounts can be stacked horizontally, allowing for several hundred grains to be scanned and analyzed in a single session.
If microCT access is less available, the protocol may be used for particularly difficult-to-identify, precious, or low-yield samples. This technique can also be used for detrital zircon studies (U-Pb or (U-Th)/He) to reduce sampling bias toward more morphologically perfect crystals by pre-screening a large number of grains and using microCT to identify zircon grains for further analysis based on their density rather than grain shape.
We show that microCT pre-screening of grains picked from separates can be used to unequivocally distinguish apatite and zircon, and to distinguish apatite and zircon from other phases, such as titanite, with a degree of certainty. Normalizing grayscale values of grains from microCT volumes by the average value of a known zircon standard accounted for differences in experimental setup, instrument performance, and processing from one mount to the next. The remaining observed variation of grayscale values within and between grains is likely due to grain-specific natural variability of material parameters, such as crystal damage and elemental substitution.
We recommend the following best practices for future studies:
Mineral standards for normalization should be matched in size to the unknown
samples to account for the effect of beam hardening. Standards should be distributed throughout the mount, and sample grains
should be normalized by the closest standard grain to minimize minor spatial
effects. The mount should be tilted vertically for microCT data acquisition to
reduce the effect of shadowing from neighboring grains. MicroCT instrument
geometries other than the one used here might require different mount
orientations. For the particular microCT instrument used here, the signal-to-noise ratio
did not improve significantly past 17 min for continuous scans. A step scan
of about 2 h (50 min counting time) was sufficient to produce
high-resolution data with a usable signal-to-noise ratio.
MicroCT scans that are set up according to the recommendations represent a robust
method for distinguishing between apatite and zircon in mounts of selected
grains. This offers a possible alternative to separating apatite from zircon
using highly toxic MEI. Grains can be picked directly from separates that
have undergone density separation with non-toxic LST, LMT, or SPT, which
is a less laborious and safer process. As an additional benefit, the data
acquired in this process can also be used to screen the sample grains for
fluid and mineral inclusions and to model alpha-ejection and alpha-implantation
corrections for (U-Th)/He dating (Evans et al., 2008; Cooperdock et al.,
2019).
Reconstructed microCT volumes for all mounts, X-ray energies, and scan times are stored at the USCHelium Lab and are available on request.
The supplement related to this article is available online at:
EHGC and FH conceptualized the study and experimental design with input from AT; AC collected FCT samples; FH, RMCT, and AC prepared samples and collected data; all co-authors contributed to data interpretation; FH and RMCT prepared figures; EHGC and FH prepared and edited the manuscript draft with input from RMCT, AC, AT, and AJC.
Aya Takase is employed by Rigaku, which manufactures the micro-computed tomography instrument used in this study.
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
We thank Justine Grabiec and Alexia Rojas for help with mineral separation; Danny Stockli and members of the UTChron laboratory for providing Fish Canyon Tuff and Durango samples; and Ken Farley for providing the Caltech Durango sample. We thank Kalin McDannell, Paul O'Sullivan, and Ryan Ickert for useful discussions about heavy liquid safety, and James Metcalf for FCT sampling information. We also thank Alan Gregorski and Aaron Alke for help sampling the FCT. We thank Greg Balco and two reviewers for improving the content and clarity of this manuscript. We thank Shigeru Sueoka for manuscript handling.
This research has been supported by the University of Southern California (Start-up and WiSE Major Support Funding grant).
This paper was edited by Shigeru Sueoka and reviewed by two anonymous referees.