Sample preparation. A large 1.894 kg fragment of Aguas Zarcas was recovered rapidly after its fall before rain, purchased by Terry Boudreaux and donated to the Field Museum of Natural History. At the Field Museum this specimen, FMHN ME 6112, is stored in a stainless-steel cabinet in an inert nitrogen atmosphere at room temperature. 79 g fragments were separated from a large sample of AZ, FMHN ME 6112 with cleaned stainless-steel tools in a nitrogen-filled glove bag. We used freeze-thaw disintegration as the first step of an effort to separate objects of interest including refractory minerals, isolated olivine grains, and presolar grains from the fine-grained matrix and organic matter of AZ. The selected pieces were roughly divided into ten ~8-gram chunks in ultrapure water (18.2 MΩ∙cm electrical resistivity; Milli-Q) and each was disintegrated using alternating cycles of liquid nitrogen and 50°C water. Typically, 30 cycles can break down the matrix of a CM2 chondrite like Murchison into powder. With AZ, most fragments were disaggregated within 50 cycles, however, more than 10 sub-cm-sized fragments (3.2 wt%) remained intact and showed no signs of mechanical breakdown after 112 cycles. We call these intact fragments “compact AZ”.
11 compact AZ fragments and 3 randomly chosen AZ (FMNH ME6110.1) fragments that were not processed by freeze-thaw were µCT-scanned at the PaleoCT facility of the University of Chicago. The fragments of compact AZ are named from CF-1 to CF-11, and the randomly selected AZ fragments are named from RF-1 to RF-3.
X-ray microtomography. We scanned all AZ samples (CF-1 to CF-11 and RF-1 to RF-3) at the University of Chicago’s PaleoCT Lab, on a GE v|tome|x S model micro-CT scanner using the 240 kV microfocus tube. The fragments were mounted in a 15 ml tube and scanned at a spatial resolution (or voxel size) of 17.028 µm. An 80 kV voltage and 220 µA current were used with an image acquisition time of 500 ms per frame. Three frames were captured and averaged for each position to reduce noise and a 0.2 mm Cu filter was used to reduce beam hardening. The total scan time for the tube of specimens was one hour and fifty minutes.
Two of the larger pieces of AZ (one compact AZ, CF-10, 0.591 g; one regular AZ, RF-1, 0.730 g) as well as a specimen of Murchison (FMNH ME2644; 1.171 g) and of Leoville (FMNH ME2628.2; 1.706 g) for comparison, were scanned at the University of Texas High-Resolution X-ray Computed Tomography Facility (UTCT) at higher resolution. These four samples were scanned on a Zeiss Versa 620 at 80 kV and 125 µA with varying acquisition time per frame (40–70 ms) with one frame per position for all samples except Leoville, which had 2 frames per position. The LE3 filter was used for all scans and a beam hardening correction was applied during reconstruction with the scanner software, and the final voxel size of each sample scan was 6.77 µm (both AZ fragments; scan time 59 minutes each), 8.47 µm (Murchison; scan time 53 minutes) and 11.01 µm (Leoville; scan time 74 minutes).
Scanning Electron Microscopy. After µCT scanning the compact fragment CF-10 was embedded in Buehler EpoxiCure 2 epoxy and cross-sectioned parallel to the long axis of the flattened chondrules with a Buehler IsoMet low-speed diamond wafering saw. The section was coarsely polished with diamond Allied High Tech Products Inc. lapping film followed by a final polish with Allied 1 µm diamond slurry. The polished mount was imaged and mapped with a field-emission TESCAN LYRA3 SEM/FIB equipped with two Oxford Instruments XMax SDD 80 mm2 energy dispersive X-ray spectroscopy (EDS) detectors at the University of Chicago. An EDS map, a backscattered electron (BSE) map, and a secondary electron (SE) map were acquired with an acceleration voltage of 15 kV and a beam current of 470 pA and a typical pixel dwell time of 25 µs at a nominal spatial resolution of 0.369 and 0.185 µm per pixel, respectively. EDS spectra were used to determine the mineral chemistry qualitatively at an accuracy of <5 wt%. Cross-sectional morphology and texture including fractures were examined with the EDS, SE, and BSE maps using Oxford Instruments AZTec software.
Chondrule segmentation and deformation analysis. First, we determined the µCT components in compact AZ by calibrating the CT data by comparing BSE and EDS maps of the polished cross section of compact AZ fragment CF-10 with a matching µCT slice. There are three types of objects in AZ that we identified based on their grayscale values within the µCT data (Supplementary Fig. 5): small bright objects without well-defined shapes, light-toned objects, and dark-toned objects. Here we mainly discuss chondrules and neglect irregular clasts. Earlier µCT studies10,14 of Murchison show that the brightest components are metal and sulfides such as pentlandite and that light-toned and dark-toned objects are mostly Fe-bearing chondrules/CAIs and Fe-poor/Mg-rich chondrules, respectively. SEM data of the polished AZ CF-10 confirm the same µCT components as in Murchison. In the µCT data of Leoville, we only observed dark chondrules and bright metal/sulfides. µCT data of regular AZ shows the same three object types as compact AZ. According to the previous research10,14, dark-toned objects (i.e., Mg-rich chondrules) are typically more deformed and display a stronger fabric compared to bright (metal and sulfide) and light-toned (Fe-bearing chondrules and CAIs) objects. Regardless of the reason for this observation, we only delineated and segmented the dark-toned objects in µCT data and calculated the fabric strength to avoid any potential observational bias with objects of different X-ray contrast.
Secondly, we outlined components of interest (here dark-toned chondrules) from the tomographic data set into distinct volumes of interest. We used manual segmentation in 3D Slicer software (http://slicer.org) where we used the “draw” tool to mark chondrules in individual 2D slices, then filled between slices to obtain a 3D visualization27. This method is labor-intensive and time-consuming if performed for every chondrule in the dataset. Therefore, we only applied it to small fragments and used a more efficient alternative, the partial segmentation method, to large ones. For partial segmentation, one or more representative cross-sections in each chondrule’s orthogonal plane are chosen for segmentation excluding the ambiguous chondrules such as those that are in contact with each other. The effectiveness of this method to accurately calculate the orientation and degree of anisotropy of objects in rocks relative to the full segmentation has been examined and confirmed10. In this study, we used the whole segmentation method for samples scanned at University of Chicago, as these data sets are small due to their lower resolution, as well as for Leoville, where our scanned volume contains only a few chondrules due to their relatively large size. For the remaining datasets, we used the partial segmentation method.
Thirdly, after segmentation with 3D Slicer, we exported all the segments to DICOM files, loaded them into Fiji, and converted them to TIFF files. For each chondrule we used Blob3D28,29 (http://www.ctlab.geo.utexas.edu/software/blob3d/) to determine the size, location, and orientation information via best-fit ellipsoids to either the full segmentation or partial segmentation via a set of orthogonal planes. Orientation biasing can occur when an object covers only a few voxels. To avoid that, we removed objects with a short axis of less than 3 voxels10. In order to make the data volume manageable, we divided each large tomographic data set into several subvolumes and segmented chondrules within each individual subvolume. This enabled faster processing of the data and a reduction in file size. We segmented 825 dark-toned objects in total. Parameters of best-fit ellipsoids to each object are shown in Supplementary Table 1.
Fabric analysis of the tomographic data in this work follows an established method10, and further details regarding parameter calculations reported in Supplementary Table 1 can be found in that work. Here we briefly introduce the quantitative analysis of deformation. We take the direction vectors of a set of axes of the fitted ellipsoids as an example. These directions are plotted on stereonets in a lower hemisphere projection, and the forming pattern is used to test whether the orientations are non-random. Meanwhile, Woodcock and Naylor13 defined K and C parameters to describe the shape and strength respectively of a fabric. An orientation tensor (3\(\times\)3 matrix) is calculated from the above direction vectors, and three eigenvectors of the tensor are defined as S1, S2, S3. K is defined as K=ln (S1/S2)/(S2/S3), and C is defined as C=ln (S1/S3). K ranges from zero (girdle or ‘great circle’ distribution on a stereonet for K <1) to infinite (cluster distribution for K > 1)30. C ranges from zero (no fabric) to 4 or above (strong fabric) and is manifested as the concentration of data points on a stereonet30. Supplementary Fig. 6 illustrates K and C parameters and the chondrule orientations for 4 types of rocks in this study.
Shock Pressure determination. Previous studies8,18 used Murchison and Allende in shock-recovery experiments to build up empirical relationships between aspect ratio and shock pressure in a single impact shock event. In Murchison, 10 GPa was a threshold over which the aspect ratio started transferring from ~1.2 to ~1.5 (see Fig. 3). Meanwhile, 20 GPa was another threshold over which the aspect ratio kept approximately constant. Specifically, the aspect ratios of chondrules in an impacted sample had a large range, but the distribution of the aspect ratios moved clearly with an increasing shock pressure. Accordingly, the mean values of those ratios rose. Also, the aspect ratios of unshocked Murchison’s and Allende’s chondrules were not counted for linear regression. In the shock-recovery experiments, the recovered samples were cut along the shock compacting axis, such that the mean 2D aspect ratio of chondrules in the section was most comparable to the mean ratio of the longest axis length to the shortest axis length in our 3D model (called 3D aspect ratio).
Lithostatic pressure Model. At depth within a spherical asteroid, the force balance is as follows: \(GM/{r}^{2}\bullet \rho 4\pi {r}^{2}dr=-4\pi {r}^{2}dP\), when r is the radial distance from the center of the parent body, G is the gravitational constant, M is the mass of the material below r, \(\rho\) is the density, P is the pressure. The left part is the gravitational force of a shell with a width of dr at a radial distance r from the center, and the right part is the supporting force provided by the pressure gradient. Due to \(M=4/3\pi {r}^{3}\rho\), the simplified equation of force balance is \(dP=-4/3\pi G{\rho }^{2}rdr\). The solution is\(P=2/3\pi G{\rho }^{2}({R}^{2}-{r}^{2})\), where R is the radius of the parent body. When r=0, P reaches the maximum that is the pressure at the center. Because most stony meteorites have densities31,32 on the order of 3 to 4 g cm−3, we take \(\rho\)=3.5 g cm−3 in the model. We consider two cases to visualize the pressure profiles in meteoritic parent bodies. One is the maximum pressure (center pressure) for spherical objects with different sizes; the other is the depth-pressure profile for a 100-km-sized body (Supplementary Fig. 3). The calculated maximum pressure for the Moon is 5.2 GPa, and most petrological experiments and seismic detections all support a ~5 GPa pressure at the lunar core or core-mantle boundary33−35. The maximum pressure for a 100-km-sized body is <0.02 GPa.
Monte Carlo Model. The movement of ejecta on Bennu is controlled by multiple forces such as Bennu’s gravity, solar radiation, reflected pressure, Poynting-Robertson effect, etc. The gravitational force in the vicinity of Bennu is 2 to 6 orders of magnitude higher than the other forces2, therefore, and for simplicity, we consider it as the only driving force in our model. Besides this, we set up initial conditions that include initial velocities of particles, launch position, and rotation of the central body. The observed velocity of Bennu ejecta ranges from 0.05 m s−1 to >3.3 m s−1, and the observations may not include all particles especially fast-moving ones. Thus, we take 0.05–5 m s−1 as the initial velocity range. The particles can be ejected from anywhere on the surface but were more frequently observed from low latitudes. We adopt the distribution of ejection sites from Chesley et al.2 to our model. Generally, the spin period of asteroids decreases with their size and clusters between 2–12 hours36. The rotation period for Bennu is 4.3 hours, and we apply this to all the simulations in this study.
First, we modeled particle movement on a spherical body whose mass, bulk density, and radius are the same as that of Bennu. 50,000 particles were released, and only those with low velocities (<0.35 m s−1) fell back on the surface. Supplementary Fig. 4 depicts the distribution of the sine of latitude for ejecta deposition. We also ran our model with larger asteroids with 10 to 100 Bennu radii. Bennu’s bulk density is low ~1.26 g cm−3 because it is a rubble pile asteroid with a high porosity37. Nevertheless, the fragment density should be close to that of its meteorite analog AZ (~2.4 g cm−3). Here we argue that 2.4 g cm−3 is the approximate upper limit for such carbonaceous chondrites because it ignores the pore space in the parent body. The mean densities of C, S, and M class asteroids are 1.38, 2.71, and 5.32 g cm−3, respectively, from calculations38. We adopted 1.26 and 2.4 g cm−3 separately in our model.