Due to the lack of atmosphere on the Moon,
the lunar surface is constantly exposed to
solar wind irradiation, galactic cosmic
radiation, solar energetic particles, and
micrometeorite impacts. While these
processes are generally considered
destructive, some scientists believe that solar
wind exposure and micrometeorite impacts
significantly contribute to the formation and
movement of water on the lunar surface.
In this study, we perform deuterium ion irradiation on silicon dioxide, a lunar-relevant material, to recreate subsurface vesicles that may contain deuterium-bearing species. We examine the ion-induced damage to these materials to calibrate the deuterium ion source exposure parameters necessary for proper vesicle formation. Our ultimate goal is to use the calibrated deuterium ion source to irradiate Apollo 17 sample 75035.232 and recreate vesicles.
To recreate the conditions of solar wind bombardment, we used a deuterium ion source with variable flux and exposure time (Figure 2). The energy of the deuterium ions is set constant at 2 keV. Additionally, an electron beam was employed to prevent charging during the irradiation of the silicon dioxide samples. The fluence (ions/cm²) of the deuterium source was adjusted by varying the flux and exposure time, facilitating vesicle formation.
During irradiation, we used samples of silicon dioxide
with a 300 nanometer oxide layer on a silicon wafer.
The samples were exposed to various
fluences, and the resulting surface damage
was analyzed using atomic force microscopy
(AFM) with a Bruker Dimension Icon.
Quantitative analysis of blister coverage in
silicon dioxide was performed using the StarDist
The AFM data of exposed silicon dioxide reveal the creation of
blisters on the surface that we believe
contain deuterium species. However, at
fluences above
Segmentation results using StarDist on an AFM image of exposed silicon dioxide provide spatial coordinate data for each blister, allowing for the calculation of metrics such as total blister percentage, average blister size, and blister size deviation (Figure 4).
In addition to using StarDist, I
experimented with another computational
approach for segmenting blisters and
extracting coordinate data. This method
involved a convolutional neural network
(CNN) with a modified U-net
First, while semantic segmentation effectively classifies pixels into categories, it does not provide a way to extract coordinate data when blisters overlap. Since deuterium blisters begin to overlap at higher fluences (Figure 3), this limitation becomes critical when extracting metrics such as average blister size. Second, the quantity of AFM image data collected was insufficient to train an accurate segmentation algorithm. Although data augmentation can increase dataset sizes, our collected AFM image data was still inadequate. StarDist addresses both issues by using star-convex polygons to represent blisters and leveraging a pretrained neural network. From this, we can accurately extract the average size and density of blisters.
The relationship between ion fluence and
blister coverage shown in Figure 5 indicates
that a deuterium fluence of less than
Our future work includes performing
irradiation with the calibrated fluence on
anorthite, a lunar-relevant
mineral that is more complex than silicon dioxide. We will use focused ion
beam (FIB) lift-outs and scanning
transmission electron microscopy (STEM)
to analyze subsurface vesicle formation in
anorthite. Upon confirmation of vesicle
formation in anorthite, we will irradiate
lunar sample 75035.232 with the same
fluence, perform FIB lift-outs and STEM,
and analyze any vesicles present.
Additionally, we will use nano-FTIR
[1] Jones, B. M., Aleksandrov, A., Hibbitts, K., Dyar, M. D., & Orlando, T. M. (2018). Solar wind‐induced water cycle on the Moon. Geophysical Research Letters, 45(20), 10-959.
[2] Managadze, G. G., Cherepin, V. T., Shkuratov, Y. G., Kolesnik, V. N., & Chumikov, A. E. (2011). Simulating OH/H2O formation by solar wind at the lunar surface. Icarus, 215(1), 449-451.
[3] Bradley, J. P., Ishii, H. A., Gillis-Davis, J. J., Ciston, J., Nielsen, M. H., Bechtel, H. A., & Martin, M. C. (2014). Detection of solar wind-produced water in irradiated rims on silicate minerals. Proceedings of the National Academy of Sciences, 111(5), 1732-1735.
[4] Badyukov, D. D. (2020). Micrometeoroids: the Flux on the Moon and a Source of Volatiles. Solar System Research, 54, 263-274.
[5] Pieters, C. M., Goswami, J. N., Clark, R. N., Annadurai, M., Boardman, J., Buratti, B., ... & Varanasi, P. (2009). Character and spatial distribution of OH/H2O on the surface of the Moon seen by M3 on Chandrayaan- 1. science, 326(5952), 568-572.
[6] Burgess, K. D., Cymes, B. A., & Stroud, R. M. (2023). Hydrogen-bearing vesicles in space weathered lunar calciumphosphates. Communications Earth & Environment, 4(1), 414.
[7] Schmidt, U., Weigert, M., Broaddus, C., & Myers, G. (2018). Cell detection with star-convex polygons. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part II 11 (pp. 265-273). Springer International Publishing.
[8] Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18 (pp. 234-241). Springer International Publishing.
[9] Huth, F., Govyadinov, A., Amarie, S., Nuansing, W., Keilmann, F., & Hillenbrand, R. (2012). Nano-FTIR absorption spectroscopy of molecular fingerprints at 20 nm spatial resolution. Nano letters, 12(8), 3973- 3978.