Data from: Detecting sub-micron space weathering effects in lunar grains with synchrotron infrared nanospectroscopy
Utt, Kainen et al. (2022), Data from: Detecting sub-micron space weathering effects in lunar grains with synchrotron infrared nanospectroscopy, Dryad, Dataset, https://doi.org/10.5061/dryad.xsj3tx9fd
Space weathering processes induce changes to the physical, chemical, and optical properties of space-exposed soil grains. For the Moon, space weathering causes reddening, darkening, and diminished contrast in reflectance spectra over visible and near-infrared wavelengths. The physical and chemical changes responsible for these optical effects occur on scales below the diffraction limit of traditional far-field spectroscopic techniques. Recently developed super-resolution spectroscopic techniques provide an opportunity to understand better the optical effects of space weathering on the sub-micrometer length scale. This paper uses synchrotron infrared nanospectroscopy to examine depth-profile samples from two mature lunar soils in the mid-infrared, 1500–700 cm-1 (6.7–14.3 µm). Our findings are broadly consistent with prior bulk observations and theoretical models of space weathered spectra of lunar materials. These results provide a direct spatial link between the physical/chemical changes in space-exposed grain surfaces and spectral changes of space-weathered bodies.
We used an iterative, non-linear, and robust peak fitting (deconvolution) procedure to determine peak parameters for SINS phase spectra. The procedure uses a trust-region minimization algorithm to fit a linear combination of Lorentzians with a local linear background to the data. Robustness is ensured by minimizing the residuals’ summed square and using bi-square weighting to reduce the impact of outliers. For more detail, see the Supporting Information for the associated publication.
Data Set S1. Raw SINS spectra for studied samples (sections 1–4 and the terrestrial anorthite standard). The relevant folders additionally contain the background spectral files (.CSV) that were used. These files are organized by section and scan.
Data Set S2. This dataset is comprised of background-referenced and Fourier-transformed SINS spectra (.CSV). Note that both the amplitude and phase signals are included here. These files are organized by section and scan.
Data Set S3. The compressed folder contains the initial conditions for the iterative fitting model as well as the peak parameters calculated thereby. These data are formatted as .CSV files.
Data Set S4. The compressed folder contains 16 animated .GIF files (one for each linescan) displaying the fitted, un-renormalized phase spectra from each point in the linescan. The general darkening trend can be seen in spite of the noise.
Data Set S5. The individual frames of the animations included in Data Set S4. Files are saved in .PNG format with a name in the style of:
Data Set S6. This dataset contains the fitted spectra (.CSV) and 2σ confidence intervals (CI). The files contained in this dataset are formatted such that the first column contains the wavenumbers, the second column contains the fitted spectra evaluated at that wavenumber, and the third and fourth columns contain the CI for the fit.