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- Creator:
- Su, Xue, Zhang, Youxue, and Liu, Yang
- Description:
- Our recent investigations have discovered inward diffusion (in-gassing) of moderately volatile elements (MVEs; e.g., Na, K and Cu) from volcanic gas into volcanic beads/droplets. In this work, we examine the distribution of sulfur in lunar orange glass beads. Our analyses reveal that sulfur exhibits a non-uniform distribution across the beads, forming "U" or "W" shaped profiles typical of in-gassing. A model developed to assess sulfur contributions from different sources (original magmatic sulfur versus atmospheric in-gassed sulfur) in the orange beads indicates that atmospheric sulfur in-gassed during eruption contributes approximately 9–24% to the total sulfur content of an orange bead, averaging around 16%. This in-gassed sulfur is derived from the eruption plume, where atmospheric sulfur could undergo photochemical reactions induced by UV light, leading to mass independent fractionation and a distinct sulfur isotope signature. Interestingly, a recent study discovered a small mass independent isotope fractionation of sulfur in lunar orange glass beads in drive tube 74002/1 and a lack of such mass independent isotope fractionation in black glass beads in the same lunar sample. This finding contrasts with sulfur in lunar basalts, which typically exhibit mass dependent fractionation. With our work, the observed mass independent fractionation signal in sulfur isotopes of orange beads can be attributed to the in-gassing of photolytic sulfur in the optically thin part of the eruption plume where UV light can penetrate. Using the sulfur isotope data of lunar orange beads, we estimate that the Δ33S value of atmospheric sulfur is approximately −0.18‰. Our study provides new insights into the complex dynamics of volatile elements in lunar volcanic processes, highlighting the role of in-gassing in shaping sulfur isotope signatures in volcanic glass beads.
- Keyword:
- Moon, Lunar orange glass beads, Sulfur, Sulfur isotope, Diffusion, Outgassing and in-gassing, Mass independent fractionation, and Eruption plume
- Citation to related publication:
- Su, X., Zhang, Y., Liu, Y. (2024) Sulfur Outgassing and In-gassing in Lunar Orange Glass Beads and Implications for 33S “Anomaly” in the Moon. (under review)
- Discipline:
- Science
-
- Creator:
- Hutson, Abby , Fujisaki-Manome, Ayumi, and Glassman, Ryan
- Description:
- The data herein resulted from a study documenting the characteristics of extratropical cyclones that pass through the Great Lakes Region (GLR) and how the cyclones are trending with time. All scripts used to create these data can be found in the Github repository https://github.com/abkenyon/GLStormTrends_2024. storm_track_slp_xxxx.npz - Structured numpy files containing all storm tracks identified over one cold season, regardless of whether the storm encountered the GLR, with the file name indicating the year on which the season ended. storm_composite_xxxx-xxxx.nc - NetCDF files containing one seasonal cyclone composite with different atmospheric variables. A composite is storm-centered, and covers a 20 degree square area.
- Keyword:
- Extratropical Cyclones, Climate Trends, Great Lakes Climate
- Citation to related publication:
- Hutson A, Fujisaki-Manome A, Glassman R.: Historical Trends in Cold-Season Mid-Latitude Cyclones. Geophysical Research Letters. In press..
- Discipline:
- Science
-
- Creator:
- Giri, Bapun, Kinsky, Nathaniel, and Diba, Kamran
- Description:
- The research that produced this data tested how sleep loss impacted the phenomena of reactivation and replay, which occurs when recently-learned information is reactivated/replayed during post-learning sleep/rest.
- Keyword:
- Hippocampus, Memory, Sleep, Sleep Deprivation, and Electrophysiology
- Citation to related publication:
- Giri, B., Kinsky, N.R., Kaya, U., Maboudi, K., Abel, T., Diba, K. (2024). Sleep loss diminishes hippocampal reactivation and replay. Nature, (2024). https://doi.org/10.1038/s41586-024-07538-2
- Discipline:
- Science
-
- Creator:
- Gottesman, Ari
- Description:
- This study analyzes correlations between magnetic field data from closely-spaced pairs of ground magnetometers to observe the spatial scale of ionospheric current signatures. Correlations were mainly calculated in 7.5 minute intervals for periods of multiple days. Distributions were taken from the collection of these 7.5 minute intervals to identify the amount of time where the magnetometers were observing "similar" or "different" ionospheric signatures. The raw magnetometer data was taken from two geomagnetic storms: one taking place on 7-8 September, 2017, and the other taking place on 23-24 March, 2023. These periods were selected due to the presence of both high and low geomagnetic activity. The final distributions calculated from this analysis are available in Correlation_Distributions.csv.
- Keyword:
- Space Weather Impacts, Geomagnetically Induced Currents, and GIC
- Discipline:
- Science
-
- Creator:
- Liemohn, Michael W
- Description:
- Earth’s upper atmosphere above 500 km altitude constantly loses charged particles to outer space in a process called ionospheric outflow. This outflow is important for the dynamics of the near-Earth space environment (“space weather”) yet is poorly understood on a global scale. A mission is needed to observe the global patterns of ionospheric outflow and its relation to space weather driving conditions. The science objectives of such a mission could include not only the reconstruction of global outflow patterns but also the relation of these patterns to geomagnetic activity and the spatial and temporal nature of outflow composition. A study is presented to show that four well-placed spacecraft would be sufficient for reasonable outflow reconstructions.
- Keyword:
- ionosphere, magnetosphere, satellite mission concept, and space weather
- Citation to related publication:
- Liemohn, M. W., Jörg-Micha Jahn, Raluca Ilie, Natalia Y. Ganushkina, Daniel T. Welling, Heather Elliott, Meghan Burleigh, Kaitlin Doublestein, Stephanie Colon-Rodriguez, Pauline Dredger, & Philip Valek (2024). Reconstruction analysis of global ionospheric outflow patterns. Journal of Geophysical Research Space Physics, 129, e2023JA032238. https://doi/org/10.1029/2024JA032238
- Discipline:
- Science
-
- Creator:
- Hong, Yi, Fry, Lauren M., Orendorf, Sophie, Ward, Jamie L., Mroczka, Bryan, Wright, David, and Gronewold, Andrew
- Description:
- Accurate estimation of hydro-meteorological variables is essential for adaptive water management in the North American Laurentian Great Lakes. However, only a limited number of monthly datasets are available nowadays that encompass all components of net basin supply (NBS), such as over-lake precipitation (P), evaporation (E), and total runoff (R). To address this gap, we developed a daily hydro-meteorological dataset covering an extended period from 1979 to 2022 for each of the Great Lakes. The daily P and E were derived from six global gridded reanalysis climate datasets (GGRCD) that include both P and E estimates, and R was calculated from National Water Model (NWM) simulations. Ensemble mean values of the difference between P and E (P – E) and NBS were obtained by analyzing daily P, E, and R. Monthly averaged values derived from our new daily dataset were validated against existing monthly datasets. This daily hydro-meteorological dataset has the potential to serve as a validation resource for current data and analysis of individual NBS components. Additionally, it could offer a comprehensive depiction of weather and hydrological processes in the Great Lakes region, including the ability to record extreme events, facilitate enhanced seasonal analysis, and support hydrologic model development and calibration. The source code and data representation/analysis figures are also made available in the data repository.
- Keyword:
- Great Lakes, Hydrometeorological, National Water Model, Daily, Overlake precipitation, Overlake evaporation, Total runoff, Net Basin Supply, and Water Balance
- Discipline:
- Science and Engineering
-
- Creator:
- Lee, Shih Kuang, Tsai, Sun Ting, and Glotzer, Sharon C.
- Description:
- The trajectory data and codes were generated for our work "Classification of complex local environments in systems of particle shapes through shape-symmetry encoded data augmentation" (amidst peer review process). The data sets contain trajectory data in GSD file format for 7 test systems, including cubic structures, two-dimensional and three-dimensional patchy particle shape systems, hexagonal bipyramids with two aspect ratios, and truncated shapes with two degrees of truncation. Besides, the corresponding Python code and Jupyter notebook used to perform data augmentation, MLP classifier training, and MLP classifier testing are included.
- Keyword:
- Machine Learning, Colloids Self-Assembly, Crystallization, and Order Parameter
- Citation to related publication:
- https://doi.org/10.48550/arXiv.2312.11822
- Discipline:
- Other, Science, and Engineering
-
- Creator:
- Sun, Hu, Ren, Jiaen, Chen, Yang, Zou, Shasha, Chang, Yurui, Wang, Zihan, and Coster, Anthea
- Description:
- Our research focuses on providing a fully-imputed map of the worldwide total electron content with high resolution and spatial-temporal smoothness. We fill in the missing values of the original Madrigal TEC maps via estimating the latent feature of each latitude and local time along the 2-D grid and give initial guess of the missing regions based on pre-computed spherical harmonics map. The resulting TEC map has high imputation accuracy and the ease of reproducing. All data are in HDF5 format and are easy to read using the h5py package in Python. The TEC map is grouped in folders based on years and each file contains a single-day data of 5-min cadence. Each individual TEC map is of size 181*361. and WARNING: 2023-12-01 the data file for 2019-Jan-03 has badly fitted values. Please avoid using it. All other days' files are ready to use.
- Keyword:
- Total Electron Content, Matrix Completion, VISTA, Spherical Harmonics, and Spatial-Temporal Smoothing
- Citation to related publication:
- Sun, H., Hua, Z., Ren, J., Zou, S., Sun, Y., & Chen, Y. (2022). Matrix completion methods for the total electron content video reconstruction. The Annals of Applied Statistics, 16(3), 1333-1358., Sun, H., Chen, Y., Zou, S., Ren, J., Chang, Y., Wang, Z., & Coster, A. (2023). Complete Global Total Electron Content Map Dataset based on a Video Imputation Algorithm VISTA. Scientific Data, in press., and Zou, S., Ren, J., Wang, Z., Sun, H., & Chen, Y. (2021). Impact of storm-enhanced density (SED) on ion upflow fluxes during geomagnetic storm. Frontiers in Astronomy and Space Sciences, 8, 746429.
- Discipline:
- Science and Engineering
-
- Creator:
- Ponder, Brandon M., Ridley, Aaron J., Goel, Ankit, and Bernstein, Dennis S.
- Description:
- This research was completed to statistically validate that a data-model refinement technique could integrate real measurements to remove bias from physics-based models via changing the forcing parameters such as the thermal conductivity coefficients.
- Keyword:
- Thermosphere, GITM, CHAMP, GRACE, MSIS, Upper Atmosphere Modeling, and Data Assimilation
- Citation to related publication:
- Ponder, B. M., Ridley, A. J., Goel, A., & Bernstein, D. S. (2023). Improving forecasting ability of GITM using data-driven model refinement. Space Weather, 21, e2022SW003290. https://doi.org/10.1029/2022SW003290
- Discipline:
- Engineering and Science
-
Supporting data: Domain-agnostic predictions of nanoscale interactions in proteins and nanoparticles
- Creator:
- Saldinger, Jacob, Raymond, Matt , Elvati, Paolo, and Violi, Angela
- Description:
- The accurate and rapid prediction of generic nanoscale interactions is a challenging problem with broad applications. Much of biology functions at the nanoscale, and our ability to manipulate materials and purposefully engage biological machinery requires knowledge of nano-bio interfaces. While several protein-protein interaction models are available, they leverage protein-specific information, limiting their abstraction to other structures. Here, we present NeCLAS, a general, and rapid machine learning pipeline that predicts the location of nanoscale interactions, providing human-intelligible predictions. Two key aspects distinguish NeCLAS: coarse-grained representations, and the use of environmental features to encode the chemical neighborhood. We showcase NeCLAS with challenges for protein-protein, protein-nanoparticle and nanoparticle-nanoparticle systems, demonstrating that NeCLAS replicates computationally- and experimentally-observed interactions. NeCLAS outperforms current nanoscale prediction models, and it shows cross-domain validity, qualifying as a tool for basic research, rapid prototyping, and design of nanostructures., Software: - To reproduce all-atom molecular dynamics (MD) NAMD is required (version 2.14 or later is suggested). NAMD software and documentation can be found at https://www.ks.uiuc.edu/Research/namd/, - To reproduce coarse-grained MD simulations, LAMMPS (version 29 Sep 2021 - Update 2 or later is suggested). LAMMPS software and documentation can be found at https://www.lammps.org, - To rebuild free energy profiles, the PLUMED plugin (version 2.6) was used. PLUMED software and documentation can be found at https://www.plumed.org/ , and - To generate force matching potentials, the was used the OpenMSCG software was used. OpenMSCG software and documentation can be found at https://software.rcc.uchicago.edu/mscg/
- Keyword:
- Neural Networks, Proteins, Dimensionality Reduction, Nanoparticles, and Coarse-Graining
- Citation to related publication:
- https://www.biorxiv.org/content/10.1101/2022.08.09.503361v2
- Discipline:
- Science
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