Lithium, a critical natural resource integral to modern technology, has influenced diverse industries since its discovery in the 1950s. Of particular interest is lithium-7, the most prevalent lithium isotope on Earth, playing a vital role in applications such as batteries, metal alloys, medicine, and nuclear research. However, its extraction presents significant environmental and logistical challenges. This article explores the potential for lithium exploration on the Moon, driven by its value as a resource and the prospect of cost reduction due to the Moon's lower gravity, which holds promise for future space exploration endeavors. Additionally, the presence of lithium in the solar wind and its implications for material transport across celestial bodies are subjects of intrigue. Drawing from a limited dataset collected during the Apollo missions (Apollo 12, 15, 16, and 17) and leveraging artificial intelligence techniques and sample expansion through bootstrapping, this study develops predictive models for lithium-7 concentration based on spectral patterns. The study areas encompass the Aitken crater, Hadley Rima, and the Taurus-Littrow Valley, where higher lithium concentrations are observed in basaltic lunar regions. This research bridges lunar geology and the formation of the solar system, providing valuable insights into celestial resources and enhancing our understanding of space. The data used in this study were obtained from the imaging sensors (infrared, visible, and ultraviolet) of the Clementine satellite, which significantly contributed to the success of our research. Furthermore, the study addresses various aspects related to statistical analysis, sample quality validation, resampling, and bootstrapping. Supervised machine learning model training and validation, as well as data import and export, were explored. The analysis of data generated by the Clementine probe in the near-infrared (NIR) and ultraviolet-visible (UVVIS) spectra revealed evidence of the presence of lithium-7 (Li-7) on the lunar surface. The distribution of Li-7 on the lunar surface is non-uniform, with varying concentrations in different regions of the Moon identified, supporting the initial hypothesis associating surface Li-7 concentration with exposure to solar wind. While a direct numerical relationship between lunar topography and Li-7 concentration has not been established due to morphological diversity and methodological limitations, preliminary results suggest significant economic and technological potential in lunar lithium exploration and extraction.
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http://dx.doi.org/10.3390/s24123931 | DOI Listing |
Sci Total Environ
January 2025
Marine Toxicology, Institute of Marine Research, Bergen, Norway.
Polycyclic aromatic hydrocarbons (PAHs) are toxic contaminants with a widespread presence in diverse environmental contexts. Transformation processes of PAHs via degradation and biotransformation have parallels in humans, animals, plants, fungi, and bacteria. Mapping the transformation products of PAHs is therefore crucial for assessing their toxicological impact and developing effective monitoring strategies.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Sorbonne Université, Paris Brain Institute (ICM), INSERM, CNRS, UMR-1127, Mov'It, DreamTeam, Paris, France.
Background: Spectral power of slow rhythms in resting-state EEG increases along Alzheimer's disease (AD) continuum. Besides, recent studies have revealed 1) the importance of analyzing the aperiodic component of an EEG power spectrum and 2) the intrusions of sleep-like slow waves identifiable in wake EEG of animals and young adults. Importantly, the occurrence of these wake slow waves is known i) to increase after sleep deprivation, ii) to be associated with markers of sleepiness, and iii) to predict behavioral errors at different tasks.
View Article and Find Full Text PDFBackground: The increasing prevalence of cognitive impairment and dementia threatens global health, necessitating the development of accessible tools for detection of cognitive impairment. This study explores using a transformer-based approach to detect cognitive impairment using acoustic markers of spontaneous speech.
Method: Recordings of unstructured interviews from baseline visits were obtained from participants of The 90+ Study, a longitudinal study of individuals older than 90 years.
Background: It is now widely acknowledged that diet, lifestyle, and environmental exposures largely affect an individual's metabolic state in health and disease, including the brain. Metabolomics has demonstrated its potential to enable exciting discoveries in brain health, facilitated by advances in analytical and informatics techniques. Here, we highlighted the use of MS/MS-based untargeted metabolomics to study the diet and medication exposure of cognitively declined cohorts through the newly developed FoodMASST and DrugMASST tools.
View Article and Find Full Text PDFJ Clin Neurophysiol
January 2025
Department of Intensive Care, Neuro-Intensive Care Unit, University Hospital of Geneva, Geneva, Switzerland.
Purpose: Recent research on quantitative EEG in coma has proposed several metrics correlating with consciousness level. However, the heterogeneous nature of coma can challenge the generalizability of these measures. This study investigates alpha-coma, an electroclinical pattern characterized by a widespread, nonreactive alpha rhythm often linked to poor outcomes.
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