Evidence map›Paper›PMID 38931715›Full record

ArticleSensors (Basel, Switzerland)2024

Lunar Lithium-7 Sensing (

Julia Fernandez, Susana Fernandez, Enrique Diez, Noemi Pinilla-Alonso, Saúl Pérez, Santiago Iglesias, Alejandro Buendía, Javier Rodríguez, Javier de Cos

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Julia FernandezInstituto de Ciencias y Tecnologías Espaciales de Asturias, Universidad de Oviedo, 33004 Oviedo, Spain.ORCID 0009-0007-2419-2405
Susana FernandezInstituto de Ciencias y Tecnologías Espaciales de Asturias, Universidad de Oviedo, 33004 Oviedo, Spain.ORCID 0000-0001-8267-0371
Enrique DiezInstituto de Ciencias y Tecnologías Espaciales de Asturias, Universidad de Oviedo, 33004 Oviedo, Spain.ORCID 0000-0002-5826-9892
Noemi Pinilla-AlonsoFlorida Space Institute, University of Central Florida, Orlando, FL 32816, USA.ORCID 0000-0002-2770-7896
Saúl PérezInstituto de Ciencias y Tecnologías Espaciales de Asturias, Universidad de Oviedo, 33004 Oviedo, Spain.ORCID 0000-0003-4161-778X
Santiago IglesiasInstituto de Ciencias y Tecnologías Espaciales de Asturias, Universidad de Oviedo, 33004 Oviedo, Spain.ORCID 0000-0001-5903-9899
Alejandro BuendíaInstituto de Ciencias y Tecnologías Espaciales de Asturias, Universidad de Oviedo, 33004 Oviedo, Spain.ORCID 0009-0000-9900-4463
Javier RodríguezInstituto de Ciencias y Tecnologías Espaciales de Asturias, Universidad de Oviedo, 33004 Oviedo, Spain.ORCID 0000-0002-2718-2022
Javier de CosInstituto de Ciencias y Tecnologías Espaciales de Asturias, Universidad de Oviedo, 33004 Oviedo, Spain.ORCID 0000-0002-9660-7944

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

artificial neural networksbootstrappingLithium-7MARSregression modelspectral patterns

Identifiers

PMID38931715
PMCPMC11207396

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.